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  Subjects -> ELECTRONICS (Total: 138 journals)
Advances in Biosensors and Bioelectronics     Open Access   (Followers: 2)
Advances in Magnetic and Optical Resonance     Full-text available via subscription   (Followers: 5)
Advances in Microelectronic Engineering     Open Access   (Followers: 1)
Advances in Power Electronics     Open Access   (Followers: 7)
Aerospace and Electronic Systems, IEEE Transactions on     Hybrid Journal   (Followers: 53)
American Journal of Electrical and Electronic Engineering     Open Access   (Followers: 7)
Annals of Telecommunications     Hybrid Journal   (Followers: 4)
APL : Organic Electronics and Photonics     Hybrid Journal   (Followers: 1)
APSIPA Transactions on Signal and Information Processing     Open Access   (Followers: 2)
Archives of Electrical Engineering     Open Access   (Followers: 9)
Autonomous Mental Development, IEEE Transactions on     Hybrid Journal   (Followers: 5)
Bell Labs Technical Journal     Hybrid Journal   (Followers: 8)
Biomedical Engineering, IEEE Reviews in     Full-text available via subscription   (Followers: 14)
Biomedical Engineering, IEEE Transactions on     Hybrid Journal   (Followers: 11)
Biomedical Instrumentation & Technology     Full-text available via subscription   (Followers: 4)
Broadcasting, IEEE Transactions on     Hybrid Journal   (Followers: 5)
BULLETIN of National Technical University of Ukraine. Series RADIOTECHNIQUE. RADIOAPPARATUS BUILDING     Open Access   (Followers: 1)
Bulletin of the Polish Academy of Sciences : Technical Sciences     Open Access  
Canadian Journal of Remote Sensing     Full-text available via subscription   (Followers: 12)
China Communications     Full-text available via subscription   (Followers: 1)
Circuits and Systems     Open Access   (Followers: 7)
Consumer Electronics Times     Open Access   (Followers: 3)
Control Systems     Hybrid Journal   (Followers: 18)
Electronic Markets     Hybrid Journal   (Followers: 5)
Electronic Materials Letters     Hybrid Journal   (Followers: 2)
Electronics     Open Access   (Followers: 3)
Electronics and Communications in Japan     Hybrid Journal   (Followers: 4)
Electronics Letters     Hybrid Journal   (Followers: 15)
Embedded Systems Letters, IEEE     Hybrid Journal   (Followers: 14)
EPJ Quantum Technology     Open Access  
EURASIP Journal on Embedded Systems     Open Access   (Followers: 8)
Foundations and TrendsĀ® in Communications and Information Theory     Full-text available via subscription   (Followers: 5)
Foundations and TrendsĀ® in Signal Processing     Full-text available via subscription   (Followers: 4)
Frequenz     Full-text available via subscription   (Followers: 1)
Frontiers of Optoelectronics     Hybrid Journal   (Followers: 1)
Geoscience and Remote Sensing, IEEE Transactions on     Hybrid Journal   (Followers: 19)
Haptics, IEEE Transactions on     Hybrid Journal   (Followers: 4)
IEEE Consumer Electronics Magazine     Full-text available via subscription   (Followers: 7)
IEEE Journal of Emerging and Selected Topics in Power Electronics     Hybrid Journal   (Followers: 3)
IEEE Journal of the Electron Devices Society     Open Access   (Followers: 1)
IEEE Power Electronics Magazine     Full-text available via subscription  
IEEE Transactions on Audio, Speech, and Language Processing     Hybrid Journal   (Followers: 9)
IEEE Transactions on Automatic Control     Hybrid Journal   (Followers: 19)
IEEE Transactions on Consumer Electronics     Hybrid Journal   (Followers: 10)
IEEE Transactions on Electron Devices     Hybrid Journal   (Followers: 6)
IEEE Transactions on Information Theory     Hybrid Journal   (Followers: 13)
IEEE Transactions on Power Electronics     Hybrid Journal   (Followers: 11)
IEICE - Transactions on Electronics     Full-text available via subscription   (Followers: 7)
IEICE - Transactions on Information and Systems     Full-text available via subscription   (Followers: 7)
IET Power Electronics     Hybrid Journal   (Followers: 7)
IET Wireless Sensor Systems     Hybrid Journal   (Followers: 5)
IETE Journal of Education     Open Access   (Followers: 2)
IETE Journal of Research     Open Access   (Followers: 5)
IETE Technical Review     Open Access   (Followers: 1)
Industrial Electronics, IEEE Transactions on     Hybrid Journal   (Followers: 9)
Industry Applications, IEEE Transactions on     Hybrid Journal   (Followers: 5)
Informatik-Spektrum     Hybrid Journal  
Instabilities in Silicon Devices     Full-text available via subscription  
Intelligent Transportation Systems Magazine, IEEE     Full-text available via subscription   (Followers: 1)
International Journal of Advanced Electronics and Communication Systems     Open Access   (Followers: 4)
International Journal of Advanced Research in Computer Science and Electronics Engineering     Open Access   (Followers: 18)
International Journal of Advances in Telecommunications, Electrotechnics, Signals and Systems     Open Access   (Followers: 1)
International Journal of Aerospace Innovations     Full-text available via subscription   (Followers: 10)
International Journal of Applied Electronics in Physics & Robotics     Open Access  
International Journal of Biomedical Nanoscience and Nanotechnology     Hybrid Journal   (Followers: 5)
International Journal of Computational Vision and Robotics     Hybrid Journal   (Followers: 4)
International Journal of Computer & Electronics Research     Full-text available via subscription   (Followers: 2)
International Journal of Control     Hybrid Journal   (Followers: 10)
International Journal of Electronics     Hybrid Journal   (Followers: 2)
International Journal of Electronics & Data Communication     Open Access   (Followers: 3)
International Journal of Electronics and Telecommunications     Open Access   (Followers: 3)
International Journal of Granular Computing, Rough Sets and Intelligent Systems     Hybrid Journal   (Followers: 1)
International Journal of High Speed Electronics and Systems     Hybrid Journal  
International Journal of Microwave and Wireless Technologies     Hybrid Journal   (Followers: 1)
International Journal of Nano Devices, Sensors and Systems     Open Access   (Followers: 1)
International Journal of Nanoscience     Hybrid Journal   (Followers: 1)
International Journal of Numerical Modelling:Electronic Networks, Devices and Fields     Hybrid Journal   (Followers: 2)
International Journal of Power Electronics     Hybrid Journal   (Followers: 3)
International Journal of Review in Electronics & Communication Engineering     Open Access   (Followers: 1)
International Journal of Sensors, Wireless Communications and Control     Hybrid Journal   (Followers: 2)
International Journal of Systems, Control and Communications     Hybrid Journal   (Followers: 2)
International Journal on Communication     Full-text available via subscription   (Followers: 7)
International Journal on Electrical and Power Engineering     Full-text available via subscription   (Followers: 10)
ISRN Electronics     Open Access   (Followers: 1)
ISRN Signal Processing     Open Access  
Journal of Advanced Dielectrics     Open Access   (Followers: 1)
Journal of Artificial Intelligence     Open Access   (Followers: 5)
Journal of Circuits, Systems, and Computers     Hybrid Journal   (Followers: 1)
Journal of Electrical and Electronics Engineering Research     Open Access   (Followers: 2)
Journal of Electrical Bioimpedance     Full-text available via subscription   (Followers: 2)
Journal of Electrical Engineering & Electronic Technology     Full-text available via subscription   (Followers: 1)
Journal of Electromagnetic Analysis and Applications     Open Access   (Followers: 3)
Journal of Electromagnetic Waves and Applications     Hybrid Journal   (Followers: 2)
Journal of Electronic Design Technology     Full-text available via subscription   (Followers: 1)
Journal of Electronics (China)     Hybrid Journal   (Followers: 2)
Journal of Field Robotics     Hybrid Journal   (Followers: 2)
Journal of Guidance, Control, and Dynamics     Full-text available via subscription   (Followers: 50)
Journal of Intelligent Procedures in Electrical Technology     Open Access  
Journal of Low Power Electronics     Full-text available via subscription   (Followers: 4)
Journal of Low Power Electronics and Applications     Open Access   (Followers: 1)

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Selected Topics in Applied Earth Observations and Remote Sensing, IEEE Journal of
   [17 followers]  Follow    
   Hybrid Journal Hybrid journal (It can contain Open Access articles)
     ISSN (Print) 1939-1404
     Published by Institute of Electrical and Electronics Engineers (IEEE) Homepage  [172 journals]   [SJR: 1.232]   [H-I: 14]
  • IEEE Journal of Selected Topics in Applied Earth Observations and Remote
           Sensing publication information
    • Pages: C2 - C2
      Abstract: Provides a listing of current staff, committee members and society officers.
      PubDate: June 2014
      Issue No: Vol. 7, No. 6 (2014)
  • [Front cover]
    • Pages: C1 - C1
      Abstract: Presents the front cover for this issue of the publication.
      PubDate: June 2014
      Issue No: Vol. 7, No. 6 (2014)
  • IEEE Journal of Selected Topics in Applied Earth Observations and Remote
           Sensing Information for Authors
    • Pages: C3 - C3
      Abstract: Provides instructions and guidelines to prospective authors who wish to submit manuscripts.
      PubDate: June 2014
      Issue No: Vol. 7, No. 6 (2014)
  • IEEE Transactions on Geoscience and Remote Sensing institutional listings
    • Pages: C4 - C4
      Abstract: Advertisement.
      PubDate: June 2014
      Issue No: Vol. 7, No. 6 (2014)
  • Table of contents
    • Pages: 1837 - 1840
      Abstract: Presents the table of contents for this issue of the periodical.
      PubDate: June 2014
      Issue No: Vol. 7, No. 6 (2014)
  • Foreword to the Special Issue on Hyperspectral Image and Signal Processing
    • Authors: Zare; A.;Bolton, J.;Chanussot, J.;Gader, P.;
      Pages: 1841 - 1843
      Abstract: The seventy-four articles in this special issue present state-of-the-art algorithms and applications for hyperspectral image and signal processing. Algorithms address topics such as spectral unmixing, classification, target and anomaly detection, compression, data fusion, noise reduction. Applications include monitoring of vegetation and environment. The large number of papers included in this special issue is indicative of the high level of research activity, interest, and applications for hyperspectral image and signal analysis. The 5th Workshop on Hyperspectral Image and Signal Processing??Evolution in Remote Sensing (WHISPERS) was held on June 25??28, 2013 in Gainesville, FL, USA. WHISPERS 2013 received the technical sponsorship of the IEEE Geoscience and Remote Sensing Society (GRSS) and support from the University of Florida and the WHISPERS Foundation.
      PubDate: June 2014
      Issue No: Vol. 7, No. 6 (2014)
  • A Review of Nonlinear Hyperspectral Unmixing Methods
    • Authors: Heylen; R.;Parente, M.;Gader, P.;
      Pages: 1844 - 1868
      Abstract: In hyperspectral unmixing, the prevalent model used is the linear mixing model, and a large variety of techniques based on this model has been proposed to obtain endmembers and their abundances in hyperspectral imagery. However, it has been known for some time that nonlinear spectral mixing effects can be a crucial component in many real-world scenarios, such as planetary remote sensing, intimate mineral mixtures, vegetation canopies, or urban scenes. While several nonlinear mixing models have been proposed decades ago, only recently there has been a proliferation of nonlinear unmixing models and techniques in the signal processing literature. This paper aims to give an historical overview of the majority of nonlinear mixing models and nonlinear unmixing methods, and to explain some of the more popular techniques in detail. The main models and techniques treated are bilinear models, models for intimate mineral mixtures, radiosity-based approaches, ray tracing, neural networks, kernel methods, support vector machine techniques, manifold learning methods, piece-wise linear techniques, and detection methods for nonlinearity. Furthermore, we provide an overview of several recent developments in the nonlinear unmixing literature that do not belong into any of these categories.
      PubDate: June 2014
      Issue No: Vol. 7, No. 6 (2014)
  • A Comparison of Nonlinear Mixing Models for Vegetated Areas Using
           Simulated and Real Hyperspectral Data
    • Authors: Dobigeon; N.;Tits, L.;Somers, B.;Altmann, Y.;Coppin, P.;
      Pages: 1869 - 1878
      Abstract: Spectral unmixing (SU) is a crucial processing step when analyzing hyperspectral data. In such analysis, most of the work in the literature relies on the widely acknowledged linear mixing model to describe the observed pixels. Unfortunately, this model has been shown to be of limited interest for specific scenes, in particular when acquired over vegetated areas. Consequently, in the past few years, several nonlinear mixing models have been introduced to take nonlinear effects into account while performing SU. These models have been proposed empirically, however, without any thorough validation. In this paper, the authors take advantage of two sets of real and physical-based simulated data to validate the accuracy of various nonlinear models in vegetated areas. These physics-based models, and their corresponding unmixing algorithms, are evaluated with respect to their ability of fitting the measured spectra and providing an accurate estimation of the abundance coefficients, considered as the spatial distribution of the materials in each pixel.
      PubDate: June 2014
      Issue No: Vol. 7, No. 6 (2014)
  • A Distance Geometric Framework for Nonlinear Hyperspectral Unmixing
    • Authors: Heylen; R.;Scheunders, P.;
      Pages: 1879 - 1888
      Abstract: In this article, a distance geometry-based framework for hyperspectral image unmixing is presented. A manifold representation of the data set is generated by creation of a nearest-neighbor graph on which shortest paths are calculated yielding a geodesic distance matrix. Instead of unfolding the manifold in a lower-dimensional Euclidean space, it is proposed to work directly on the manifold. To do so, algorithms need to be rewritten in terms of distance geometry. Building further on earlier work, where distance-based dimensionality estimation and endmember extraction methods were presented, we will propose a distance geometric version of the actual unmixing (abundance estimation) step. In this way, a complete distance geometric unmixing framework is obtained that is efficient compared to the classical methods based on optimization. Furthermore, the distance geometry-adapted algorithms can be applied on nonlinear data manifolds by employing geodesic distances. In the experiments, we demonstrate this by comparing the obtained nonlinear framework to its linear counterpart.
      PubDate: June 2014
      Issue No: Vol. 7, No. 6 (2014)
  • Non-Local Sparse Unmixing for Hyperspectral Remote Sensing Imagery
    • Authors: Zhong; Y.;Feng, R.;Zhang, L.;
      Pages: 1889 - 1909
      Abstract: Sparse unmixing is a promising approach that acts as a semi-supervised unmixing strategy by assuming that the observed image signatures can be expressed in the form of linear combinations of a number of pure spectral signatures that are known in advance. However, conventional sparse unmixing involves finding the optimal subset of signatures for the observed data in a very large standard spectral library, without considering the spatial information. In this paper, a new sparse unmixing algorithm based on non-local means, namely non-local sparse unmixing (NLSU), is proposed to perform the unmixing task for hyperspectral remote sensing imagery. In NLSU, the non-local means method, as a regularizer for sparse unmixing, is used to exploit the similar patterns and structures in the abundance image. The NLSU algorithm based on the sparse spectral unmixing model can improve the spectral unmixing accuracy by incorporating the non-local spatial information by means of a weighting average for all the pixels in the abundance image. Five experiments with three simulated and two real hyperspectral images were performed to evaluate the performance of the proposed algorithm in comparison to the previous sparse unmixing methods: sparse unmixing via variable splitting and augmented Lagrangian (SUnSAL) and sparse unmixing via variable splitting augmented Lagrangian and total variation (SUnSAL-TV). The experimental results demonstrate that NLSU outperforms the other algorithms, with a better spectral unmixing accuracy, and is an effective spectral unmixing algorithm for hyperspectral remote sensing imagery.
      PubDate: June 2014
      Issue No: Vol. 7, No. 6 (2014)
  • Earth Movers Distance-Based Simultaneous Comparison of Hyperspectral
           Endmembers and Proportions
    • Authors: Zare; A.;Anderson, D.T.;
      Pages: 1910 - 1921
      Abstract: A new approach for simultaneously comparing sets of hyperspectral endmembers and proportion values using the Earth Movers Distance (EMD) is presented. First, the EMD is defined and calculated per-pixel based on the proportion values and corresponding endmembers. Next, these per-pixel EMD distances are aggregated to obtain a final measure of dissimilarity. In particular, the proposed EMD approach can be used to simultaneously compare endmembers and proportion values with differing numbers of endmembers. The proposed method has a number of uses, including: computing the similarity between two sets of endmembers and proportion values that were obtained using any algorithm or underlying mixing model, clustering sets of hyperspectral endmember and proportion values, or evaluating spectral unmixing results by comparing estimated values to ground truth information. Experiments on both simulated and measured hyperspectral data sets demonstrate that the EMD is effective at simultaneous endmember and proportion comparison.
      PubDate: June 2014
      Issue No: Vol. 7, No. 6 (2014)
  • Unsupervised Spectral Mixture Analysis of Highly Mixed Data With Hopfield
           Neural Network
    • Authors: Mei; S.;He, M.;Wang, Z.;Feng, D.D.;
      Pages: 1922 - 1935
      Abstract: Hopfield Neural Network (HNN) has been demonstrated to be an effective tool for Spectral Mixture Analysis (SMA). However, the spectrum of pure ground objects, known as endmember, must be known previously. In this paper, the HNN is utilized to solve unsupervised SMA, in which Endmember Extraction (EE) and Abundance Estimation (AE) are performed iteratively. Two different HNNs are constructed to solve such multiplicative updating procedure, respectively. The proposed HNN based unsupervised SMA framework is then applied to solve three second-order constrained Nonnegative Matrix Factorization (NMF) models for SMA, including Minimum Distance Constrained NMF (MDC-NMF), Minimum endmember-wise Distance Constrained NMF (MewDC-NMF), and Minimum Dispersion Constrained NMF (MiniDisCo-NMF). As a result, our proposed HNN based algorithms are able to perform unsupervised SMA and extract virtual endmembers without assuming the presence of spectrally pure constituents in highly mixed hyperspectral data. Experimental results on both synthetic and real hyperspectral images demonstrate that our proposed HNN based algorithms clearly outperform traditional Projected Gradient (PG) based solutions for these constrained NMF based SMA.
      PubDate: June 2014
      Issue No: Vol. 7, No. 6 (2014)
  • A Data-Driven Stochastic Approach for Unmixing Hyperspectral Imagery
    • Authors: Bhatt; J.S.;Joshi, M.V.;Raval, M.S.;
      Pages: 1936 - 1946
      Abstract: In this paper, we propose a two-step Bayesian approach to handle the ill-posed nature of the unmixing problem for accurately estimating the abundances. The abundances are dependent on the scene contents and they represent mixing proportions of the endmembers over an area. In this work, a linear mixing model (LMM) is used for the image formation process in order to derive the data term. In the first step, a Huber–Markov random field (HMRF)-based prior distribution is assumed to model the dependencies within the abundances across the spectral space of the data. The threshold used in the HMRF prior is derived from an initial estimate of abundances obtained using the matched filters. This makes the HMRF prior data-driven, i.e., dHMRF. Final abundance maps are obtained in the second step within a maximum a posteriori probability (MAP) framework, and the objective function is optimized using the particle swarm optimization (PSO). Theoretical analysis is carried out to show the effectiveness of the proposed method. The approach is evaluated using the synthetic and real AVIRIS Cuprite data. The proposed method has the following advantages. 1) The estimated abundances are resistant to noise since they are based on an initial estimate that has high signal-to-noise ratio (SNR). 2) The variance in the abundance maps is well preserved since the threshold in the dHMRF is derived from the data.
      PubDate: June 2014
      Issue No: Vol. 7, No. 6 (2014)
  • Fuzzy Assessment of Spectral Unmixing Algorithms
    • Authors: Jia; X.;Wang, L.;
      Pages: 1947 - 1955
      Abstract: While a single spectrum is often used to present a pure class, it is more realistic to consider intra-class spectral variation and model a pure class using a group of its representative spectra. In line with this consideration, crisp unmixing accuracy assessment, where unmixing performance is assessed using a mean squared error of the estimated endmember fractions, can be misleading. In this paper, alterative spectral unmixing assessment methods are introduced to account for the uncertainty contained in the spectral measurements and during the ground truth data collection. Two fuzzy measures are developed to assess unmixing performance. One is fuzzy unmixing fraction error for a realistic assessment and the other is pixel level unmixing accuracy to provide a good quantitative understanding of the unmixing success rates spatially. To demonstrate and illustrate how they work, the two fuzzy measures are applied to evaluate the performance of several spectral unmixing methods including both single spectrum based and multiple spectra based algorithms. Crisp assessments and fuzzy results at various tolerance levels are presented and compared. Based on the realistic measures proposed, it is found the recent developed unmixing method with extended Support Vector Machines outperforms other algorithms tested.
      PubDate: June 2014
      Issue No: Vol. 7, No. 6 (2014)
  • Quantifying Nonlinear Spectral Mixing in Vegetated Areas: Computer
           Simulation Model Validation and First Results
    • Authors: Somers; B.;Tits, L.;Coppin, P.;
      Pages: 1956 - 1965
      Abstract: Our understanding of nonlinear mixing events in vegetated areas is currently hampered by a pertinent lack of well-validated datasets. Most quantification and modeling efforts are, therefore, based on the theoretical assumptions or indirect empirical observations. Here, we performed a quantitative and qualitative evaluation of the accuracy of nonlinear mixing effects as modeled by a fully calibrated virtual orchard model (based on physically based ray-tracer software). For validation, we had available data from an in situ experiment. This experiment comprised in situ measured mixed pixel reflectance spectra, pixel-specific endmember spectra, and subpixel cover fraction distributions, all collected in the same orchard for which the virtual model was calibrated. We took advantage of this unique-coupled dataset to demonstrate that both the nature and the intensity of the nonlinear mixing events observed in the in situ data are realistically modeled by the ray-tracing software. This is an important observation because this implies that our virtual model now provides a solid tool for the detailed study of nonlinear mixing in vegetated areas which could facilitate as such the design, calibration, and validation of different nonlinear mixing modeling approaches. Initial results revealed that the nonlinear mixing is dependent on fractional distribution, soil moisture conditions, and endmember definitions. We could further demonstrate that the bilinear spectral mixture model nicely described nonlinear mixing events but at the same time overestimated reflectances in spectral regions with moderate-to-low nonlinear mixing behavior.
      PubDate: June 2014
      Issue No: Vol. 7, No. 6 (2014)
  • Subspace-Projection-Based Geometric Unmixing for Material Quantification
           in Hyperspectral Imagery
    • Authors: Yang; H.;An, J.;Zhu, C.;
      Pages: 1966 - 1975
      Abstract: Linear spectral unmixing is a widely used technique in hyperspectral remote sensing to quantify materials present in an image pixel. In order to produce accurate estimates of abundances, nonnegativity constraint and sum-to-one constraint must be imposed on the abundances of materials. Under these two constraints, linear spectral unmixing is often formulated as a convex optimization problem that requires more advanced optimization technology, leading to excessive computational complexity. In this paper, a novel geometric method is presented for solving the fully constrained linear spectral unmixing problem. Specifically, abundances are first expressed as the ratios of signed volumes of simplexes. Then, Laplace expansion is applied in the process of determinant calculation, which derives a new low-complexity abundance estimation method. Furthermore, the mixed pixel outside the simplex is iteratively projected onto the facet planes through the endmember vertices for making the abundances satisfy the nonnegativity constraint. This process is continued until one finds a projected point lying inside the simplex. The proposed method is in line with the least squares criterion. Experimental results based on simulated and the AVIRIS Cuprite data sets demonstrate the superiority of the proposed algorithm with respect to other state-of-the-art approaches.
      PubDate: June 2014
      Issue No: Vol. 7, No. 6 (2014)
  • Spatially Constrained Multiple Endmember Spectral Mixture Analysis for
           Quantifying Subpixel Urban Impervious Surfaces
    • Authors: Wu; C.;Deng, C.;Jia, X.;
      Pages: 1976 - 1984
      Abstract: Multiple endmember spectral mixture analysis (MESMA) has been extensively employed to accommodate endmember variability associated with the mixed pixel problem in remote sensing imagery. However, endmember extraction is a critical step in the application of MESMA. Considering that spatial information can be helpful for selecting local representative endmembers, this paper develops a spatially constrained MESMA method, with which multiple endmembers for each class are automatically derived within a predefined neighborhood. Two specific novelties are: 1) to identify all the endmembers over the whole image scene for each class through a classification tree approach; and 2) to generate spatially constrained endmembers for the neighborhood of each target pixel of the image through a k-means clustering method. MESMA is then performed using the derived spatially constrained endmembers. This proposed method was applied to a Landsat Enhanced Thematic Mapper ( ${bf ETM}+$ ) image for examining subpixel urban impervious surfaces, and its performance was compared with that of a global MESMA method. The results suggest that spatially constrained MESMA is able to yield adequate estimates, supported by a relatively decent precision and low bias (10.68% for mean absolute error and ${bf- 3.58}$ % for systematic error).
      PubDate: June 2014
      Issue No: Vol. 7, No. 6 (2014)
  • Integrating Spatial Information in Unsupervised Unmixing of Hyperspectral
           Imagery Using Multiscale Representation
    • Authors: Torres-Madronero; M.C.;Velez-Reyes, M.;
      Pages: 1985 - 1993
      Abstract: This paper presents an unsupervised unmixing approach that takes advantage of multiscale representation based on nonlinear diffusion to integrate the spatial information in the spectral endmembers extraction from a hyperspectral image. The main advantages of unsupervised unmixing based on multiscale representation (UUMR) are the avoidance of matrix rank estimation to determine the number of endmembers and the use of spatial information without employing spatial kernels. Multiscale representation builds a family of smoothed images where locally spectrally uniform regions can be identified. The multiscale representation is extracted solving a nonlinear diffusion partial differential equation (PDE). Locally, homogeneous regions are identified by taking advantage of an algebraic multigrid method used to solve the PDE. Representative spectra for each region are extracted and then clustered to build spectral endmember classes. These classes represent the different spectral components of the image as well as their spectral variability. The number of spectral endmember classes is estimated using the Davies and Bouldin validity index. A quantitative assessment of unmixing approach based on multiscale representation is presented using an AVIRIS image captured over Fort. A.P. Hill, Virginia. A comparison of UUMR results with others unmixing techniques is included.
      PubDate: June 2014
      Issue No: Vol. 7, No. 6 (2014)
  • Spatial and Spectral Unmixing Using the Beta Compositional Model
    • Authors: Du; X.;Zare, A.;Gader, P.;Dranishnikov, D.;
      Pages: 1994 - 2003
      Abstract: This paper introduces the beta compositional model (BCM) for hyperspectral unmixing and four algorithms for unmixing given the BCM. Hyperspectral unmixing estimates the proportion of each endmember at every pixel of a hyperspectral image. Under the BCM, each endmember is a random variable distributed according to a beta distribution. By using a beta distribution, spectral variability is accounted for during unmixing, the reflectance values of each endmember are constrained to a physically realistic range, and skew can be accounted for in the distribution. Spectral variability is incorporated to increase hyperspectral unmixing accuracy. Two BCM-based spectral unmixing approaches are presented: BCM-spectral and BCM-spatial. For each approach, two algorithms, one based on quadratic programming (QP) and one using a Metropolis–Hastings (MH) sampler, are developed. Results indicate that the proposed BCM unmixing algorithms are able to successfully perform unmixing on simulated data and real hyperspectral imagery while incorporating endmember spectral variability and spatial information.
      PubDate: June 2014
      Issue No: Vol. 7, No. 6 (2014)
  • Spatial-Spectral Information Based Abundance-Constrained Endmember
           Extraction Methods
    • Authors: Xu; M.;Du, B.;Zhang, L.;
      Pages: 2004 - 2015
      Abstract: Endmember extraction, which is an important technique for hyperspectral data interpretation, selects a collection of pure signature spectra of the different materials, called endmembers, which are present in a remotely sensed hyperspectral image scene. These pure signatures are then used in spectral unmixing algorithms to decompose the scene into abundance fractions, which indicate the proportion of each endmember's presence in a mixed pixel. In other words, abundances can be obtained by the given endmembers. Correspondingly, endmembers can be extracted based on an abundance constraint. In this paper, we first propose an endmember extraction framework based on an abundance constraint whose efficiency is related to the abundance calculation. The mainstream existing spatial-spectral algorithms can have a very high complexity and are sensitive to outliers, or the spatial information is considered followed by the spectral information. We therefore propose a strategy to consider the spectral information followed by the spatial information, using an abundance-constrained framework. The spatial strategy is also assumed to be immune to outliers. Experiments on both synthetic and real hyperspectral data sets indicate that: 1) the abundance constraint is effective for endmember extraction; and 2) the proposed spatial processing method used in the abundance-constrained endmember extraction framework can effectively avoid outliers.
      PubDate: June 2014
      Issue No: Vol. 7, No. 6 (2014)
  • A Dynamic Unmixing Framework for Plant Production System Monitoring
    • Authors: Iordache; M.;Tits, L.;Bioucas-Dias, J.M.;Plaza, A.;Somers, B.;
      Pages: 2016 - 2034
      Abstract: Hyperspectral remote sensing or imaging spectroscopy is an emerging technology in plant production monitoring and management. The continuous reflectance spectra allow for the intensive monitoring of biophysical and biochemical tree characteristics during growth, through for instance the use of vegetation indices. Yet, since most of the pixels in hyperspectral images are mixed, the evaluation of the actual vegetation state on the ground directly from the measured spectra is degraded by the presence of other endmembers, such as soil. Spectral unmixing, then, becomes a necessary processing step to improve the interpretation of vegetation indices. In this sense, an active research direction is based on the use of large collections of pure spectra, called spectral libraries or dictionaries, which model a wide variety of possible states of the endmembers of interest on the ground, i.e., vegetation and soil. Under the linear mixing model (LMM), the observed spectra are assumed to be linear combinations of spectra from the available dictionary. Combinatorial techniques (e.g., MESMA) and sparse regression algorithms (e.g., SUnSAL) are widely used to tackle the unmixing problem in this case. However, both combinatorial and sparse techniques benefit from appropriate library reduction strategies. In this paper, we develop a new efficient method for library reduction (or dictionary pruning), which exploits the fact that hyperspectral data generally lives in a lower-dimensional subspace. Specifically, we present a slight modification of the MUSIC-CSR algorithm, a two-step method which aims first at pruning the dictionary and second at infering high-quality reconstruction of the vegetation spectra on the ground (this application being called signal unmixing in remote sensing), using the pruned dictionary as input to available unmixing methods. Our goal is two-fold: 1) to obtain high-accuracy unmixing output using sparse unmixing, with low-execution time; - nd 2) to improve MESMA performances in terms of accuracy. Our experiments, which have been conducted in a multi-temporal case study, show that the method achieves these two goals and proposes sparse unmixing as a reliable and robust alternative to the combinatorial methods in plant production monitoring applications. We further demonstrate that the proposed methodology of combining a library pruning approach with spectral unmixing provides a solid framework for the year-round monitoring of plant production systems.
      PubDate: June 2014
      Issue No: Vol. 7, No. 6 (2014)
  • Spectral-Spatial Classification of Hyperspectral Image Based on
           Discriminant Analysis
    • Authors: Yuan; H.;Tang, Y.Y.;Lu, Y.;Yang, L.;Luo, H.;
      Pages: 2035 - 2043
      Abstract: This paper proposes a spectral-spatial linear discriminant analysis (LDA) method for the hyperspectral image classification. A natural assumption is that similar samples have similar structure in the dimensionality reduced feature space. The proposed method uses a local scatter matrix from a small neighborhood as a regularizer incorporated into the objective function of LDA. Different from traditional LDA and its variants, our proposed method yields a self-adaptive projection matrix for dimension reduction, which improves the classification accuracy and avoids running out of memory. In order to consider the nonlinear case, this paper generalizes our linear version to its kernel version. Experimental results demonstrate that our proposed methods outperform several dimension reduction algorithms.
      PubDate: June 2014
      Issue No: Vol. 7, No. 6 (2014)
  • Modified Co-Training With Spectral and Spatial Views for Semisupervised
           Hyperspectral Image Classification
    • Authors: Zhang; X.;Song, Q.;Liu, R.;Wang, W.;Jiao, L.;
      Pages: 2044 - 2055
      Abstract: Hyperspectral images are characterized by limited labeled samples, large number of spectral channels, and existence of noise and redundancy. Supervised hyperspectral image classification is difficult due to the unbalance between the high dimensionality of the data and the limited labeled training samples available in real analysis scenarios. The collection of labeled samples is generally hard, expensive, and time-consuming, whereas unlabeled samples can be obtained much easier. This observation has fostered the idea of adopting semisupervised learning techniques in hyperspectral image classification. In this paper, a semisupervised method based on a modified co-training process with spectral and spatial views is proposed for hyperspectral image classification. The original spectral features and the 2-D Gabor features extracted from spatial domains are adopted as two distinct views for co-training, which considers both the spectral and spatial information. Then, a modified co-training process with a new sample selection scheme is presented, which can effectively improve the co-training performance, especially when there are extremely limited labeled samples available. Experiments carried out on two real hyperspectral images show the superiority of the proposed semisupervised method with the modified co-training process over the corresponding supervised techniques, the semisupervised method with the conventional co-training version, and the semisupervised graph-based method.
      PubDate: June 2014
      Issue No: Vol. 7, No. 6 (2014)
  • A Nonlocal Weighted Joint Sparse Representation Classification Method for
           Hyperspectral Imagery
    • Authors: Zhang; H.;Li, J.;Huang, Y.;Zhang, L.;
      Pages: 2056 - 2065
      Abstract: As a powerful and promising statistical signal modeling technique, sparse representation has been widely used in various image processing and analysis fields. For hyperspectral image classification, previous studies have shown the effectiveness of the sparsity-based classification methods. In this paper, we propose a nonlocal weighted joint sparse representation classification (NLW-JSRC) method to improve the hyperspectral image classification result. In the joint sparsity model (JSM), different weights are utilized for different neighboring pixels around the central test pixel. The weight of one specific neighboring pixel is determined by the structural similarity between the neighboring pixel and the central test pixel, which is referred to as a nonlocal weighting scheme. In this paper, the simultaneous orthogonal matching pursuit technique is used to solve the nonlocal weighted joint sparsity model (NLW-JSM). The proposed classification algorithm was tested on three hyperspectral images. The experimental results suggest that the proposed algorithm performs better than the other sparsity-based algorithms and the classical support vector machine hyperspectral classifier.
      PubDate: June 2014
      Issue No: Vol. 7, No. 6 (2014)
  • Classification of Hyperspectral Data Using an AdaBoostSVM Technique
           Applied on Band Clusters
    • Authors: Ramzi; P.;Samadzadegan, F.;Reinartz, P.;
      Pages: 2066 - 2079
      Abstract: Supervised classification of hyperspectral image data using conventional statistical classification methods is difficult because a sufficient number of training samples is often not available for the wide range of spectral bands. In addition, spectral bands are usually highly correlated and contain data redundancies because of the short spectral distance between the adjacent bands. To address these limitations, a multiple classifier system based on Adaptive Boosting (AdaBoost) is proposed and evaluated to classify hyperspectral data. In this method, the hyperspectral datasets are first split into several band clusters based on the similarities between the contiguous bands. In an AdaBoost classification system, the redundant and noninformative bands in each cluster are then removed using an optimal band selection technique. Next, a support vector machine (SVM) is applied to each refined cluster based on the classification results of previous clusters, and the results of these classifiers are fused using the weights obtained from the AdaBoost processing. Experimental results with standard hyperspectral datasets clearly demonstrate the superiority of the proposed algorithm with respect to both global and class accuracies, when compared to another ensemble classifiers such as simple majority voting and Naïve Bayes to combine decisions from each cluster, a standard SVM applied on the selected bands of entire datasets and on all the spectral bands. More specifically, the proposed method performs better than other approaches, especially in datasets which contain classes with greater complexity and fewer available training samples.
      PubDate: June 2014
      Issue No: Vol. 7, No. 6 (2014)
  • Dynamic Linear Classifier System for Hyperspectral Image Classification
           for Land Cover Mapping
    • Authors: Damodaran; B.B.;Nidamanuri, R.R.;
      Pages: 2080 - 2093
      Abstract: Exploitation of the spectral capabilities of modern hyperspectral image demands efficient preprocessing and analyses methods. Analysts’ choice of classifier and dimensionality reduction (DR) method and the harmony between them determine the accuracy of image classification. Multiple classifier system (MCS) has the potential to combine the relative advantages of several classifiers into a single image classification exercise for the hyperspectral image classification. In this paper, we propose an algorithmic extension of the MCS, named as dynamic classifier system (DCS), which exploits the context-based image and information class characteristics represented by multiple DR methods for hyperspectral image classification for land cover mapping. The proposed DCS algorithm pairs up optimal combinations of classifiers and DR methods specific to the hyperspectral image and performs image classifications based only on the identified combinations. Further, the impact of various trainable and nontrainable combination functions on the performance of the proposed DCS has been assessed. Image classifications were carried out on five multi-site airborne hyperspectral images using the proposed DCS and were compared with the MCS and SVM based supervised image classifications with and without DR. The results indicate the potential of the proposed DCS algorithm to increase the classification accuracy considerably over that of MCS or SVM supervised image classifications.
      PubDate: June 2014
      Issue No: Vol. 7, No. 6 (2014)
  • Deep Learning-Based Classification of Hyperspectral Data
    • Authors: Chen; Y.;Lin, Z.;Zhao, X.;Wang, G.;Gu, Y.;
      Pages: 2094 - 2107
      Abstract: Classification is one of the most popular topics in hyperspectral remote sensing. In the last two decades, a huge number of methods were proposed to deal with the hyperspectral data classification problem. However, most of them do not hierarchically extract deep features. In this paper, the concept of deep learning is introduced into hyperspectral data classification for the first time. First, we verify the eligibility of stacked autoencoders by following classical spectral information-based classification. Second, a new way of classifying with spatial-dominated information is proposed. We then propose a novel deep learning framework to merge the two features, from which we can get the highest classification accuracy. The framework is a hybrid of principle component analysis (PCA), deep learning architecture, and logistic regression. Specifically, as a deep learning architecture, stacked autoencoders are aimed to get useful high-level features. Experimental results with widely-used hyperspectral data indicate that classifiers built in this deep learning-based framework provide competitive performance. In addition, the proposed joint spectral–spatial deep neural network opens a new window for future research, showcasing the deep learning-based methods’ huge potential for accurate hyperspectral data classification.
      PubDate: June 2014
      Issue No: Vol. 7, No. 6 (2014)
  • A Probability-Based Improved Binary Encoding Algorithm for Classification
           of Hyperspectral Images
    • Authors: Xie; H.;Tong, X.;
      Pages: 2108 - 2118
      Abstract: This paper presents a probability-based improved binary encoding algorithm (PIBE) for classification of hyperspectral imagery. In the proposed PIBE method, the spectral, texture and shape information from hyperspectral images as well as height information from digital elevation models (if available) are combined to form a binary code. Based on this, a probability-based approach is further introduced to match the constructed binary code to the corresponding one obtain from target classes (or training data set). Some experiments on a pair of hyperspectral images confirm the effectiveness of the proposed PIBE method.
      PubDate: June 2014
      Issue No: Vol. 7, No. 6 (2014)
  • Model Selection and Classification With Multiple Kernel Learning for
           Hyperspectral Images via Sparsity
    • Authors: Gu; Y.;Gao, G.;Zuo, D.;You, D.;
      Pages: 2119 - 2130
      Abstract: The goal of multiple kernel learning (MKL) is to simultaneously learn a kernel and the associated predictor in task of supervised learning. In this paper, a new sparse MKL (SMKL) algorithm is proposed to simultaneously carry out classification and kernel interpretation on hyperspectral remote sensing images. First, the multiscale Gaussian kernels are adopted as basis kernels, and learning from these basis kernels is then formulated as a problem of maximizing variance projection, which can be solved by singular value decomposition (SVD). A cardinality-based constraint is then involved to control the sparsity of the MKL and selection of the Gaussian kernel scales for improving the interpretability. This cardinality-constrained optimization can be further converted to a convex optimization. The proposed MKL algorithm can achieve a good classification performance by using a linear combination of only a few kernels. The experiments are conducted on three real hyperspectral datasets, and the results prove the effectiveness of the SMKL in terms of classification statistics and computational feasibility, by comparing it with the state-of-the-art MKL algorithms. More important, interpretability of learning model can be preliminary addressed by the proposed SMKL.
      PubDate: June 2014
      Issue No: Vol. 7, No. 6 (2014)
  • Harmonic Analysis for Hyperspectral Image Classification Integrated With
           PSO Optimized SVM
    • Authors: Xue; Z.;Du, P.;Su, H.;
      Pages: 2131 - 2146
      Abstract: A novel hyperspectral image classification approach named as HA-PSO-SVM is proposed by integrating the harmonic analysis (HA), particle swarm optimization (PSO), and support vector machine (SVM). In the combined method, HA is first proposed to transform the pixels from spectral domain into frequency domain expressed by amplitude, phase and residual, yielding more functional and discriminative features for classification purpose. In this step, the original pixel vector can also be reconstructed. Then, PSO is adapted to optimize the penalty parameter C and the kernel parameter ${{gammab}}$ for SVM, which leads to improved classification performance. Finally, the extracted features are classified with the optimized model. The experimental results with three hyperspectral data sets collected by the airborne visible infrared imaging spectrometer (AVIRIS) and the reflective optics spectrographic imaging system (ROSIS) indicate that the proposed method provides improved classification performance compared with some related techniques in terms of both the classification accuracy and the computational time.
      PubDate: June 2014
      Issue No: Vol. 7, No. 6 (2014)
  • Automatic Framework for Spectral–Spatial Classification Based on
           Supervised Feature Extraction and Morphological Attribute Profiles
    • Authors: Ghamisi; P.;Benediktsson, J.A.;Cavallaro, G.;Plaza, A.;
      Pages: 2147 - 2160
      Abstract: Supervised classification plays a key role in terms of accurate analysis of hyperspectral images. Many applications can greatly benefit from the wealth of spectral and spatial information provided by these kind of data, including land-use and land-cover mapping. Conventional classifiers treat hyperspectral images as a list of spectral measurements and do not consider spatial dependencies of the adjacent pixels. To overcome these limitations, classifiers need to use both spectral and spatial information. In this paper, a framework for automatic spectral–spatial classification of hyperspectral images is proposed. In order to extract the spatial information, Extended Multi-Attribute Profiles (EMAPs) are taken into account. In addition, in order to reduce the redundancy of features and address the so-called curse of dimensionality, different supervised feature extraction (FE) techniques are considered. The final classification map is provided by using a random forest classifier. The proposed automatic framework is tested on two widely used hyperspectral data sets; Pavia University and Indian Pines. Experimental results confirm that the proposed framework automatically provides accurate classification maps in acceptable CPU processing times.
      PubDate: June 2014
      Issue No: Vol. 7, No. 6 (2014)
  • Improving the Dynamic Clustering of Hyperspectral Data Based on the
           Integration of Swarm Optimization and Decision Analysis
    • Authors: Naeini; A.A.;Homayouni, S.;Saadatseresht, M.;
      Pages: 2161 - 2173
      Abstract: Unsupervised or clustering algorithms can be considered to overcome the need for both high-quantity and high-quality training data for hyperspectral data classification. One of the most widely used algorithms for the clustering of remotely-sensed data is partitional clustering. Partitional clustering is affected by 1) the optimal number of clusters (NOC), 2) the position of cluster centers in hyper-dimension space, and 3) a set of optimally discriminating spectral bands. Among these three parameters, the NOC and their positions can be found simultaneously by dynamic clustering approaches. In this paper, an innovative two-stage dynamic clustering method is proposed and evaluated. In the first stage, the optimum set of solutions is achieved by a multi-objective particle swarm optimization. Then, using an efficient multi-criteria decision-making method, namely, the technique for order of preference by similarity to ideal solution (TOPSIS), a ranking is done among the optimal set of solutions to select the best one. Comparisons with some classic algorithms reveal that the proposed method is more effective at detecting the optimal number and position of clusters. In addition, the proposed algorithm generates better clustering results for hyperspectral data. Indeed, our method leads to a 5%–10% improvement upon classification accuracy.
      PubDate: June 2014
      Issue No: Vol. 7, No. 6 (2014)
  • Hyperspectral Image Classification Based on Regularized Sparse
    • Authors: Yuan; H.;Tang, Y.Y.;Lu, Y.;Yang, L.;Luo, H.;
      Pages: 2174 - 2182
      Abstract: Sparsity-based models have been widely applied to hyperspectral image (HSI) classification. The class label of the test sample is determined by the minimum residual error based on the sparse vector, which is viewed as a pattern of original sample in the sparsity-based model. From the aspect of pattern classification, similar samples in the same class should have similar patterns. However, due to the independent sparse reconstruction process, the similarity among the sparse vectors of these similar samples is lost. To enforce such similarity information, a regularized sparse representation (RSR) model is proposed. First, a centralized quadratic constraint as the regularization term is incorporated into the objective function of ${bf {ell _1}}$ -norm sparse representation model. Second, RSR can be effectively solved by the feature-sign search algorithm. Experimental results demonstrate that RSR can achieve excellent classification performance.
      PubDate: June 2014
      Issue No: Vol. 7, No. 6 (2014)
  • A Study on the Effectiveness of Different Independent Component Analysis
           Algorithms for Hyperspectral Image Classification
    • Authors: Falco; N.;Benediktsson, J.A.;Bruzzone, L.;
      Pages: 2183 - 2199
      Abstract: This paper presents a thorough study on the performances of different independent component analysis (ICA) algorithms for the extraction of class-discriminant information in remote sensing hyperspectral image classification. The study considers the three implementations of ICA that are most widely used in signal processing, namely Infomax, FastICA, and JADE. The analysis aims to address a number of important issues regarding the use of ICA in the RS domain. Three scenarios are considered and the performances of the ICA algorithms are evaluated and compared against each other, in order to reach the final goal of identifying the most suitable approach to the analysis of hyperspectral images in supervised classification. Different feature extraction and selection techniques are used for dimensionality reduction with ICA and are then compared to the commonly used strategy, which is based on preprocessing data with principal components analysis (PCA) prior to classification. Experimental results obtained on three real hyperspectral data sets from each of the considered algorithms are presented and analyzed in terms of both classification accuracies and computational time.
      PubDate: June 2014
      Issue No: Vol. 7, No. 6 (2014)
  • Joint Within-Class Collaborative Representation for Hyperspectral Image
    • Authors: Li; W.;Du, Q.;
      Pages: 2200 - 2208
      Abstract: Representation-based classification has gained great interest recently. In this paper, we extend our previous work in collaborative representation-based classification to spatially joint versions. This is due to the fact that neighboring pixels tend to belong to the same class with high probability. Specifically, neighboring pixels near the test pixel are simultaneously represented via a joint collaborative model of linear combinations of labeled samples, and the weights for representation are estimated by an ${ell _2}$ -minimization derived closed-form solution. Experimental results confirm that the proposed joint within-class collaborative representation outperforms other state-of-the-art techniques, such as joint sparse representation and support vector machines with composite kernels.
      PubDate: June 2014
      Issue No: Vol. 7, No. 6 (2014)
  • Informational Clustering of Hyperspectral Data
    • Authors: Pompilio; L.;Pepe, M.;Pedrazzi, G.;Marinangeli, L.;
      Pages: 2209 - 2223
      Abstract: Hyperspectral remote sensing is recognized as a powerful tool for mineralogical mapping of exposed surfaces on Earth and planets, as well. It allows for more rigorous discrimination among materials than multispectral imaging. Nevertheless, the huge data volume that comes with single observations results in severe limitations to successful data exploitation. Many techniques of feature reduction that have been developed so far do not allow for the complete exploitation of the informational content of the hyper-dimensional space. The present investigation aims at providing a feature reduction technique that preserves the spectral information and improves the classification results. We accomplished the feature reduction of synthetic and real hypercubes through exponential Gaussian optimization (EGO) and compared the results of k-means, spectral angle mapper (SAM), support vector machines (SVMs), and CLUES clustering techniques. The results show that the k-means clustering of hyper-dimensional spaces is the most efficient technique, but it does not automatically retrieve the optimal number of clusters. The SAM and SVM techniques give discrete results in terms of data partitioning, although the process of endmembers’ selection is challenging and the definition of model parameters is not trivial. The combination of EGO modeling and CLUES algorithm allows for correctly estimating the number of clusters and deriving the accurate partitions when the cluster separability lies on two variables, at least. With real data, the CLUES clustering in the reduced space allows for higher overall performances than the more conventional techniques, although it underestimates the number of categories.
      PubDate: June 2014
      Issue No: Vol. 7, No. 6 (2014)
  • (Semi-) Supervised Probabilistic Principal Component Analysis for
           Hyperspectral Remote Sensing Image Classification
    • Authors: Xia; J.;Chanussot, J.;Du, P.;He, X.;
      Pages: 2224 - 2236
      Abstract: In this paper, we have applied supervised probabilistic principal component analysis (SPPCA) and semi-supervised probabilistic principal component analysis (S $^{2}$ PPCA) for feature extraction in hyperspectral remote sensing imagery. The two models are all based on probabilistic principal component analysis (PPCA) using EM learning algorithm. SPPCA only relies on the labeled samples into the projection phase, while S $^{2}$ PPCA is able to incorporate both the labeled and unlabeled information. Experimental results on three real hyperspectral images demonstrate the SPPCA and S $^{2}$ PPCA outperform some conventional feature extraction methods for classifying hyperspectral remote sensing image with low computational complexity.
      PubDate: June 2014
      Issue No: Vol. 7, No. 6 (2014)
  • An Operational Approach to PCA+JPEG2000 Compression of Hyperspectral
    • Authors: Du; Q.;Ly, N.;Fowler, J.E.;
      Pages: 2237 - 2245
      Abstract: Lossy-compression algorithms typically adopted for hyperspectral remote-sensing imagery—such as JPEG2000—usually produce a monotonically increasing signal-to-noise ratio (SNR) for increasing bitrate. Consequently, it is a common philosophy to employ as large a bitrate as possible so as to obtain the highest achievable SNR. However, it has been observed previously that a higher SNR may not necessarily correspond to better performance at data-analysis tasks, such as classification, anomaly detection, or linear unmixing. Considered specifically is the coupling of JPEG2000 with principal component analysis for spectral decorrelation such that only a few principal components are retained, and, for this compression paradigm, a technique to determine an operational bitrate is proposed with the aim of preserving both the majority of information in a dataset as well as its anomalous pixels. This operational bitrate may be much less than the largest bitrate that the system can allow. Experimental results show that classification and unmixing applied to reconstructed data after compression at this operational bitrate result in performance that is the same as or better than that achieved at higher bitrates; meanwhile, removal and lossless storage of anomalies prior to compression results in their perfect preservation in the reconstructed dataset.
      PubDate: June 2014
      Issue No: Vol. 7, No. 6 (2014)
  • Compression of Hyperspectral Images Containing a Subpixel Target
    • Authors: Huber-Lerner; M.;Hadar, O.;Rotman, S.R.;Huber-Shalem, R.;
      Pages: 2246 - 2255
      Abstract: Hyperspectral (HS) image sensors measure the reflectance of each pixel at a large number of narrow spectral bands, creating a three-dimensional representation of the captured scene. The HS image (HSI) consumes a great amount of storage space and transmission time. Hence, it would be desirable to reduce the image representation to the extent possible using a compression method appropriate to the usage and processing of the image. Many compression methods have been proposed aiming at different applications and fields. This research focuses on the lossy compression of images that contain subpixel targets. This target type requires minimum compression loss over the spatial dimension in order to preserve the target, and the maximum possible spectral compression that would still enable target detection. For this target type, we propose the PCA-DCT (principle component analysis followed by the discrete cosine transform) compression method. It combines the PCA’s ability to extract the background from a small number of components, with the individual spectral compression of each pixel of the residual image, obtained by excluding the background from the HSI, using quantized DCT coefficients. The compression method is kept simple for fast processing and implementation, and considers lossy compression only on the spectral axis. The spectral compression achieves a compression ratio of over 20. The popular Reed-Xiaoli (RX) algorithm and the improved quasi-local RX ( ${bf RX}_{{bf QLC}}$ ) are used as target detection methods. The detection performance is evaluated using receiver operating characteristics (ROC) curve generation. The proposed compression method achieves maintained and enhanced detection performance, compared to the detection performance of the original image, mainly due to its inherent smoothing and noise reduction effects. Our proposed method is also compared with two other compression m- thods: PCA-ICA (independent component analysis) and band decimation (BandDec), yielding superior results for high compression rates.
      PubDate: June 2014
      Issue No: Vol. 7, No. 6 (2014)
  • Efficient Implementation of Hyperspectral Anomaly Detection Techniques on
           GPUs and Multicore Processors
    • Authors: Molero; J.M.;Garzon, E.M.;Garcia, I.;Quintana-Orti, E.S.;Plaza, A.;
      Pages: 2256 - 2266
      Abstract: Anomaly detection is an important task for hyperspectral data exploitation. Although many algorithms have been developed for this purpose in recent years, due to the large dimensionality of hyperspectral image data, fast anomaly detection remains a challenging task. In this work, we exploit the computational power of commodity graphics processing units (GPUs) and multicore processors to obtain implementations of a well-known anomaly detection algorithm developed by Reed and Xiaoli (RX algorithm), and a local (LRX) variant, which basically consists in applying the same concept to a local sliding window centered around each image pixel. LRX has been shown to be more accurate to detect small anomalies but computationally more expensive than RX. Our interest is focused on improving the computational aspects, not only through efficient parallel implementations, but also by analyzing the mathematical issues of the method and adopting computationally inexpensive solvers. Futhermore, we also assess the energy consumption of the newly developed parallel implementations, which is very important in practice. Our optimizations (based on software and hardware techniques) result in a significant reduction of execution time and energy consumption, which are keys to increase the practical interest of the considered algorithms. Indeed, for RX, the runtime obtained is less than the data acquisition time when real hyperspectral images are used. Our experimental results also indicate that the proposed optimizations and the parallelization techniques can significantly improve the general performance of the RX and LRX algorithms while retaining their anomaly detection accuracy.
      PubDate: June 2014
      Issue No: Vol. 7, No. 6 (2014)
  • A New Digital Repository for Hyperspectral Imagery With Unmixing-Based
           Retrieval Functionality Implemented on GPUs
    • Authors: Sevilla; J.;Plaza, A.;
      Pages: 2267 - 2280
      Abstract: Over the last few years, hyperspectral image data have been collected for a large number of locations over the world, using a variety of instruments for Earth observation. In addition, several new hyperspectral missions will become operational in the near future. Despite the increasing availability and large volume of hyperspectral data in many applications, there is no common repository of hyperspectral data intended to distribute and share free hyperspectral data sets in the community. Quite opposite, the hyperspectral data sets which are available for public use are spread among different storage locations and exhibit significant heterogeneity regarding their format, associated meta-data (if any), or ground-truth information. The development of a standardized hyperspectral data repository is a highly desired goal in the remote sensing community. In this paper, we take a necessary first step toward the development of a completely open digital repository for remotely sensed hyperspectral data. The proposed system (available online for public use at: allows uploading new hyperspectral data sets along with meta-data, ground-truth, analysis results, and pointers to bibliographic references describing the use of the data. The current implementation consists of a front-end which allows management of hyperspectral images through a web interface. The system is implemented on a parallel cluster system in order to guarantee storage availability and fast performance. The system includes a spectral unmixing-guided content-based image retrieval (CBIR) functionality which allows searching for images from the database using queries or available information such as spectral libraries. Specifically, for each new hyperspectral scene which is cataloged in our system, we extract the spectrally pure components (endmembers) and their associated fractional- abundances, and then store this information as meta-data associated to the hyperspectral image. The meta-data can be used to efficiently retrieve images based on their information content. In order to accelerate the process of obtaining the meta-data for a new entry in the system, we develop efficient implementations of spectral unmixing algorithms on graphics processing units (GPUs). This paper particularly focuses on the software design of the system and provides an experimental validation of the unmixing-based retrieval functionality using both synthetic and real hyperspectral images.
      PubDate: June 2014
      Issue No: Vol. 7, No. 6 (2014)
  • Multi-GPU Implementation of the Minimum Volume Simplex Analysis Algorithm
           for Hyperspectral Unmixing
    • Authors: Agathos; A.;Li, J.;Petcu, D.;Plaza, A.;
      Pages: 2281 - 2296
      Abstract: Spectral unmixing is an important task in remotely sensed hyperspectral data exploitation. The linear mixture model has been widely used to unmix hyperspectral images by identifying a set of pure spectral signatures, called endmembers, and estimating their respective abundances in each pixel of the scene. Several algorithms have been proposed in the recent literature to automatically identify endmembers, even if the original hyperspectral scene does not contain any pure signatures. A popular strategy for endmember identification in highly mixed hyperspectral scenes has been the minimum volume simplex analysis (MVSA), known to be a computationally very expensive algorithm. This algorithm calculates the minimum volume enclosing simplex, as opposed to other algorithms that perform maximum simplex volume analysis (MSVA). The high computational complexity of MVSA, together with its very high memory requirements, has limited its adoption in the hyperspectral imaging community. In this paper, we develop several optimizations to the MVSA algorithm. The main computational task of MVSA is the solution of a quadratic optimization problem with equality and inequality constraints, with the inequality constraints being in the order of the number of pixels multiplied by the number of endmembers. As a result, storing and computing the inequality constraint matrix is highly inefficient. The first optimization presented in this paper uses algebra operations in order to reduce the memory requirements of the algorithm. In the second optimization, we use graphics processing units (GPUs) to effectively solve (in parallel) the quadratic optimization problem involved in the computation of MVSA. In the third optimization, we extend the single GPU implementation to a multi-GPU one, developing a hybrid strategy that distributes the computation while taking advantage of GPU accelerators at each node. The presented optimizations are tested in different analysis scenarios (using both synthetic - nd real hyperspectral data) and shown to provide state-of-the-art results from the viewpoint of unmixing accuracy and computational performance. The speedup achieved using the full GPU cluster compared to the CPU implementation in tenfold in a real hyperspectral image.
      PubDate: June 2014
      Issue No: Vol. 7, No. 6 (2014)
  • Hyperspectral Unmixing on Multicore DSPs: Trading Off Performance for
    • Authors: Castillo; M.I.;Fernandez, J.C.;Igual, F.D.;Plaza, A.;Quintana-Orti, E.S.;Remon, A.;
      Pages: 2297 - 2304
      Abstract: Wider coverage of observation missions will increase onboard power restrictions while, at the same time, pose higher demands from the perspective of processing time, thus asking for the exploration of novel high-performance and low-power processing architectures. In this paper, we analyze the acceleration of spectral unmixing, a key technique to process hyperspectral images, on multicore architectures. To meet onboard processing restrictions, we employ a low-power Digital Signal Processor (DSP), comparing processing time and energy consumption with those of a representative set of commodity architectures. We demonstrate that DSPs offer a fair balance between ease of programming, performance, and energy consumption, resulting in a highly appealing platform to meet the restrictions of current missions if onboard processing is required.
      PubDate: June 2014
      Issue No: Vol. 7, No. 6 (2014)
  • Assessing the Performance-Energy Balance of Graphics Processors for
           Spectral Unmixing
    • Authors: Sanchez; S.;Leon, G.;Plaza, A.;Quintana-Orti, E.S.;
      Pages: 2305 - 2316
      Abstract: Remotely sensed hyperspectral imaging missions are often limited by onboard power restrictions while, simultaneously, require high computing power in order to address applications with relevant constraints in terms of processing times. In recent years, graphics processing units (GPUs) have emerged as a commodity computing platform suitable to meet real-time processing requirements in hyperspectral image processing. On the other hand, GPUs are power-hungry devices, which result in the need to explore the tradeoff between the expected high performance and the significant power consumption of computing architectures suitable to perform fast processing of hyperspectral images. In this paper, we explore the balance between computing performance and power consumption of GPUs in the context of a popular hyperspectral imaging application, such as spectral unmixing. Specifically, we investigate several processing chains for spectral unmixing and evaluate them on three different GPUs, corresponding to the two latest generations of GPUs from NVIDIA (“Fermi” and “Kepler”), as well as an alternative low-power system more suitable for embedded appliances. Our paper provides some observations about the possibility to use GPUs as effective onboard devices in hyperspectral imaging applications.
      PubDate: June 2014
      Issue No: Vol. 7, No. 6 (2014)
  • An Overview of Background Modeling for Detection of Targets and Anomalies
           in Hyperspectral Remotely Sensed Imagery
    • Authors: Matteoli; S.;Diani, M.;Theiler, J.;
      Pages: 2317 - 2336
      Abstract: This paper reviews well-known classic algorithms and more recent experimental approaches for distinguishing the weak signal of a target (either known or anomalous) from the cluttered background of a hyperspectral image. Making this distinction requires characterization of the targets and characterization of the backgrounds, and our emphasis in this review is on the backgrounds. We describe a variety of background modeling strategies—Gaussian and non-Gaussian, global and local, generative and discriminative, parametric and nonparametric, spectral and spatio-spectral—in the context of how they relate to the target and anomaly detection problems. We discuss the major issues addressed by these algorithms, and some of the tradeoffs made in choosing an effective algorithm for a given detection application. We identify connections among these algorithms and point out directions where innovative modeling strategies may be developed into detection algorithms that are more sensitive and reliable.
      PubDate: June 2014
      Issue No: Vol. 7, No. 6 (2014)
  • Performance Prediction of Matched Filter and Adaptive Cosine Estimator
           Hyperspectral Target Detectors
    • Authors: Truslow; E.;Manolakis, D.;Pieper, M.;Cooley, T.;Brueggeman, M.;
      Pages: 2337 - 2350
      Abstract: Many applications of hyperspectral remote sensing involve the detection of subpixel targets for search and rescue or defense and intelligence operations. The design and potential capabilities of these systems depends on their target detection performance. Therefore, it is important to have tools that reliably predict the performance of target detection systems under different realistic situations. The purpose of this paper is to present a hyperspectral target performance prediction model for the widely used matched filter (MF) and adaptive cosine estimator (ACE) detectors. We use a replacement signal model for resolved and subpixel targets and a finite probability mixture of $t$ -elliptically contoured distributions ( $t$ -ECDs) for the background. A major contribution of this paper is the development of a robust analytical and numerical approach to determine the output distribution of ACE for mixtures of $t$ -ECDs. The proposed technique can be a very useful tool for evaluating target detection performance for highly complex backgrounds.
      PubDate: June 2014
      Issue No: Vol. 7, No. 6 (2014)
  • Weighted-RXD and Linear Filter-Based RXD: Improving Background Statistics
           Estimation for Anomaly Detection in Hyperspectral Imagery
    • Authors: Guo; Q.;Zhang, B.;Ran, Q.;Gao, L.;Li, J.;Plaza, A.;
      Pages: 2351 - 2366
      Abstract: Anomaly detection is an active topic in hyperspectral imaging, with many practical applications. Reed-Xiaoli detector (RXD), a widely used method for anomaly detection, uses the covariance matrix and mean vector to represent background signals, assuming that the background information adjusts to a multivariate normal distribution. However, in general, real images present very complex backgrounds. As a result, in many situations, the background information cannot be properly modeled. An important reason is that that background samples often contain also anomalous pixels and noise, which lead to a high false alarm rate. Therefore, the characterization of the background is essential for successful anomaly detection. In this paper, we develop two novel approaches: weighted-RXD (W-RXD) and linear filter-based RXD (LF-RXD) aimed at improving background in RXD-based anomaly detection. By reducing the weight of the anomalous pixels or noise signals and increasing the weight of the background samples, W-RXD can provide better estimations of the background information. In turn, LF-RXD uses the probability of each pixel as background to filter wrong anomalous or noisy instances. Our experimental results, intended to analyze the performance of the newly developed anomaly detectors, indicate that the proposed approaches achieve good performance when compared with other classic approaches for anomaly detection in the literature.
      PubDate: June 2014
      Issue No: Vol. 7, No. 6 (2014)
  • An Automatic Robust Iteratively Reweighted Unstructured Detector for
           Hyperspectral Imagery
    • Authors: Wang; T.;Du, B.;Zhang, L.;
      Pages: 2367 - 2382
      Abstract: Unstructured background model-based detectors, which usually utilize a single a priori target spectral signature as the input, have been successfully applied in various hyperspectral target detection applications. However, the detection results are greatly affected by the quality of the a priori target spectral signature, as the spectral variability phenomenon is universal in hyperspectral image data. This paper proposes an iteratively reweighted method to generate an optimal target spectrum from limited target training spectra, which is able to alleviate the spectral variation. When the priori target spectral can only be chosen from the hyperspectral image, an optimal target spectrum can be iteratively generated by adjusting the pixel signals with varying weights. The experimental results with three different types of real hyperspectral images confirm the robust performance of the proposed method.
      PubDate: June 2014
      Issue No: Vol. 7, No. 6 (2014)
  • An Inner-Product-Based Discriminative IRLS Algorithm for Sparse
           Hyperspectral Detection
    • Authors: Huang; Z.;Shi, Z.;Zhang, Y.;
      Pages: 2383 - 2392
      Abstract: Automatic target detection is an important application in the hyperspectral image processing field. It is currently known that any test pixels in hyperspectral images can be represented within a spectral dictionary using appropriate sparse coefficients. Based on this assumption, some sparsity-based algorithms are developed for hyperspectral detection. This kind of sparse learning method attempts to find the sparse representation from a spectral library, i.e., a dictionary data set from which useful information is extracted. Among these algorithms, the iteratively reweighted least squares (IRLS) strategy is believed to be a simple and useful tool for sparse representation. However, when dealing with the hyperspectral data, the dictionary for sparse learning is usually high-dimensional which dramatically increases the scale and complexity of sparse learning. In such cases, most sparsity-based algorithms including the IRLS strategy lost their efficacy. To deal with this situation, we propose a discriminative IRLS algorithm, called inner-product-based discriminative IRLS detector (IDIRLSD), which decreases the scale and complexity problem by discriminatively seeking a subdictionary that retains the most critical information. Also, IDIRLSD applies a convex minimization for approximately solving the sparse recovery problem. A weighted ${{mmb {ell}}_{bf 1}}$ minimization is relaxed and solved by IRLS strategy. The proposed algorithm applies an inner-product-based function for constructing the small-scale weighted ${{mmb {ell}}_{bf 1}}$ minimization with respect to the subdictionary. The solution provided by IDIRLSD is then applied to label the test pixel as target or background. Experimental results from both synthetic and real hyperspectral data demonstrate the improved efficacy of the proposed algorithm.
      PubDate: June 2014
      Issue No: Vol. 7, No. 6 (2014)
  • Advanced Anomalous Pixel Correction Algorithms for Hyperspectral Thermal
           Infrared Data: The TASI-600 Case Study
    • Authors: Santini; F.;Palombo, A.;Dekker, R.J.;Pignatti, S.;Pascucci, S.;Schwering, P.B.W.;
      Pages: 2393 - 2404
      Abstract: Anomalous pixel responses often seriously affect remote sensing applications, especially in the thermal spectral range. In this paper, a new method to identify and correct anomalous pixel responses is presented. The method was specifically developed to handle with hyperspectral data and is based on the statistical analysis of a gray scale RX detector (RXD) image applied on the focal plane space rather than on the image space. An iterative thresholding method to correct anomalous pixels in automatic modality was tuned. Moreover, a band depth-based method to properly restore the lost information was applied. The band depth method serves to prevent the creation of new artifacts during the anomalous pixel correction that could affect applications such as anomaly or change detection and classification for thermal infrared (TIR) hyperspectral imagery. In this paper, we take into consideration hyperspectral TASI-600 data acquired during recent airborne campaigns in Europe. Evidences of the benefits on remote sensing applications such as classification and change detection algorithms in urban areas are shown.
      PubDate: June 2014
      Issue No: Vol. 7, No. 6 (2014)
  • Hyperspectral and LiDAR Data Fusion: Outcome of the 2013 GRSS Data Fusion
    • Authors: Debes; C.;Merentitis, A.;Heremans, R.;Hahn, J.;Frangiadakis, N.;van Kasteren, T.;Liao, W.;Bellens, R.;Pizurica, A.;Gautama, S.;Philips, W.;Prasad, S.;Du, Q.;Pacifici, F.;
      Pages: 2405 - 2418
      Abstract: The 2013 Data Fusion Contest organized by the Data Fusion Technical Committee (DFTC) of the IEEE Geoscience and Remote Sensing Society aimed at investigating the synergistic use of hyperspectral and Light Detection And Ranging (LiDAR) data. The data sets distributed to the participants during the Contest, a hyperspectral imagery and the corresponding LiDAR-derived digital surface model (DSM), were acquired by the NSF-funded Center for Airborne Laser Mapping over the University of Houston campus and its neighboring area in the summer of 2012. This paper highlights the two awarded research contributions, which investigated different approaches for the fusion of hyperspectral and LiDAR data, including a combined unsupervised and supervised classification scheme, and a graph-based method for the fusion of spectral, spatial, and elevation information.
      PubDate: June 2014
      Issue No: Vol. 7, No. 6 (2014)
  • Fusion of Hyperspectral and LiDAR Data for Landscape Visual Quality
    • Authors: Yokoya; N.;Nakazawa, S.;Matsuki, T.;Iwasaki, A.;
      Pages: 2419 - 2425
      Abstract: Landscape visual quality is an important factor associated with daily experiences and influences our quality of life. In this work, the authors present a method of fusing airborne hyperspectral and mapping light detection and ranging (LiDAR) data for landscape visual quality assessment. From the fused hyperspectral and LiDAR data, classification and depth images at any location can be obtained, enabling physical features such as land-cover properties and openness to be quantified. The relationship between physical features and human landscape preferences is learned using least absolute shrinkage and selection operator (LASSO) regression. The proposed method is applied to the hyperspectral and LiDAR datasets provided for the 2013 IEEE GRSS Data Fusion Contest. The results showed that the proposed method successfully learned a human perception model that enables the prediction of landscape visual quality at any viewpoint for a given demographic used for training. This work is expected to contribute to automatic landscape assessment and optimal spatial planning using remote sensing data.
      PubDate: June 2014
      Issue No: Vol. 7, No. 6 (2014)
  • Comparative Sensor Fusion Between Hyperspectral and Multispectral
           Satellite Sensors for Monitoring Microcystin Distribution in Lake Erie
    • Authors: Chang; N.;Vannah, B.;Jeffrey Yang, Y.;
      Pages: 2426 - 2442
      Abstract: Urban growth and agricultural production have caused an influx of nutrients into Lake Erie, leading to eutrophication in the water body. These conditions result in the formation of algal blooms, some of which are toxic due to the presence of Microcystis (a cyanobacteria), which produces the hepatotoxin microcystin. The hepatotoxin microcystin threatens human health and the ecosystem, and it is a concern for water treatment plants using the lake water as a tap water source. This study demonstrates the prototype of a near real-time early warning system using integrated data fusion and mining (IDFM) techniques with the aid of both hyperspectral (MERIS) and multispectral (MODIS and Landsat) satellite sensors to determine spatiotemporal microcystin concentrations in Lake Erie. In the proposed IDFM, the MODIS images with high temporal resolution are fused with the MERIS and Landsat images with higher spatial resolution to create synthetic images on a daily basis. The spatiotemporal distributions of microcystin within western Lake Erie were then reconstructed using the band data from the fused products with machine learning or data mining techniques such as genetic programming (GP) models. The performance of the data mining models derived using fused hyperspectral and fused multispectral sensor data are quantified using four statistical indices. These data mining models were further compared with traditional two-band models in terms of microcystin prediction accuracy. This study confirmed that GP models outperformed traditional two-band models, and additional spectral reflectance data offered by hyperspectral sensors produces a noticeable increase in the prediction accuracy especially in the range of low microcystin concentrations.
      PubDate: June 2014
      Issue No: Vol. 7, No. 6 (2014)
  • Robust Registration by Rank Minimization for Multiangle
           Hyper/Multispectral Remotely Sensed Imagery
    • Authors: Hu; T.;Zhang, H.;Shen, H.;Zhang, L.;
      Pages: 2443 - 2457
      Abstract: Multiangle images are acquired over roughly the same earth surface from different angles, and accurate image registration is a key prerequisite for their application. In this paper, we propose a robust registration method by rank minimization (RRRM) for multiangle hyper/multispectral remotely sensed imagery (MA-HSI-MSI). First, the low-rank structure of the MA-HSI-MSI is exploited and utilized as the registration constraint, thus recasting the image registration problem as searching for an optimal set of transformations, such that the matrix of the transformed images can reach its minimum rank. Second, a patch-based registration scheme is adopted to solve the problem of inconsistent geometric distortion over the entire image, taking the homography model as the local transformation. An iterative convex optimization algorithm is then used to solve the rank minimization-based image registration model for each image patch. Finally, all the transformed patches are used to synthesize the final registration image. The experimental results demonstrate that the proposed low-rank registration method works effectively for CHRIS/Proba imagery and WorldView-2 imagery.
      PubDate: June 2014
      Issue No: Vol. 7, No. 6 (2014)
  • Hyperspectral Image Denoising Using First Order Spectral Roughness Penalty
           in Wavelet Domain
    • Authors: Rasti; B.;Sveinsson, J.R.;Ulfarsson, M.O.;Benediktsson, J.A.;
      Pages: 2458 - 2467
      Abstract: In this paper, a new denoising method for hyperspectral images is proposed using First Order Roughness Penalty (FORP). FORP is applied in the wavelet domain to exploit the Multi-Resolution Analysis (MRA) property of wavelets. Stein's Unbiased Risk Estimator (SURE) is used to choose the tuning parameters automatically. The simulation results show that the penalized least squares using FORP can improve the Signal to Noise Ratio (SNR) compared to other denoising methods. The proposed method is also applied to a corrupted hyperspectral data set and it is shown that certain classification indices improve significantly.
      PubDate: June 2014
      Issue No: Vol. 7, No. 6 (2014)
  • Hyperspectral Imagery Denoising Based on Oblique Subspace Projection
    • Authors: Wang; Q.;Zhang, L.;Tong, Q.;Zhang, F.;
      Pages: 2468 - 2480
      Abstract: This paper presents a hyperspectral imagery denoising algorithm based on oblique subspace projection (DOBSP), which considers the correlation between noise and signal. The algorithm first estimates the signal and noise through segmentation Gaussian filtering which can reduce more influence of the image texture than traditional Gaussian filtering. Then, signal and noise estimates are fed into principal component analysis (PCA) to identify their respective subspace basis vectors. Finally, these basis vectors are used to compute matrices of oblique subspace projection (OBSP), and the signal and noise are extracted from the original image through OBSP. We assessed the DOBSP algorithm using both simulated and real Hyperion images. The orthogonal subspace projection (OSP) which assumes that noise is independent on signal and the subspace-based striping noise reduction (SBSR) algorithm which uses polynomial model to describe the relationship between noise and signal were introduced for comparison. Compared with signal and noise results by OSP and SBSR, both signal and noise extracted by DOBSP on the simulated image are closer to the original simulated signal and noise, and the noise image obtained by DOBSP on the Hyperion image has fewer textures.
      PubDate: June 2014
      Issue No: Vol. 7, No. 6 (2014)
  • Classification of Australian Native Forest Species Using Hyperspectral
           Remote Sensing and Machine-Learning Classification Algorithms
    • Authors: Shang; X.;Chisholm, L.A.;
      Pages: 2481 - 2489
      Abstract: Mapping forest species is highly relevant for many ecological and forestry applications. In Australia, the classification of native forest species using remote sensing data remains a particular challenge since there are many eucalyptus species that belong to the same genus and, thus, exhibit similar biophysical characteristics. This study assessed the potential of using hyperspectral remote sensing data and state-of-the-art machine-learning classification algorithms to classify Australian forest species at the leaf, canopy and community levels in Beecroft Peninsula, NSW, Australia. Spectral reflectance was acquired from an ASD spectrometer and airborne Hymap imagery for seven native forest species over an Australian eucalyptus forest. Three machine-learning classification algorithms: Support Vector Machine (SVM), AdaBoost and Random Forest (RF) were applied to classify the species. A comparative study was carried out between machine-learning classification algorithms and Linear Discriminant Analysis (LDA). The classification results show that all machine-leaning classification algorithms significantly improve the results produced by LDA. At the leaf level, RF achieved the best classification accuracy (94.7%), and SVM outperformed the other algorithms at both the canopy (84.5%) and community levels (75.5%). This study demonstrates that hyperspectral remote sensing and machine-learning classification has substantial potential for the classification of Australian native forest species.
      PubDate: June 2014
      Issue No: Vol. 7, No. 6 (2014)
  • Discrete Wavelet Transform Approach for the Estimation of Crop Residue
           Mass From Spectral Reflectance
    • Authors: S Sahadevan; A.;Shrivastava, P.;Das, B.S.;M C, S.;
      Pages: 2490 - 2495
      Abstract: Estimation of crop residue mass (CRM) using cellulose absorption index (CAI) from spectral reflectance data is a widely used approach in crop residue management. A specific limitation with the CAI approach is its inefficacy to predict CRM at high residue loadings and its failure to account for the overlapping of residue fragments on soil surface. In this study, we used a combination of discrete wavelet transform (DWT) and partial least square regression (PLSR) to estimate CRM of rice, wheat, maize, sugarcane and soybean. We followed a wavelet packet approach to select appropriate DWT coefficients by examining the variance (referred to as DWTv-PLSR) and correlation (referred to as DWTc-PLSR) structure of the multi-resolution DWT coefficients. Results showed that the DWTc-PLSR approach yielded excellent predictability regardless of crop residue types. An interesting observation of this study is that the wavelet-based approaches showed significant spectral features in the visible and NIR range in contrast to the commonly used SWIR (2100 nm) range representing the CAI. Spectral reflectance curves in our study and those reported in the literature clearly show that both the depth and width of cellulose absorption peaks generally do not vary much with the residue mass. Such lack of sensitivity may have been portrayed in the DWTc-PLSR approach and this method appears to overcome the limitations of using CAI for crop residue assessment.
      PubDate: June 2014
      Issue No: Vol. 7, No. 6 (2014)
  • Seasonal Course of the Spectral Properties of Alder and Birch Leaves
    • Authors: Mottus; M.;Sulev, M.;Hallik, L.;
      Pages: 2496 - 2505
      Abstract: To interpret time series of hyperspectral measurements of vegetation canopies, the basic characteristics of the scattering elements forming these canopies—shoots, leaves, or needles—must be known. Unfortunately, data on the seasonal variation of leaf reflectance and transmittance are very scarce. To obtain a ground truth dataset applicable to modeling the phenological development of European (hemi)boreal forests, we measured the spectral properties of green leaves of two species common to this biome, gray alder (Alnus incana), and silver birch (Betula pendula). Measurements covered the full growing season of 2008 in Tõravere, Estonia. Leaves were sampled from sunlit locations and measured in a laboratory using an integrating sphere and a VNIR spectroradiometer. We measured four different optical parameters: directional-hemispherical reflectance and transmittance factors for leaf adaxial and abaxial surfaces. Leaf reflectance was used to calculate four leaf-level indices which, according to literature, are highly correlated with leaf chlorophyll content. Additionally, we calculated the derivative spectra of leaf reflectance in the red edge (RE) spectral region and fitted it with two Gaussian curves. Our analysis indicated continuous changes of leaf optical properties almost until the end of the growing season. The changes in the weights of the two Gaussian curves led to an effect known as peak jump, an abrupt shift in the RE inflection point. In near infrared (NIR), leaf absorption was negligible in the beginning of the growing period. However, we noted a slow but steady increase in the leaf NIR absorption with time.
      PubDate: June 2014
      Issue No: Vol. 7, No. 6 (2014)
  • Integrate Growing Temperature to Estimate the Nitrogen Content of Rice
           Plants at the Heading Stage Using Hyperspectral Imagery
    • Authors: Onoyama; H.;Ryu, C.;Suguri, M.;Iida, M.;
      Pages: 2506 - 2515
      Abstract: Ground-based hyperspectral imaging was used for estimating the nitrogen content of rice plants at the heading stage. The images were separated into two parts: 1) the rice plant; and 2) other elements using the equation of “GreenNDVI–NDVI.” ${mbi{Ref}}_{mbi{RICE}}$ was calculated as the ratio of the reflectance of the rice plant to that of a reference board. Partial least square (PLS) model using reflectance data (R-PLS model) and PLS model using reflectance and temperature data (RT-PLS) was constructed to compare the accuracy between them. RT-PLS model was developed to improve the accuracy of R-PLS model by considering the differences of weather condition among years. When the precision ( ${mbi{R}}^{bf 2}$ ) and accuracy [root-mean-square error (RMSE) and relative error (RE)] of each R-PLS model were evaluated for each year using twofold cross-validation, ${mbi{R}}^{bf 2}$ ranged from 0.42 to 0.81, RMSE ranged from 0.81 to ${bf 1.13}nbsphbox{bf gm}^{bf -2}$ , and RE ranged from 10.1% to 11.8%. When R-PLS model of each year was used to predict the other years’ data to determine the predictive power, RMSE values were higher (ranging from 1.40 to ${bf 5.82}nbsphbox{bf gm}^{bf -2}$ ) than those in each year’s validation value due to over- or underestimation. When an R-PLS model based on the data of 2 years was fitted, RMSE ranged from 1.11 to ${bf 4.15}nbsphbox{bf gm}^{bf -2}$ and RE ranged from 13.7% to 42.8%. By contrast, in RT-PLS models, RMSE and RE fell to less than ${bf 1.21}nbsphbox{bf gm}^{bf -2}$ and 12.3%, respectively. Thus, a combination of reflectance and temperature data was useful for constructing a model of rice plant at the heading stage.
      PubDate: June 2014
      Issue No: Vol. 7, No. 6 (2014)
  • New Optimized Spectral Indices for Identifying and Monitoring Winter Wheat
    • Authors: Huang; W.;Guan, Q.;Luo, J.;Zhang, J.;Zhao, J.;Liang, D.;Huang, L.;Zhang, D.;
      Pages: 2516 - 2524
      Abstract: The vegetation indices from hyperspectral data have been shown to be effective for indirect monitoring of plant diseases. However, a limitation of these indices is that they cannot distinguish different diseases on crops. We aimed to develop new spectral indices (NSIs) that would be useful for identifying different diseases on crops. Three different pests (powdery mildew, yellow rust, and aphids) in winter wheat were used in this study. The new optimized spectral indices were derived from a weighted combination of a single band and a normalized wavelength difference of two bands. The most and least relevant wavelengths for different diseases were first extracted from leaf spectral data using the RELIEF-F algorithm. Reflectance of a single band extracted from the most relevant wavelengths and the normalized wavelength difference from all possible combinations of the most and least relevant wavelengths were used to form the optimized spectral indices. The classification accuracies of these new indices for healthy leaves and leaves infected with powdery mildew, yellow rust, and aphids were 86.5%, 85.2%, 91.6%, and 93.5%, respectively. We also applied these NSIs for nonimaging canopy data of winter wheat, and the classification results of different diseases were promising. For the leaf scale, the powdery mildew-index (PMI) correlated well with the disease index (DI), supporting the use of the PMI to invert the severity of powdery mildew. For the canopy scale, the detection of the severity of yellow rust using the yellow rust-index (YRI) showed a high coefficient of determination ( ${mbi{R}}^{bf 2 =} {bf 0.86}$ ) between the estimated DI and its observations, suggesting that the NSIs may improve disease detection in precision agriculture application.
      PubDate: June 2014
      Issue No: Vol. 7, No. 6 (2014)
  • Effects of Different Illumination and Observation Techniques of Cultivated
           Soils on Their Hyperspectral Bidirectional Measurements Under Field and
           Laboratory Conditions
    • Authors: Cierniewski; J.;Kazmierowski, C.;Krolewicz, S.;Piekarczyk, J.;Wrobel, M.;Zagajewski, B.;
      Pages: 2525 - 2530
      Abstract: This paper evaluates the fitting of the hyperspectral bidirectional reflectance data of soil surfaces formed by a cultivator, a pulverizing harrow, and a smoothing harrow, collected in field conditions as illuminated by direct and diffuse solar radiation, to their bidirectional reflectance equivalents measured in the laboratory with only a direct radiation component using the same soil materials shaped such that their roughness was similar to that formed in the field by the farming tools mentioned above. Both kinds of data were measured by the same ASD FieldSpec 3 spectrophotometer attached to goniometric devices, which observed the soil surfaces under the same directions, pointing at various fragments of the surface under field conditions and always pointing at the center of the soil samples under laboratory conditions. The worst average fit for the analyzed soil surfaces did not exceed 36%. The fit was weaker if the soil was spectrally darker with a lower spectrum level, especially at lower solar zenith angles and higher soil surface roughness. It was found that the fit increased from 400 to 450 nm, and decreased especially for wavelengths between 1950 and 2300 nm. A less significant decrease in fit was revealed at around of 700, 940, and 1140 nm.
      PubDate: June 2014
      Issue No: Vol. 7, No. 6 (2014)
  • Spectral Unmixing-Based Crop Residue Estimation Using Hyperspectral Remote
           Sensing Data: A Case Study at Purdue University
    • Authors: Chi; J.;Crawford, M.M.;
      Pages: 2531 - 2539
      Abstract: Crop residue helps to moderate soil temperature and increase water use efficiency in the short term, while providing improvement in soil quality, increasing soil organic carbon, and facilitating biodegradation of pollutants for long-term sustainability. Since good management of crop residue can also increase irrigation efficiency and reduce erosion, remote sensing-based techniques are receiving increased attention for monitoring crop residue coverage. Indices based on differences and ratios of hyperspectral bands are considered state-of-the-art for operational applications, but are limited because of low signal-to-noise-ratio (SNR) image data from pushbroom sensors such as Hyperion. This study aims to investigate spectral unmixing as an alternative approach to effectively estimate and monitor crop residue cover with airborne and space-based hyperspectral sensors. The secondary aim is to compare traditional linear unmixing to manifold learning-based unmixing approaches to capture nonlinearities inherent in hyperspectral data. For the data in this case study, manifold learning approaches provide more robust estimates than either the cellulose absorption index (CAI) or linear unmixing of airborne and Hyperion hyperspectral data.
      PubDate: June 2014
      Issue No: Vol. 7, No. 6 (2014)
  • Estimation of Arsenic Contamination in Reclaimed Agricultural Soils Using
           Reflectance Spectroscopy and ANFIS Model
    • Authors: Tan; K.;Ye, Y.;Cao, Q.;Du, P.;Dong, J.;
      Pages: 2540 - 2546
      Abstract: Heavy metal contamination from anthropogenic sources is a threat to human health. To assess the feasibility of predicting surface soil arsenic (As) concentration from hyperspectral reflectance measurement, three different regression algorithms are compared in this paper, i.e., multiple linear regression (MLR), partial least squares regression (PLSR), and adaptive neural fuzzy inference system (ANFIS) modeling. Soil samples were taken from three study sites in mining/agricultural areas after reclamation. As concentration was determined by hydride generation atomic fluorescence spectrometry (HG-AFS) analysis, and the reflectance was measured with an analytical spectral devices (ASD) field spectrometer covering the spectral region of 350–2500 nm. First, after preprocessing of the original reflectance spectroscopy, the correlation coefficients between the As concentration and spectral reflectance measurement were derived. Characteristic bands were then chosen for the quantitative retrieval model. Finally, all of the 30 samples were divided into a calibration set and a validation set of 18 and 12 samples, respectively. When compared with the MLR and PLSR algorithms, the ANFIS model was the best retrieval model, with a coefficient of determination ( ${bf R}^{bf 2}$ ) of 0.94 and a root-mean-square error (RMSE) of 0.88. ANFIS model and reflectance spectroscopy therefore have the potential to map the spatial distribution of As abundance, with the aim of improving public health.
      PubDate: June 2014
      Issue No: Vol. 7, No. 6 (2014)
  • Comparison of Feature Reduction Algorithms for Classifying Tree Species
           With Hyperspectral Data on Three Central European Test Sites
    • Authors: Fassnacht; F.E.;Neumann, C.;Forster, M.;Buddenbaum, H.;Ghosh, A.;Clasen, A.;Joshi, P.K.;Koch, B.;
      Pages: 2547 - 2561
      Abstract: Tree species information is a basic variable for forest inventories. Knowledge on tree species is relevant for biomass estimation, habitat quality assessment, and biodiversity characterization. Hyperspectral data have been proven to have a high potential for the mapping of tree species composition. However, open questions remain concerning the robustness of existing classification approaches. Here, a number of classification approaches were compared to classify tree species from airborne hyperspectral data across three forest sites to identify a single approach which continuously delivers high classification performances over all test sites. Examined approaches included three feature selection methods [genetic algorithm (GA), support vector machines (SVM) wrapper, and sparse generalized partial least squares selection (PLS)] each combined with two nonparametric classifiers (SVM and Random Forest). Two further setups included classifications applied to the full hyperspectral dataset and to an image transformed with a minimum noise fraction (MNF) transformation. Results showed that SVM wrapper and the GA slightly outperformed the PLS-based algorithm. In most cases, the best classification runs involving a feature selection algorithm outperformed those incorporating the full hyperspectral dataset. However, the best overall results were obtained when using the first 10–20 components of the MNF-transformed image. Selected bands were frequently located in the visual region close to the green peak, at the chlorophyll absorption feature and the red edge rise as well as in three parts of the short-wave infrared region close to water absorption features. These findings are relevant for improving the robustness of tree species classifications from airborne hyperspectral data incorporating feature reduction techniques.
      PubDate: June 2014
      Issue No: Vol. 7, No. 6 (2014)
  • Determining the Effects of Storage on Cotton and Soybean Leaf Samples for
           Hyperspectral Analysis
    • Authors: Lee; M.A.;Huang, Y.;Yao, H.;Thomson, S.J.;Bruce, L.M.;
      Pages: 2562 - 2570
      Abstract: This paper studies the effect of storage techniques for transporting collected plant leaves from the field to the laboratory for hyperspectral analysis. The strategy of collecting leaf samples in the field for laboratory analysis is typically used when ground truthing is needed in remote sensing studies. Results indicate that the accuracy of hyperspectral measurements depends on a combination of storage technique (in a cooler or outside a cooler), time elapsed between collecting leaf samples in the field and measuring in the laboratory, and the plant species. A nonlinear model fitting method is proposed to estimate the spectrum of decaying plant leaves. This revealed that the reflectance of soybean leaves remained within the normal range for 45 min when the leaves were stored in a cooler, while soybean leaves stored outside a cooler remained within the normal range for 30 min. However, cotton leaves stored in a cooler decayed faster initially. Regardless of storage technique, results indicate that up to a maximum of 30 min can elapse between plant leaf sampling in the field and hyperspectral measurements in the laboratory. This study focused on cotton and soybean leaves, but the implication that time elapsing between sampling leaves and measuring their spectrum should be limited as much as possible can be applied to any study on other crop leaves. Results of the study also provide a guideline for crop storage limits when analyzing by laboratory hyperspectral sensing setting to improve the quality and reliability of data for precision agriculture.
      PubDate: June 2014
      Issue No: Vol. 7, No. 6 (2014)
  • Unmixing-Based Fusion of Hyperspatial and Hyperspectral Airborne Imagery
           for Early Detection of Vegetation Stress
    • Authors: Delalieux; S.;Zarco-Tejada, P.J.;Tits, L.;Bello, M.A.J.;Intrigliolo, D.S.;Somers, B.;
      Pages: 2571 - 2582
      Abstract: Many applications require a timely acquisition of high spatial and spectral resolution remote sensing data. This is often not achievable since spaceborne remote sensing instruments face a tradeoff between spatial and spectral resolution, while airborne sensors mounted on a manned aircraft are too expensive to acquire a high temporal resolution. This gap between information needs and data availability inspires research on using Remotely Piloted Aircraft Systems (RPAS) to capture the desired high spectral and spatial information, furthermore providing temporal flexibility. Present hyperspectral imagers on board lightweight RPAS are still rare, due to the operational complexity, sensor weight, and instability. This paper looks into the use of a hyperspectral–hyperspatial fusion technique for an improved biophysical parameter retrieval and physiological assessment in agricultural crops. First, a biophysical parameter extraction study is performed on a simulated citrus orchard. Subsequently, the unmixing-based fusion is applied on a real test case in commercial citrus orchards with discontinuous canopies, in which a more efficient and accurate estimation of water stress is achieved by fusing thermal hyperspatial and hyperspectral (APEX) imagery. Narrowband reflectance indices that have proven their effectiveness as previsual indicators of water stress, such as the Photochemical Reflectance Index (PRI), show a significant increase in tree water-stress detection when applied on the fused dataset compared to the original hyperspectral APEX dataset ( ${bf R}^{bf 2} = {bf 0.62}$ , ${bf p} lt {bf 0.001}$ vs. ${bf R}^{bf 2} = {bf 0.21}$ , ${bf p} gt {bf 0.1}$ ). Maximal ${bf R}^{bf 2}$ values of 0.93 and 0.86 are obtained by a linear relationship between the vegetation index and the resp., water and chlorophyll, parameter content maps.
      PubDate: June 2014
      Issue No: Vol. 7, No. 6 (2014)
  • Semi-Supervised Hyperspectral Subspace Learning Based on a Generalized
           Eigenvalue Problem for Regression and Dimensionality Reduction
    • Authors: Uto; K.;Kosugi, Y.;Saito, G.;
      Pages: 2583 - 2599
      Abstract: Manifold learning for the hyperspectral data structure of intra-class variation provides useful information for investigating the intrinsic coordinates corresponding to the quantitative properties inherent in the class. However, in the high-dimensional feature space, it is unfeasible to acquire a statistically sufficient number of labeled data to estimate the coordinates. In this paper, we propose semi-supervised regression and dimensionality reduction methods for hyperspectral subspace learning that utilize abundant unlabeled data and a small number of labeled data. The quantitative target variables for regression and the order constraints for dimensionality reduction are embedded in matrices representing data relations, i.e., a set of between-class scatter matrices, within-class scatter matrices, and supervised local attraction matrices. The optimal projection matrices are estimated by generalized eigenvalue problems based on the matrices. The proposed methods are applied to synthetic linear regression problems and dimensionality reduction problems based on a time-series of hyperspectral data for a deciduous broad-leaved forest to extract local coordinates related to phenological changes. The order consistency of the projections is assessed by evaluating an index based on the Mann-Kendall test statistics. The proposed methods demonstrate much better performances in terms of both regression and dimensionality reduction than the alternative supervised and unsupervised methods.
      PubDate: June 2014
      Issue No: Vol. 7, No. 6 (2014)
  • Continuous Fields From Imaging Spectrometer Data for Ecosystem Parameter
           Mapping and Their Potential for Animal Habitat Assessment in Alpine
    • Authors: Kneubuhler; M.;Damm, A.;Schweiger, A.;Risch, A.C.;Schutz, M.;Schaepman, M.E.;
      Pages: 2600 - 2610
      Abstract: Remote sensing offers an objective and efficient way to monitor ecosystem properties including their spatial variability across different land cover types. Especially, the representation of gradients of biochemical and structural properties of ecosystems using continuous fields (CF) approaches bears advantages compared to discrete land cover classification schemes. This paper presents a concept to synergistically generate CF maps of an alpine ecosystem parameter, i.e., total surface water content, from imaging spectrometer (IS) data. Further, the potential of linking such maps to ecological patterns, i.e., the spatial distribution of large ungulates is being assessed. In vegetated areas, total surface water content is considered as a surrogate of plant physiological status. Water is, besides temperature, light, or nutrients, an important limiting growth factor determining biomass production and therefore potential animal forage quantity in alpine grasslands. Resource ecology interested in trophic interactions between large ungulates and their forage requires spatial and temporal information on ecosystem properties and processes. The study area is located in the upper Trupchun Valley (Val Trupchun) in the Swiss National Park (SNP). The valley is famous for its high densities of chamois (Rupicapra rupicapra L.), ibex (Capra ibex L.), and red deer (Cervus elaphus L.). CF maps of total surface water content were derived from Airborne Prism EXperiment (APEX) IS data collected over the SNP in June 2010 and 2011. Abundance maps of predominant land cover classes were derived from linear spectral mixture analysis (SMA). They were then combined with water content information of the respective land cover originating from either empirically or physically based approaches. The resulting CF maps depicted a spatially continuous representation of relative total surface water content. APEX IS data from two consecut- ve seasons revealed differences in total surface water content in June 2010 and 2011, predominantly related to an advanced phenological development in spring 2011 and to considerable differences in snow cover between the 2 years. Linking total surface water content of grasslands to observed ungulates spatial distributions did not reveal any statistically significant patterns of habitat use. We conclude that water availability in Val Trupchun may not be the dominant limiting factor for potential forage quantity (biomass), or that ungulates choose their grazing sites based on other criteria, i.e., high nutritious quality (P, N). Nevertheless, multitemporal CF maps derived from APEX IS data were found to provide spatially explicit and fine-scaled information for analyses of an ecosystem’s total surface water content. The combination of multitemporal CF maps of a wide range of ecosystem parameters and more accurate and extensive observations of animal habitat use will contribute to ongoing and future vegetation-ungulates research in the SNP.
      PubDate: June 2014
      Issue No: Vol. 7, No. 6 (2014)
  • A Split-Window Model for Deriving Total Suspended Sediment Matter From
           MODIS Data in the Bohai Sea
    • Authors: Chen; J.;Cui, T.;Qiu, Z.;Lin, C.;
      Pages: 2611 - 2618
      Abstract: In this paper, as a case study in the Bohai Sea, a split-window model is established for retrieving total suspended sediment matter (TSM) from MODIS data. The split-window model is initialized using the MODIS-derived water-leaving reflectance and field-measured TSM concentration. Based on the results of the analyses, it is shown that the split-window model may be used for estimating TSM concentration in the Bohai Sea without requiring accurate atmospheric correction for the MODIS dataset, regardless of the fact that the model output would be influenced by the inherent noise at the MODIS near-infrared wavelengths in cases with low TSM concentration. Finally, the split-window model is used to produce TSM images from MODIS data obtained on September 22, 2005. As expected, the TSM concentration decreases from the coastal zone in all directions, with the sharpest decline in the direction of the Central Bohai Sea. These distribution characteristics of the TSM concentrations are caused primarily by wind waves, Stoke drifts, tidal currents, river discharge, and other factors.
      PubDate: June 2014
      Issue No: Vol. 7, No. 6 (2014)
  • Correction of the Water Column Attenuation: Application to the Seabed
           Mapping of the Lagoon of New Caledonia Using MERIS Images
    • Authors: Minghelli-Roman; A.;Dupouy, C.;
      Pages: 2619 - 2629
      Abstract: Features on the seabed can be mapped from remote sensing multi/hyperspectral imagery, provided that their effects on the measured reflectance spectrum can be made independent of those produced by the atmosphere and water column. The nonlinear effect of water column light attenuation can then be corrected to obtain the absolute reflectance of the seabed. Light attenuation by the water column and bathymetry are both determined from the satellite image. The water column attenuation is then removed in order to apply an automated supervised classification, whatever the depth is. We have compared the results obtained with and without the correction of water column attenuation, for two different statistical measures: Euclidean (ED) and spectral angle mapper (SAM) distances. We have applied this methodology to MERIS images acquired on the lagoon of New Caledonia. The best overall accuracy (79%), as compared to in situ data, is obtained with the corrected image and the SAM distance.
      PubDate: June 2014
      Issue No: Vol. 7, No. 6 (2014)
  • Spectral Calibration of Hyperspectral Data Observed From a
           Hyperspectrometer Loaded on an Unmanned Aerial Vehicle Platform
    • Authors: Liu; Y.;Wang, T.;Ma, L.;Wang, N.;
      Pages: 2630 - 2638
      Abstract: Hyperspectral imaging has been widely applied in remote sensing scientific fields. For this study, hyperspectral imaging data covering the spectral region from 400 to 1000 nm were collected from an unmanned aerial vehicle visible/near-infrared imaging hyperspectrometer (UAV-VNIRIS). Theoretically, the spectral calibration parameters of the UAV-VNIRIS measured in the laboratory should be refined when applied to the hyperspectral data obtained from the UAV platform due to variations between the laboratory and actual flight environments. Therefore, accurate spectral calibration of the UAV-VNIRIS is essential to further applications of the hyperspectral data. Shifts in both the spectral center wavelength position and the full-width at half-maximum (FWHM) were retrieved using two different methods (Methods I and II) based on spectrum matching of atmospheric absorption features at oxygen bands near 760 nm and water vapor bands near 820 and 940 nm. Comparison of the spectral calibration results of these two methods over the calibration targets showed that the derived center wavelength and FWHM shifts are similar. For the UAV-VNIRIS observed data used here, the shifts in center wavelength derived from both Methods I and II over the three absorption bands are less than 0.13 nm, and less than 0.22 nm in terms of FWHM. The findings of this paper revealed: 1) the UAV-VNIRIS payload on the UAV platform performed well in terms of spectral calibration; and 2) the applied methods are effective for on-orbit spectral calibration of the hyper spectrometer.
      PubDate: June 2014
      Issue No: Vol. 7, No. 6 (2014)
  • Analysis of the Proportion of Surface Reflected Radiance in Mid-Infrared
           Absorption Bands
    • Authors: Zhang; B.;Liu, Y.;Zhang, W.;Gao, L.;Li, J.;Wang, J.;Li, X.;
      Pages: 2639 - 2646
      Abstract: Image simulation of remote sensing systems is important for the development of new instruments and validations of data processing algorithms. In image simulation process, surface scene simulation is a fundamental issue and usually has the first priority. For two mid-infrared absorption bands near 2.7 µm and 4.3 µm, although there are a lot of applications in remote sensing field, relevant research on surface scene simulation is very limited. In these two mid-infrared ranges, surface radiance is a combination of reflected and emitted radiance. However, the radiance is generally reduced because of strong absorption by atmosphere. Therefore, analysis of surface reflected radiance is essential for simulation work. In this paper, we use a radiative transfer model MODTRAN to simulate proportions of surface reflected radiance for common ground materials under various observation conditions. The obtained results show that proportions of studied materials are 0.8%–99.8% in the band of 2.63–2.83 µm and 1.1%–94.8% in the band of 4.2–4.5 µm. The proportions of surface reflected radiance in both absorption bands are affected by surface reflectivity. In addition, in the band of 2.7 µm the proportion of surface reflected radiance is sensitive to solar geometry, water vapor content and surface temperature, whereas it is insensitive in the band of 4.3 µm. Based on these results, we conduct that both reflection and emission are important for surface scene simulations in the 2.7 µm and 4.3 µm absorption bands.
      PubDate: June 2014
      Issue No: Vol. 7, No. 6 (2014)
  • Hyperspectral Image Visualization Using Band Selection
    • Authors: Su; H.;Du, Q.;Du, P.;
      Pages: 2647 - 2658
      Abstract: Several simple but efficient hyperspectral image display approaches are proposed to use selected bands for Red-Green-Blue (RGB) color composite construction, where visualization-oriented spectral segmentation and integration are developed. A series of band selection algorithms, including minimum estimated abundance covariance (MEAC) and linear prediction (LP), are implemented and compared. The resulting color displays are evaluated in terms of class separability using a statistical classifier, and perceptual color distance. Experimental results demonstrate that the color composite displays using MEAC and LP-selected bands can outperform other band selection methods with low computational cost, and their performance is also better than those of one-bit transform (1BT) and principal component analysis (PCA)-based hyperspectral visualization methods in the literature.
      PubDate: June 2014
      Issue No: Vol. 7, No. 6 (2014)
  • Optimized Hyperspectral Band Selection Using Particle Swarm Optimization
    • Authors: Su; H.;Du, Q.;Chen, G.;Du, P.;
      Pages: 2659 - 2670
      Abstract: A particle swarm optimization (PSO)-based system is proposed to select bands and determine the optimal number of bands to be selected simultaneously, which is near-automatic with only a few data-independent parameters. The proposed system includes two particle swarms, i.e., the outer one for estimating the optimal number of bands and the inner one for the corresponding band selection. To avoid employing an actual classifier within PSO so as to greatly reduce computational cost, criterion functions that can gauge class separability are preferred; specifically, minimum estimated abundance covariance (MEAC) and Jeffreys–Matusita (JM) distance are adopted in this research. The experimental results show that the 2PSO-based algorithm outperforms the popular sequential forward selection (SFS) method and PSO with one particle swarm in band selection.
      PubDate: June 2014
      Issue No: Vol. 7, No. 6 (2014)
  • Hyperspectral Imagery Super-Resolution by Spatial–Spectral Joint
           Nonlocal Similarity
    • Authors: Zhao; Y.;Yang, J.;Chan, J.C.-W.;
      Pages: 2671 - 2679
      Abstract: Hyperspectral (HS) super-resolution reconstruction is an ill-posed inversion problem, for which the solution from reconstruction constraint is not unique. To address this, an HS image super-resolution method is proposed to first utilize the joint regulation of spatial and spectral nonlocal similarities. We then fused the HS and panchromatic images with sparse regulation. With these two regulation terms, edge sharpness and spectrum consistency are preserved and noises are suppressed. The proposed method is tested with Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) and Hyperion images and evaluated by quantitative measures. The resulting enhanced images from the proposed method are superior to the results obtained by other well-known methods.
      PubDate: June 2014
      Issue No: Vol. 7, No. 6 (2014)
  • Real-Time Identification of Hyperspectral Subspaces
    • Authors: Torti; E.;Acquistapace, M.;Danese, G.;Leporati, F.;Plaza, A.;
      Pages: 2680 - 2687
      Abstract: The identification of signal subspace is a crucial operation in hyperspectral imagery, enabling a correct dimensionality reduction that often yields gains in algorithm performance and efficiency. This paper presents new parallel implementations of a widely used hyperspectral subspace identification with minimum error (HySime) algorithm on different types of high-performance computing architectures, including general purpose multicore CPUs, graphics processing units (GPUs), and digital signal processors (DSPs). We first developed an optimized serial version of the HySime algorithm using the C programming language, and then we developed three parallel versions: one for a multi-core Intel CPU using the OpenMP API and the ATLAS algebra library, another one using NVIDIA’s compute unified device architecture (CUDA) and its basic linear algebra subroutines library (CuBLAS), and another one using a Texas’ multicore DSP. Experimental results, based on the processing of simulated and real hyperspectral images of various sizes, show the effectiveness of our GPU and multicore CPU implementations, which satisfy the real-time constraints given by the data acquisition rate. The DSP implementation offers a good tradeoff between low power consumption and computational performance, but it is still penalized by the absence of double precision floating point accuracy and/or suitable mathematical libraries.
      PubDate: June 2014
      Issue No: Vol. 7, No. 6 (2014)
  • Collaborative Graph-Based Discriminant Analysis for Hyperspectral Imagery
    • Authors: Ly; N.H.;Du, Q.;Fowler, J.E.;
      Pages: 2688 - 2696
      Abstract: In previous work, a sparse graph-based discriminant analysis was proposed for when labeled samples are available. Although an affinity-based graph itself may not necessarily enhance the disciminant power, the discriminant power can truly be improved when an affinity matrix is retrieved from labeled samples. Additionally, a sparsity-preserving graph has been demonstrated to be capable of providing performance superior to that of the commonly used ${mbi{k}}$ -nearest-neighbor graphs and other widely used dimensionality-reduction approaches in the literature. Deviating from the concept of sparse representation, a collaborative graph-based discriminant analysis is proposed, originating from collaborative representation among labeled samples whose solution can be nicely expressed in closed form. Experimental results demonstrate that the proposed collaborative approach can yield even better classification performance than the previous state-of-the-art sparsity-based approach with much lower computational cost.
      PubDate: June 2014
      Issue No: Vol. 7, No. 6 (2014)
  • A New Band Selection Method for Hyperspectral Image Based on Data Quality
    • Authors: Sun; K.;Geng, X.;Ji, L.;Lu, Y.;
      Pages: 2697 - 2703
      Abstract: Most unsupervised band selection methods take the information of bands into account, but few of them pay attention to the quality of bands. In this paper, by combining idea of noise-adjusted principal components (NAPCs) with a state-of-art band selection method [maximum determinant of covariance matrix (MDCM)], we define a new index ${mbi{Q}}$ to quantitatively measure the quality of the hyperspectral data cube. Both signal-to-noise ratios (SNRs) and correlation of bands are simultaneously considered in ${mbi{Q}}$ . Based on the new index defined in this article, we propose an unsupervised band selection method called minimum noise band selection (MNBS). Taking the quality ( ${mbi{Q}}$ ) of the data cube as selection criterion, MNBS tries to find the bands with both high SNRs and low correlation (high ${mbi{Q}}$ ). The subset selection method, sequential backward selection (SBS), is used in MNBS to improve the search efficiency. Some comparative experiments based on simulated as well as real hyperspectral data are conducted to evaluate the performance of MNBS in this study. The experimental results show that the bands selected by MNBS are always more effective than those selected by other methods in terms of classification.
      PubDate: June 2014
      Issue No: Vol. 7, No. 6 (2014)
  • Open Access
    • Pages: 2704 - 2704
      Abstract: Advertisement: This publication offers open access options for authors. IEEE open access publishing.
      PubDate: June 2014
      Issue No: Vol. 7, No. 6 (2014)
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