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  Subjects -> ELECTRONICS (Total: 152 journals)
Advances in Biosensors and Bioelectronics     Open Access   (Followers: 1)
Advances in Electronics     Open Access   (Followers: 3)
Advances in Magnetic and Optical Resonance     Full-text available via subscription   (Followers: 5)
Advances in Microelectronic Engineering     Open Access   (Followers: 2)
Advances in Power Electronics     Open Access   (Followers: 7)
Aerospace and Electronic Systems, IEEE Transactions on     Hybrid Journal   (Followers: 93)
American Journal of Electrical and Electronic Engineering     Open Access   (Followers: 12)
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: 6)
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: 9)
Biomedical Engineering, IEEE Reviews in     Full-text available via subscription   (Followers: 16)
Biomedical Engineering, IEEE Transactions on     Hybrid Journal   (Followers: 15)
Biomedical Instrumentation & Technology     Hybrid Journal   (Followers: 5)
Broadcasting, IEEE Transactions on     Hybrid Journal   (Followers: 5)
BULLETIN of National Technical University of Ukraine. Series RADIOTECHNIQUE. RADIOAPPARATUS BUILDING     Open Access   (Followers: 2)
Bulletin of the Polish Academy of Sciences : Technical Sciences     Open Access  
Canadian Journal of Remote Sensing     Full-text available via subscription   (Followers: 13)
China Communications     Full-text available via subscription   (Followers: 4)
Circuits and Systems     Open Access   (Followers: 9)
Consumer Electronics Times     Open Access   (Followers: 4)
Control Systems     Hybrid Journal   (Followers: 25)
Electronic Design     Partially Free  
Electronic Markets     Hybrid Journal   (Followers: 5)
Electronic Materials Letters     Hybrid Journal   (Followers: 3)
Electronics     Open Access   (Followers: 9)
Electronics and Communications in Japan     Hybrid Journal   (Followers: 5)
Electronics Letters     Hybrid Journal   (Followers: 19)
Embedded Systems Letters, IEEE     Hybrid Journal   (Followers: 23)
Energy Harvesting and Systems : Materials, Mechanisms, Circuits and Storage     Hybrid Journal   (Followers: 1)
EPJ Quantum Technology     Open Access  
EURASIP Journal on Embedded Systems     Open Access   (Followers: 9)
Facta Universitatis, Series : Electronics and Energetics     Open Access  
Frequenz     Hybrid Journal   (Followers: 3)
Frontiers of Optoelectronics     Hybrid Journal   (Followers: 2)
Geoscience and Remote Sensing, IEEE Transactions on     Hybrid Journal   (Followers: 21)
Haptics, IEEE Transactions on     Hybrid Journal   (Followers: 3)
IEEE Antennas and Propagation Magazine     Hybrid Journal   (Followers: 15)
IEEE Antennas and Wireless Propagation Letters     Hybrid Journal   (Followers: 13)
IEEE Consumer Electronics Magazine     Full-text available via subscription   (Followers: 17)
IEEE Journal of Emerging and Selected Topics in Power Electronics     Hybrid Journal   (Followers: 12)
IEEE Journal of the Electron Devices Society     Open Access   (Followers: 3)
IEEE Power Electronics Magazine     Full-text available via subscription   (Followers: 6)
IEEE Transactions on Antennas and Propagation     Full-text available via subscription   (Followers: 10)
IEEE Transactions on Audio, Speech, and Language Processing     Hybrid Journal   (Followers: 13)
IEEE Transactions on Automatic Control     Hybrid Journal   (Followers: 26)
IEEE Transactions on Consumer Electronics     Hybrid Journal   (Followers: 17)
IEEE Transactions on Electron Devices     Hybrid Journal   (Followers: 8)
IEEE Transactions on Information Theory     Hybrid Journal   (Followers: 14)
IEEE Transactions on Power Electronics     Hybrid Journal   (Followers: 20)
IEICE - Transactions on Electronics     Full-text available via subscription   (Followers: 8)
IEICE - Transactions on Information and Systems     Full-text available via subscription   (Followers: 7)
IET Microwaves, Antennas & Propagation     Hybrid Journal   (Followers: 6)
IET Power Electronics     Hybrid Journal   (Followers: 13)
IET Wireless Sensor Systems     Hybrid Journal   (Followers: 10)
IETE Journal of Education     Open Access   (Followers: 2)
IETE Journal of Research     Open Access   (Followers: 9)
IETE Technical Review     Open Access   (Followers: 4)
Industrial Electronics, IEEE Transactions on     Hybrid Journal   (Followers: 12)
Industry Applications, IEEE Transactions on     Hybrid Journal   (Followers: 3)
Informatik-Spektrum     Hybrid Journal  
Instabilities in Silicon Devices     Full-text available via subscription  
Intelligent Transportation Systems Magazine, IEEE     Full-text available via subscription   (Followers: 2)
International Journal of Advanced Electronics and Communication Systems     Open Access   (Followers: 5)
International Journal of Advanced Research in Computer Science and Electronics Engineering     Open Access   (Followers: 20)
International Journal of Advances in Telecommunications, Electrotechnics, Signals and Systems     Open Access   (Followers: 3)
International Journal of Aerospace Innovations     Full-text available via subscription   (Followers: 12)
International Journal of Antennas and Propagation     Open Access   (Followers: 7)
International Journal of Applied Electronics in Physics & Robotics     Open Access   (Followers: 1)
International Journal of Biomedical Nanoscience and Nanotechnology     Hybrid Journal   (Followers: 6)
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: 13)
International Journal of Electronics     Hybrid Journal   (Followers: 2)
International Journal of Electronics & Data Communication     Open Access   (Followers: 4)
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: 5)
International Journal of Nanoscience     Hybrid Journal  
International Journal of Numerical Modelling:Electronic Networks, Devices and Fields     Hybrid Journal   (Followers: 2)
International Journal of Power Electronics     Hybrid Journal   (Followers: 8)
International Journal of Review in Electronics & Communication Engineering     Open Access   (Followers: 2)
International Journal of Sensors, Wireless Communications and Control     Hybrid Journal   (Followers: 2)
International Journal of Superconductivity     Open Access  
International Journal of Systems, Control and Communications     Hybrid Journal   (Followers: 2)
International Journal on Communication     Full-text available via subscription   (Followers: 9)
International Journal on Electrical and Power Engineering     Full-text available via subscription   (Followers: 10)
International Transaction of Electrical and Computer Engineers System     Open Access  
Journal of Biosensors & Bioelectronics     Open Access   (Followers: 2)
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 Computational Intelligence and Electronic Systems     Full-text available via subscription  
Journal of Electrical and Electronics Engineering Research     Open Access   (Followers: 4)
Journal of Electrical Bioimpedance     Full-text available via subscription   (Followers: 2)
Journal of Electrical Engineering & Electronic Technology     Hybrid Journal   (Followers: 2)

        1 2 | Last

Journal Cover   Selected Topics in Applied Earth Observations and Remote Sensing, IEEE Journal of
  [SJR: 1.632]   [H-I: 19]   [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  [176 journals]
  • IEEE Journal of Selected Topics in Applied Earth Observations and Remote
           Sensing Information for Authors
    • Abstract: Provides instructions and guidelines to prospective authors who wish to submit manuscripts.
      PubDate: May 2015
      Issue No: Vol. 8, No. 5 (2015)
  • IEEE Transactions on Geoscience and Remote Sensing institutional listings
    • Abstract: Advertisement.
      PubDate: May 2015
      Issue No: Vol. 8, No. 5 (2015)
  • IEEE Journal of Selected Topics in Applied Earth Observations and Remote
           Sensing publication information
    • Abstract: Provides a listing of current staff, committee members and society officers.
      PubDate: May 2015
      Issue No: Vol. 8, No. 5 (2015)
  • Front cover
    • Abstract: Presents the front cover for this issue of the publication.
      PubDate: May 2015
      Issue No: Vol. 8, No. 5 (2015)
  • Table of contents
    • Pages: 1869 - 1871
      Abstract: Presents the table of contents for this issue of this publication.
      PubDate: May 2015
      Issue No: Vol. 8, No. 5 (2015)
  • Foreword to the Special Issue on Information Extraction From
           High-Spatial-Resolution Optical Remotely Sensed Imagery
    • Authors: Huang; X.;Fauvel, M.;Dalla Mura, M.;Zhang, L.;
      Pages: 1872 - 1875
      Abstract: The articles in this special section focus on information extraction from high-spatial-resolution optical remotely sensed imagery technology and applications.
      PubDate: May 2015
      Issue No: Vol. 8, No. 5 (2015)
  • A Simplified Empirical Line Method of Radiometric Calibration for Small
           Unmanned Aircraft Systems-Based Remote Sensing
    • Authors: Wang; C.;Myint, S.W.;
      Pages: 1876 - 1885
      Abstract: The use of small unmanned aircraft systems (sUAS) to acquire very high-resolution multispectral imagery has attracted growing attention recently; however, no systematic, feasible, and convenient radiometric calibration method has been specifically developed for sUAS remote sensing. In this research, we used a modified color infrared (CIR) digital single-lens reflex (DSLR) camera as the sensor and the DJI S800 hexacopter sUAS as the platform to collect imagery. Results show that the relationship between the natural logarithm of measured surface reflectance and image raw, unprocessed digital numbers (DNs) is linear and the ${bm{y}}$ -intercept of the linear equation can be theoretically interpreted as the minimal possible surface reflectance that can be detected by each sensor waveband. The empirical line calibration equation for every single band image can be built using the ${bm{y}}$ -intercept as one data point, and the natural log-transformed measured reflectance and image DNs of a gray calibration target as another point in the coordinate system. Image raw DNs are therefore converted to reflectance using the calibration equation. The Mann–Whitney ${bm{U}}$ test results suggest that the difference between the measured and the predicted reflectance values of 13 tallgrass sampling quadrats is not statistically significant. The method theory developed in this study can be employed for other sUAS-based remote sensing applications.
      PubDate: May 2015
      Issue No: Vol. 8, No. 5 (2015)
  • Evaluation of Atmospheric Correction Methods in Identifying Urban Tree
           Species With WorldView-2 Imagery
    • Authors: Pu; R.;Landry, S.;Zhang, J.;
      Pages: 1886 - 1897
      Abstract: The radiance recorded at a sensor is not fully a representative of Earth surface section features but is altered by atmosphere. In this study, we evaluated three atmospheric correction (AC) methods (a typical empirical modeling method, a radiative transfer modeling approach, and a combination of the both methods) in identifying urban tree species/groups with high-resolution WorldView-2 (WV2) imagery in the City of Tampa, FL, USA. We tested whether AC methods were necessary in urban tree species discrimination. In situ spectral measurements were taken from tops of tree canopy and tree crowns were delineated from WV2 imagery. Two-sample $bm{t}$ -tests, repeated measures ANOVA (RANOVA) tests, linear discriminant analysis (LDA), and classification and regression trees (CART) classifiers were used to test the spectral difference between in situ spectra and atmospherically corrected image spectra and to discriminate urban tree species/groups. The experimental results demonstrate that 1) the empirical line-based AC methods were relatively more effective than a radiative transfer-based AC model to atmospherically correct the image data, due to lacking accurate and reliable atmospheric parameters to run the radiative transfer model and 2) the AC processing to WV2 imagery was unnecessary in identifying seven tree species/groups in this particular case, most likely because the WV2 image data used in this analysis were acquired on a single date and covered a relatively small area ( ${303};mathbf{km}^{2}$ ). The study results also indicate that compared with a nonparametric classifier CART, the parametric classifier LDA produced higher overall accuracy (55% vs. 48%) for identifying the seven species/groups.
      PubDate: May 2015
      Issue No: Vol. 8, No. 5 (2015)
  • Smart Information Reconstruction via Time-Space-Spectrum Continuum for
           Cloud Removal in Satellite Images
    • Authors: Chang; N.;Bai, K.;Chen, C.;
      Pages: 1898 - 1912
      Abstract: Cloud contamination is a big obstacle when processing satellite images retrieved from visible and infrared spectral ranges for application. Although computational techniques including interpolation and substitution have been applied to recover missing information caused by cloud contamination, these algorithms are subject to many limitations. In this paper, a novel smart information reconstruction (SMIR) method is proposed, in order to reconstruct cloud contaminated pixel values from the time-space-spectrum continuum with the aid of a machine learning tool, namely extreme learning machine (ELM). For the purpose of demonstration, the performance of SMIR is evaluated by reconstructing the missing remote sensing reflectance values derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) on board the Terra satellite over Lake Nicaragua, where is a very cloudy area year round. For comparison, the traditional backpropagation neural network algorithms will also be implemented to reconstruct the missing values. Experimental results show that the ELM outperforms the BP algorithms by an enhanced machine learning capacity with simulated memory effect embedded in MODIS due to linking the complex time-space-spectrum continuum between cloud-free and cloudy pixels. The ELM-based SMIR practice presents a correlation coefficient of 0.88 with root mean squared error of $7.4{hbox{E}} - 04{hbox{sr}}^{-1}$ between simulated and observed reflectance values. Finding suggests that the SMIR method is effective to reconstruct all the missing information providing visually logical and quantitatively assured images for further image processing and interpretation in environmental applications.
      PubDate: May 2015
      Issue No: Vol. 8, No. 5 (2015)
  • Development of Line-of-Sight Digital Surface Model for Co-Registering
           Off-Nadir VHR Satellite Imagery With Elevation Data
    • Authors: Suliman; A.;Zhang, Y.;
      Pages: 1913 - 1923
      Abstract: Co-registration of very-high-resolution (VHR) images with elevation data is extremely important for many remote sensing applications due to the complementary properties of these two data types. However, this type of multidata source registration has many associated challenges. For instance, although VHR satellite images are usually acquired off-nadir, the integration of off-nadir images with digital surface models (DSMs) for the purpose of urban mapping has been rarely seen in research publications. This is due to the relief displacement of the elevated objects, which causes a problematic misregistration between the perspective off-nadir images and the corresponding orthographic DSMs. Therefore, the co-registration of such datasets is almost impossible unless a true orthorectification process is executed. However, true orthoimages are expensive, time consuming, and difficult to achieve. Thus, this paper proposes a registration method based on developing a line-of-sight DSM solution to effectively register elevation data with off-nadir VHR images. The method utilizes the relevant sensor model in two phases: deriving DSM from stereo images and reprojecting the DSM back to one of the stereo images to generate a line-of-sight DSM for accurate co-registration. To demonstrate the applicability of the proposed method and evaluate the effect of the misregistration, a building detection procedure is implemented. The proposed method is found to be feasible, inexpensive, and of subpixel accuracy. Additionally, it improves the overall accuracy of detecting buildings by almost 12% relative to that when the conventional two-dimensional (2-D) registration technique is used solely due to the elimination of the misregistration effect.
      PubDate: May 2015
      Issue No: Vol. 8, No. 5 (2015)
  • Semantic Segmentation of Remote Sensing Imagery Using Object-Based Markov
           Random Field Model With Regional Penalties
    • Authors: Zheng; C.;Wang, L.;
      Pages: 1924 - 1935
      Abstract: This paper proposes a novel object-based Markov random field model (OMRF) for semantic segmentation of remote sensing images. First, the method employs the region size and edge information to build a weighted region adjacency graph (WRAG) for capturing the complicated interactions among objects. Thereafter, aimed at modeling object interactions in the OMRF, the size and edge information are further introduced into the Gibbs joint distribution of the random field as regional penalties. Finally, the semantic segmentation is achieved through a principled probabilistic inference of the OMRF with regional penalties. The proposed method is compared with other MRF-based methods and some state-of-the-art methods. Experiments are conducted on a series of synthetic and real-world images. Segmentation results demonstrate that our method provides better performance (an accuracy improvement about 3%). Moreover, we further discuss the application of the proposed method for classification.
      PubDate: May 2015
      Issue No: Vol. 8, No. 5 (2015)
  • Unsupervised Quantification of Under- and Over-Segmentation for
           Object-Based Remote Sensing Image Analysis
    • Authors: Troya-Galvis; A.;Gancarski, P.;Passat, N.;Berti-Equille, L.;
      Pages: 1936 - 1945
      Abstract: Object-based image analysis (OBIA) has been widely adopted as a common paradigm to deal with very high-resolution remote sensing images. Nevertheless, OBIA methods strongly depend on the results of image segmentation. Many segmentation quality metrics have been proposed. Supervised metrics give accurate quality estimation but require a ground-truth segmentation as reference. Unsupervised metrics only make use of intrinsic image and segment properties; yet most of them strongly depend on the application and do not deal well with the variability of objects in remote sensing images. Furthermore, the few metrics developed in a remote sensing context mainly focus on global evaluation. In this paper, we propose a novel unsupervised metric, which evaluates local quality (per segment) by analyzing segment neighborhood, thus quantifying under- and over-segmentation given a certain homogeneity criterion. Additionally, we propose two variants of this metric, for estimating global quality of remote sensing image segmentation by the aggregation of local quality scores. Finally, we analyze the behavior of the proposed metrics and validate their applicability for finding segmentation results having good tradeoff between both kinds of errors.
      PubDate: May 2015
      Issue No: Vol. 8, No. 5 (2015)
  • Constructing Hierarchical Segmentation Tree for Feature Extraction and
           Land Cover Classification of High Resolution MS Imagery
    • Authors: Wang; L.;Dai, Q.;Xu, Q.;Zhang, Y.;
      Pages: 1946 - 1961
      Abstract: Accurate interpretation of high spatial resolution multispectral (MS) imagery relies on the extraction and fusion of information obtained from both spectral and spatial domains. Feature extraction from one or several fixed windows uses inaccurate description of pixel contexts and produces blurred object boundaries and low classification accuracy. In order to accurately characterize the spatial context properties of pixels, this paper presents a hierarchical-segmentation-based classification system. The system consists of two main modules: 1) hierarchical segmentation and 2) context-based classification. The segmentation module involves an optimization procedure to prevent undersegmentation of the land objects of interest and a scale selection procedure to find the most representative segmentation layers for modeling pixel contexts. The classification module couples a context-driven multilevel feature extraction methodology with a support vector machine classifier to get classification result. The proposed system is validated on three high spatial resolution MS data sets. Compared with state-of-the-art classification methods based on the similar concept, the proposed method demonstrates superior performance on both the classification accuracy and the quality of classification maps.
      PubDate: May 2015
      Issue No: Vol. 8, No. 5 (2015)
  • Pointwise Graph-Based Local Texture Characterization for Very High
           Resolution Multispectral Image Classification
    • Authors: Pham; M.;Mercier, G.;Michel, J.;
      Pages: 1962 - 1973
      Abstract: A new method for local texture characterization in very high resolution (VHR) multispectral imagery is proposed based on a pointwise approach embedded into a graph model. Due to the fact that increasing the spatial resolution of satellite sensors leads to the lack of stationarity hypothesis in optical images, a pointwise approach based on a set of interest pixels only, not on the whole image pixels, seems to be relevant. Beside that no stationary condition is required, this approach could also provide the ability to deal with huge-size data as in case of VHR multispectral images. In this paper, our motivation is to exploit the radiometric, spectral as well as spatial information of characteristic pixels to describe textural features from a multispectral image. Then, a weighted graph is constructed to link these feature points based on the similarity between their previous pointwise-based descriptors. Finally, textural features can be characterized and extracted from the spectral domain of this graph. In order to evaluate the performance of the proposed method, a texture-based classification algorithm is implemented. Here, we propose to investigate both the spectral graph clustering and the spectral graph wavelet transform approaches for an unsupervised classification. Experimental results show the effectiveness of our method in terms of classification precision as well as low complexity requirement.
      PubDate: May 2015
      Issue No: Vol. 8, No. 5 (2015)
  • A Mean Shift Vector-Based Shape Feature for Classification of High Spatial
           Resolution Remotely Sensed Imagery
    • Authors: Qin; R.;
      Pages: 1974 - 1985
      Abstract: The development of very high spatial resolution remote sensing sensors opens a new era for mapping the earth with submeter level of detail, whereas the increased resolution brings about difficulties for the land-cover classification in terms of intra-class variability and inter-class similarity. This paper presents a novel spatial feature, mean shift (MS) vector-based shape feature (MSVSF), to improve the classification accuracy of very high resolution (VHR) remote sensing imagery. MSVSF is a ${bf 3} times {bf 1}$ feature vector extracted in per-pixel fashion. It describes the shape of a spectrally homogeneous area surrounding each pixel by measuring the two-dimensional (2-D) image deformation of its local area imposed by the MS vector. The proposed feature is particularly effective to discriminate objects with similar spectral response but different 2-D shapes, such as buildings and roads. Independent component analysis is adopted to extract spectral features and Support Vector Machine (SVM) classifier is adopted to classify the spectral and spatial features and several state-of-the-art spatial/structural features are compared to the proposed feature. A synthetic experiment demonstrates that the proposed feature has good capability to describe 2-D shapes with different scale, two real dataset experiments on QuickBird and IKONOS images show MSVSF has achieved better overall accuracy (OA) than the compared ones. In addition, the MSVSF feature is extended to the object-based classification (OBC), and the result shows that the MSVSF is effective to improve the classification accuracy on high resolution images of the urban area.
      PubDate: May 2015
      Issue No: Vol. 8, No. 5 (2015)
  • Image Enhancement and Feature Extraction Based on Low-Resolution Satellite
    • Authors: Syrris; V.;Ferri, S.;Ehrlich, D.;Pesaresi, M.;
      Pages: 1986 - 1995
      Abstract: The purpose of this study is to investigate the sensitivity of contrast-based textural measurements and morphological characteristics that derive from high-resolution satellite imagery (three-band SPOT-5) when diverse image enhancements techniques are piloted. The general framework of the application is the built-up/nonbuilt-up detection. In the existence of a low-resolution reference layer, we apply supervised learning that indirectly reduces the uncertainty and improves the quality of the reference layer. Based on the new class label assignments, the image histogram is adjusted suitably for the computation of contrast-based textural/morphological features. A case study is presented where we test a mixture of image enhancement operations like linear and decorrelation stretching and assess the performance through ROC analysis against available building footprints. Experimental results demonstrate that spectral band combination is the key factor that conditions the contrast of grayscale images. Contrast adjustment (before or after the band combination and merging) supports considerably the extraction of informative features from a low-contrast image; in case of a well-contrasted image, the improvement is marginal.
      PubDate: May 2015
      Issue No: Vol. 8, No. 5 (2015)
  • An Approximate Spectral Clustering Ensemble for High Spatial Resolution
           Remote-Sensing Images
    • Authors: Tasdemir; K.;Moazzen, Y.;Yildirim, I.;
      Pages: 1996 - 2004
      Abstract: Unsupervised clustering of high spatial resolution remote-sensing images plays a significant role in detailed land-cover identification, especially for agricultural and environmental monitoring. A recently promising method is approximate spectral clustering (SC) which enables spectral partitioning for large datasets to extract clusters with distinct characteristics without a parametric model. It also facilitates the use of various information types via advanced similarity criteria. However, it requires an empirical selection of a similarity criterion optimal for the corresponding application. To address this challenge, we propose an approximate SC ensemble (ASCE2) which fuses partitionings obtained by different similarity representations. Contrary to existing spectral ensembles for remote-sensing applications, the proposed ASCE2 employs neural gas quantization instead of random sampling, advanced similarity criteria instead of traditional distance-based Gaussian kernel with different decay parameters, and a two-level ensemble. We evaluate the proposed ASCE2 with three measures (accuracy, adjusted Rand index, and normalized mutual information) using five remote-sensing images, two of which are commonly available. We apply the ASCE2 in two applications for agricultural monitoring: 1) land-cover identification to determine orchard fields using a WorldView-2 image (0.5-m spatial resolution) and 2) finding lands in good agricultural condition using multitemporal RapidEye images (5-m spatial resolution). Experimental results indicate a significant betterment of the resulting partitionings obtained by the proposed ensemble, with respect to the evaluation measures in these applications.
      PubDate: May 2015
      Issue No: Vol. 8, No. 5 (2015)
  • Semantic Classification of Heterogeneous Urban Scenes Using Intrascene
           Feature Similarity and Interscene Semantic Dependency
    • Authors: Zhang; X.;Du, S.;Wang, Y.;
      Pages: 2005 - 2014
      Abstract: Semantic classification of urban scenes aims to classify scenes composed of many different types of objects into predefined semantic classes. To learn the association between urban scenes and semantic classes, five tasks are needed: 1) segmenting the image into scenes; 2) establishing semantic classes of scenes; 3) extracting and transforming features; 4) measuring the intrascenes feature similarity; and 5) labeling each scene by a semantic classification method. Despite many efforts on these tasks, most existing works consider only visual features with inconsistent similarity measurement, while ignore semantic features inside scenes and the interactions between scenes, leading to poor classification results for high heterogeneous scenes. To solve these problems, this study combines intrascene feature similarity and interscene semantic dependency to form a two-step classification approach. For the first step, visual and semantic features are first optimized to be invariant to affine transformation, and then are employed in K-Nearest Neighbor to initially classify scenes. For the second step, multinomial distribution is presented to model both the spatial and semantic dependency between scenes, and then used to improve the initial classification results. The implementations conducted in two study areas indicate that the proposed approach produces better results for heterogeneous scenes than visual interpretation, as it can discover and model the hidden information between scenes which is often ignored by existing methods. In addition, compared with the initial classification, the optimized step improves accuracies by 3.6% and 5% in the two study areas, respectively.
      PubDate: May 2015
      Issue No: Vol. 8, No. 5 (2015)
  • Unsupervised Feature Learning Via Spectral Clustering of Multidimensional
           Patches for Remotely Sensed Scene Classification
    • Authors: Hu; F.;Xia, G.;Wang, Z.;Huang, X.;Zhang, L.;Sun, H.;
      Pages: 2015 - 2030
      Abstract: Scene classification plays an important role in the interpretation of remotely sensed high-resolution imagery. However, the performance of scene classification strongly relies on the discriminative power of feature representation, which is generally hand-engineered and requires a huge amount of domain-expert knowledge as well as time-consuming hand tuning. Recently, unsupervised feature learning (UFL) provides an alternative way to automatically learn discriminative feature representation from images. However, the performances achieved by conventional UFL methods are not comparable to the state-of-the-art, mainly due to the neglect of locally substantial image structures. This paper presents an improved UFL algorithm based on spectral clustering, named UFL-SC, which cannot only adaptively learn good local feature representations but also discover intrinsic structures of local image patches. In contrast to the standard UFL pipeline, UFL-SC first maps the original image patches into a low-dimensional and intrinsic feature space by linear manifold analysis techniques, and then learns a dictionary (e.g., using K-means clustering) on the patch manifold for feature encoding. To generate a feature representation for each local patch, an explicit parameterized feature encoding method, i.e., triangle encoding, is applied with the learned dictionary on the same patch manifold. The holistic feature representation of image scenes is finally obtained by building a bag-of-visual-words (BOW) model of the encoded local features. Experiments demonstrate that the proposed UFL-SC algorithm can extract efficient local features for image scenes and show comparable performance to the state-of-the-art approach on open scene classification benchmark.
      PubDate: May 2015
      Issue No: Vol. 8, No. 5 (2015)
  • Super-Resolution Land Cover Mapping Based on Multiscale Spatial
    • Authors: Hu; J.;Ge, Y.;Chen, Y.;Li, D.;
      Pages: 2031 - 2039
      Abstract: Super-resolution mapping (SRM) is a method for allocating land cover classes at a fine scale according to coarse fraction images. Based on a spatial regularization framework, this paper proposes a new regularization method for SRM that integrates multiscale spatial information from the fine scale as a smooth term and from the coarse scale as a penalty term. The smooth term is considered a homogeneity constraint, and the penalty term is used to characterize the heterogeneity constraint. Specifically, the smooth term depends on the local fine scale spatial consistency, and is used to smooth edges and eliminate speckle points. The penalty term depends on the coarse scale local spatial differences, and suppresses the over-smoothing effect from the fine scale information while preserving more details (e.g., connectivity and aggregation of linear land cover patterns). We validated our method using simulated and synthetic images, and compared the results to four representative SRM algorithms. Our numerical experiments demonstrated that the proposed method can produce more accurate maps, reduce differences in the number of patches, visually preserve smoother edges and more details, reject speckle points, and suppress over-smoothing.
      PubDate: May 2015
      Issue No: Vol. 8, No. 5 (2015)
  • Hybrid Constraints of Pure and Mixed Pixels for Soft-Then-Hard
           Super-Resolution Mapping With Multiple Shifted Images
    • Authors: Chen; Y.;Ge, Y.;Heuvelink, G.B.M.;Hu, J.;Jiang, Y.;
      Pages: 2040 - 2052
      Abstract: Multiple shifted images (MSIs) have been widely applied to many super-resolution mapping (SRM) approaches to improve the accuracy of fine-scale land-cover maps. Most SRM methods with MSIs involve two processes: subpixel sharpening and class allocation. Complementary information from the MSIs has been successfully adopted to produce soft attribute values of subpixels during the subpixel sharpening process. Such information, however, is not used in the second process of class allocation. In this paper, a new class-allocation algorithm, named “hybrid constraints of pure and mixed pixels” (HCPMP), is proposed to allocate land-cover classes to subpixels using MSIs. HCPMP first determines the classes of subpixels that overlap with the pure pixels of auxiliary images in MSIs, after which the remaining subpixels are classified using information derived from the mixed pixels of the base image in MSIs. An artificial image and two remote sensing images were used to evaluate the performance of the proposed HCPMP algorithm. The experimental results demonstrate that HCPMP successfully applied MSIs to produce SRM maps that are visually closer to the reference images and that have greater accuracy than five existing class-allocation algorithms. Especially, it can produce more accurate SRM maps for high-resolution land-cover classes than low-resolution cases. The algorithm takes slightly less runtime than class allocation using linear optimization techniques. Hence, HCPMP provides a valuable new solution for class allocation in SRM using auxiliary data from MSIs.
      PubDate: May 2015
      Issue No: Vol. 8, No. 5 (2015)
  • Object Detection Based on Sparse Representation and Hough Voting for
           Optical Remote Sensing Imagery
    • Authors: Yokoya; N.;Iwasaki, A.;
      Pages: 2053 - 2062
      Abstract: We present a novel method for detecting instances of an object class or specific object in high-spatial-resolution optical remote sensing images. The proposed method integrates sparse representations for local-feature detection into generalized-Hough-transform object detection. Object parts are detected via class-specific sparse image representations of patches using learned target and background dictionaries, and their co-occurrence is spatially integrated by Hough voting, which enables object detection. We aim to efficiently detect target objects using a small set of positive training samples by matching essential object parts with a target dictionary while the residuals are explained by a background dictionary. Experimental results show that the proposed method achieves state-of-the-art performance for several examples including object-class detection and specific-object identification.
      PubDate: May 2015
      Issue No: Vol. 8, No. 5 (2015)
  • Shadow-Based Rooftop Segmentation in Visible Band Images
    • Authors: Femiani; J.;Li, E.;Razdan, A.;Wonka, P.;
      Pages: 2063 - 2077
      Abstract: This paper presents a method to extract rooftops from aerial images with only visible red, green, and blue bands of data. In particular, it does not require near-infrared data, lidar, or multiple viewpoints. The proposed method uses shadows in the image in order to detect buildings and to determine a set of constraints on which parts can or cannot be rooftops. We then use the grabcut algorithm to identify complete rooftop regions and a method to make corrections that simulate a user performing interactive image segmentation in order to improve the precision of our results. The precision, recall, and F-score of the proposed approach show significant improvement over two very recently published papers. On our test dataset, we observe an average F-score of 89% compared to scores of 68% and 33%.
      PubDate: May 2015
      Issue No: Vol. 8, No. 5 (2015)
  • Cauchy Graph Embedding Optimization for Built-Up Areas Detection From
           High-Resolution Remote Sensing Images
    • Authors: Li; Y.;Tan, Y.;Deng, J.;Wen, Q.;Tian, J.;
      Pages: 2078 - 2096
      Abstract: Automatic built-up areas detection from remote sensing images has attracted considerable research interest, due to its crucial roles in various applications. As far as built-up areas detection, the corner density map to predict the presence of the built-up areas has been widely adopted, but the calculation is generally time-consuming. In addition, the density map is just segmented by a statistical threshold, resulting in that the accurate boundaries of the built-up areas are unachievable. In order to address these issues, this paper proposes a novel built-up areas detection approach. Instead of pixel units, our approach takes the superpixel-based image partitions as the primary calculation units, which benefits to improve the computational efficiency and visual organization performance. Based on the superpixel-based units, this paper first proposes a sparse corner voting method for accelerating the production of corner density map. Then, Cauchy graph embedding optimization is presented to cope with the problem of segmenting the density map, which can preserve the well-defined boundaries of built-up areas. A diverse and representative test set including 2.1-m resolution ZY3 imagery, 2.0-m resolution GF1 imagery, 1.0-m resolution IKONOS imagery, and 0.61-m resolution QUICKBIRD imagery is collected. Experimental results on these test images show that our proposed approach is robust to sensor and resolution variation, and can outperform state-of-the-art approaches remarkably.
      PubDate: May 2015
      Issue No: Vol. 8, No. 5 (2015)
  • Combining Pixel- and Object-Based Machine Learning for Identification of
           Water-Body Types From Urban High-Resolution Remote-Sensing Imagery
    • Authors: Huang; X.;Xie, C.;Fang, X.;Zhang, L.;
      Pages: 2097 - 2110
      Abstract: Water is one of the vital components for the ecological environment, which plays an important role in human survival and socioeconomic development. Water resources in urban areas are gradually decreasing due to the rapid urbanization, especially in developing countries. Therefore, the precise extraction and automatic identification of water bodies are of great significance and urgently required for urban planning. It should be noted that although some studies have been reported regarding the water-area extraction, to our knowledge, few papers concern the identification of urban water types (e.g., rivers, lakes, canals, and ponds). In this paper, a novel two-level machine-learning framework is proposed for identifying the water types from urban high-resolution remote-sensing images. The framework consists of two interpretation levels: 1) water bodies are extracted at the pixel level, where the water/shadow/vegetation indexes are considered and 2) water types are further identified at the object level, where a set of geometrical and textural features are used. Both levels employ machine learning for the image interpretation. The proposed framework is validated using the GeoEye-1 and WorldView-2 images, over two mega cities in China, i.e., Wuhan and Shenzhen, respectively. The experimental results show that the proposed method achieved satisfactory accuracies for both water extraction [95.4% (Shenzhen), 96.2% (Wuhan)], and water type classification [94.1% (Shenzhen), 95.9% (Wuhan)] in complex urban areas.
      PubDate: May 2015
      Issue No: Vol. 8, No. 5 (2015)
  • Region-of-Interest Extraction Based on Saliency Analysis of Co-Occurrence
           Histogram in High Spatial Resolution Remote Sensing Images
    • Authors: Zhang; L.;Li, A.;
      Pages: 2111 - 2124
      Abstract: The extraction of a region of interest is an important component of remote sensing image analyses. Driven by practical applications, a good region of interest (ROI) needs to have three properties: uniformly highlighting entire ROIs, well-defined boundaries, and good stability against noisy data. Motivated by these requirements, we propose a ROI extraction model based on saliency analysis of co-occurrence histogram (SACH) in high spatial resolution remote sensing images. First, a co-occurrence histogram is utilized to capture the global and local distribution of intensity values. Secondly, our model estimates the saliency of the co-occurrence histogram by utilizing a logarithm function. Thirdly, a saliency-enhanced method based on moving K-means aggregation is utilized to establish well-defined boundary for ROIs and improve immunity to noise. Finally, ROIs are segmented from the saliency maps of original images, which are acquired from the saliency of the co-occurrence histogram. In the experimental part, we compare our model with nine other extraction models by applying the models to clean images and to images corrupted by noises. The experimental results show that compared to the nine competing models, SACH model better defines the boundaries of target ROIs and gets more entire ROIs. Furthermore, SACH model is also robust against images corrupted by Gaussian and Salt and Pepper noises.
      PubDate: May 2015
      Issue No: Vol. 8, No. 5 (2015)
  • Object-Based 3-D Building Change Detection on Multitemporal Stereo Images
    • Authors: Qin; R.;Huang, X.;Gruen, A.;Schmitt, G.;
      Pages: 2125 - 2137
      Abstract: Due to the rapid process of urbanization, there is an increasing demand for detecting building changes over time using very high-resolution (VHR) images. Traditional two-dimensional (2-D) change detection methods are limited due to the image perspective variation and illumination discrepancies. One current trend for building detection combines the use of orthophotos and digital surface models (DSMs), because of its robustness against false changes, as well as its capability of providing volumetric information. In this paper, we propose an object-based three-dimensional (3-D) building change detection framework based on supervised classification, which makes use of the height, spectral, and shape information in a combined fashion with object-based analysis. The proposed method follows the following steps: First, a synergic mean-shift segmentation method is applied on the orthophoto with the constraints of the DSM, which derives segments with homogenous spectrum and height. In a second step, the segments are classified with a hybrid decision tree and SVM approach, and then the segments of the building class are merged as building objects for change detection. An initial change indicator (CI) is then computed for each building object concerning height and spectral information. Finally, an adaptive CI updating strategy based on segment overlapping is proposed and the traffic light system based on a dual threshold is used to identify the change status of each building as “change,” “no-change,” and “uncertain change”. The experimental results on scanned aerial stereo images have demonstrated that our proposed framework is able to achieve high-detection accuracy on images with limited spectral quality.
      PubDate: May 2015
      Issue No: Vol. 8, No. 5 (2015)
  • Expert Knowledge-Based Method for Satellite Image Time Series Analysis and
    • Authors: Rejichi; S.;Chaabane, F.;Tupin, F.;
      Pages: 2138 - 2150
      Abstract: For many remote-sensing applications, there is usually a gap between the automatic analysis techniques and the direct expert interpretation. This semantic gap is all the more critical as the amount and diversity of satellite data increase. In this context, an important challenge is the integration of expert knowledge in automatic satellite image time series (SITS) analysis to improve results’ reliability and precision. In this paper, we propose an original expert knowledge-based SITS analysis technique for land-cover monitoring and region dynamics assessing. Particularly, we are interested in extracting region temporal evolution similar to a given scenario proposed by the user, which can be useful in many applications such as urbanization and forest regions’ monitoring. As a first step, with the formalization and exploitation of the expert semantic information, we construct a multitemporal knowledge base describing the remote-sensing scene ontology. Then, the temporal evolution of each region in the SITS is modeled by means of graph theory. Finally, given a user scenario, the most similar region temporal evolution is recognized using the marginalized graph kernel (MGK) similarity measure.
      PubDate: May 2015
      Issue No: Vol. 8, No. 5 (2015)
  • Unsupervised Change Detection in Multitemporal Multispectral Satellite
           Images Using Parallel Particle Swarm Optimization
    • Authors: Kusetogullari; H.;Yavariabdi, A.;Celik, T.;
      Pages: 2151 - 2164
      Abstract: In this paper, a novel algorithm for unsupervised change detection in multitemporal multispectral images of the same scene using parallel binary particle swarm optimization (PBPSO) is proposed. The algorithm operates on a difference image, which is created by using a novel fusion algorithm on multitemporal multispectral images, by iteratively minimizing a cost function with PBPSO to produce a final binary change-detection mask representing changed and unchanged pixels. Each BPSO of parallel instances is run on a separate processor and initialized with a different starting population representing a set of change-detection masks. A communication strategy is applied to transmit data in between BPSOs running in parallel. The algorithm takes the full advantage of parallel processing to improve both the convergence rate and detection performance. We demonstrate the accuracy of the proposed method by quantitative and qualitative tests on semisynthetic and real-world data sets. The semisynthetic results for different levels of Gaussian noise are obtained in terms of false and miss alarm (MA) rates between the estimated change-detection mask and the ground truth image. The proposed method on the semisynthetic data with high level of Gaussian noise obtains the final change-detection mask with a false error rate of 1.50 and MA error rate of 14.51.
      PubDate: May 2015
      Issue No: Vol. 8, No. 5 (2015)
  • Web-Based Supervised Thematic Mapping
    • Authors: Lozano Silva; J.;Aginako Bengoa, N.;Quartulli, M.;Olaizola, I.G.;Zulueta, E.;
      Pages: 2165 - 2176
      Abstract: We introduce a methodology for semiautomatic thematic map generation from remotely sensed Earth Observation raster image data based on user-selected examples. The methodology is based on a probabilistic k-nearest neighbor supervised classification algorithm. Efficient operation is attained by exploiting data structures for high-dimensional indexing. The methodology is integrated in a Web-mapping server that is coupled to an HTML supervision interface that supports interactive navigation as well as model training and tuning. Quantitative classification quality and performance measurements are extracted for real optical data with 0.25 m resolution on a highly diverse training area.
      PubDate: May 2015
      Issue No: Vol. 8, No. 5 (2015)
  • Hyperspectral Tree Species Classification of Japanese Complex Mixed Forest
           With the Aid of Lidar Data
    • Authors: Matsuki; T.;Yokoya, N.;Iwasaki, A.;
      Pages: 2177 - 2187
      Abstract: The classification of tree species in forests is an important task for forest maintenance and management. With the increase in the spatial resolution of remote sensing imagery, individual tree classification is the next target of research area for the forest inventory. In this work, we propose a methodology involving the combination of hyperspectral and LiDAR data for individual tree classification, which can be extended to areas of shadow caused by the illumination of tree crowns with sunlight. To remove the influence of shadows in hyperspectral data, an unmixing-based correction is applied as preprocessing. Spectral features of trees are obtained by principal component analysis of the hyperspectral data. The sizes and shapes of individual trees are derived from the LiDAR data after individual tree-crown delineation. Both spectral and tree-crown features are combined and input into a support vector machine classifier pixel by pixel. This procedure is applied to data taken over Tama Forest Science Garden in Tokyo, Japan, to classify it into 16 classes of tree species. It is found that both shadow correction and tree-crown information improve the classification performance, which is further improved by postprocessing based on tree-crown information derived from the LiDAR data. Regarding the classification results in the case of 10% training data, when using the random sampling of pixels to select training samples, a classification accuracy of 82% was obtained, while the use of reference polygons as a more practical means of sample selection reduced the accuracy to 71%. These values are, respectively, 21.5% and 9% higher than those that are obtained using hyperspectral data only.
      PubDate: May 2015
      Issue No: Vol. 8, No. 5 (2015)
  • Estimating House Vacancy Rate in Metropolitan Areas Using NPP-VIIRS
           Nighttime Light Composite Data
    • Authors: Chen; Z.;Yu, B.;Hu, Y.;Huang, C.;Shi, K.;Wu, J.;
      Pages: 2188 - 2197
      Abstract: House vacancy rate (HVR) is an important index in assessing the healthiness of residential real estate market. Investigating HVR by field survey requires a lot of human and economic resources. The nighttime light (NTL) data, derived from Suomi National Polar-orbiting Partnership, can detect the artificial light from the Earth surface, and have been used to study social-economic activities. This paper proposes a method for estimating the HVR in metropolitan areas using NPP-VIIRS NTL composite data. This method combines NTL composite data with land cover information to extract the light intensity in urbanized areas. Then, we estimate the light intensity values for nonvacancy areas, and use such values to calculate the HVR in corresponding regions. Fifteen metropolitan areas in the United States have been selected for this study, and the estimated HVR values are validated using corresponding statistical data. The experimental results show a strong correlation between our derived HVR values and the statistical data. We also visualize the estimated HVR on maps, and discover that the spatial distribution of HVR is influenced by natural situations as well as the degree of urban development.
      PubDate: May 2015
      Issue No: Vol. 8, No. 5 (2015)
  • Enhancement of Spectral Resolution for Remotely Sensed Multispectral Image
    • Authors: Sun; X.;Zhang, L.;Yang, H.;Wu, T.;Cen, Y.;Guo, Y.;
      Pages: 2198 - 2211
      Abstract: Hyperspectral (HS) remote sensing has an important role in a wide variety of fields. However, its rapid progress has been constrained due to the narrow swath of HS images. This paper proposes a spectral resolution enhancement method (SREM) for remotely sensed multispectral (MS) image, to generate wide swath HS images using auxiliary multi/hyper-spectral data. Firstly, a set number of spectra of different materials are extracted from both the MS and HS data. Secondly, the approach makes use of the linear relationships between multi and hyper-spectra of specific materials to generate a set of transformation matrices. Then, a spectral angle weighted minimum distance (SAWMD) matching method is used to select a suitable matrix to create HS vectors from the original MS image, pixel by pixel. The final result image data has the same spectral resolution as the original HS data that used and the spatial resolution and swath were also the same as for the original MS data. The derived transformation matrices can also be used to generate multitemporal HS data from MS data for different periods. The approach was tested with three image datasets, and the spectra-enhanced and real HS data were compared by visual interpretation, statistical analysis, and classification to evaluate the performance. The experimental results demonstrated that SREM produces good image data, which will not only greatly improve the range of applications for HS data but also encourage more utilization of MS data.
      PubDate: May 2015
      Issue No: Vol. 8, No. 5 (2015)
  • Mapping Vegetation-Covered Urban Surfaces Using Seeded Region Growing in
           Visible-NIR Air Photos
    • Authors: Zhou; J.;Huang, Y.;Yu, B.;
      Pages: 2212 - 2221
      Abstract: Unreliability involved in the extraction of shaded vegetation-covered surfaces (VS) is a common problem in urban vegetation mapping. Serving as a solution to it, a novel method named Nonlinear Fitting-based Seeded Region Growing (NFSRG) is explored. With NFSRG, a series of classified results are organized by a seeded-region-growing process. In order to adapt to the variable separability between VS and background, the growing is limited in several weighted buffers defined by some nonlinear fitting relationships. When searching new VS members (member means both pixel and patch) within such a buffer, a gradually reduced weight makes the buffer width continually narrowed as the separability worsens. To avoid unexpected entrances of water and smooth shaded background members, a during-growing constraint, named expansion rate, is proposed. Accuracy assessments reveal that more than 96% of VS members can be accurately extracted by the proposed method.
      PubDate: May 2015
      Issue No: Vol. 8, No. 5 (2015)
  • Ensemble of Adaptive Rule-Based Granular Neural Network Classifiers for
           Multispectral Remote Sensing Images
    • Authors: Meher; S.K.;Kumar, D.A.;
      Pages: 2222 - 2231
      Abstract: Information granulation opens ample scope to design likely transparent neural networks called granular neural networks (GNNs). The paper proposes a classification model in the framework of ensemble of GNN-based classifiers, and justifies its improved performance in classifying land use/cover classes of multispectral remote sensing (RS) images. The model also provides an adaptive method for fuzzy rules extraction from the fuzzified input variables for GNN and thus avoid the uncertainty in empirical search of rules for output class labels. The superiority of the proposed model to other similar methods is established both visually and quantitatively for land use/cover classification of multispectral RS images. Comparative analysis revealed that GNN with multiple rules performed better than GNN with single rule assigned for each of the classes, and ensemble of GNNs outperformed all other methods. Various performance measures, such as overall accuracy, producer’s accuracy, user’s accuracy, kappa coefficient, and measure of dispersion estimation, are used for comparative analysis.
      PubDate: May 2015
      Issue No: Vol. 8, No. 5 (2015)
  • Integrating Encryption and Marking for Remote Sensing Image Based on
           Orthogonal Decomposition
    • Authors: Jiang; L.;Niu, T.;Xu, Z.;Xu, Y.;
      Pages: 2232 - 2239
      Abstract: For the special characters, remote sensing image has higher requirements not only in the security but also in the management; it requires not only the active encryption during storage and transmission for preventing information leakage but also the marking technology to prevent illegal usage as well as copyright protection or even source tracing. Therefore, this paper proposes to integrate encryption and marking technology by the independence and fusion of orthogonal decomposition for the comprehensive security protection of remote sensing image. Under the proposed scheme, encryption and marking technology can achieve the operation independence and content mergence; moreover, there is no special requirement in selecting encryption and marking algorithms. It makes up the shortage of recent integration of encryption and watermarking based on spatial scrambling in applicability and security. According to the experimental results, integration of encryption and marking technology based on orthogonal decomposition satisfies the common constraints of encryption, and marking technology, furthermore, has little impact on remote sensing image data characters and later applications.
      PubDate: May 2015
      Issue No: Vol. 8, No. 5 (2015)
  • Benchmarking of Remote Sensing Segmentation Methods
    • Authors: Mikes; S.;Haindl, M.;Scarpa, G.;Gaetano, R.;
      Pages: 2240 - 2248
      Abstract: We present the enrichment of the Prague Texture Segmentation Data-Generator and Benchmark (PTSDB) to include the assessment of the remote sensing (RS) image segmenters. The PTSDB tool is a Web-based ( service designed for real-time performance evaluation, mutual comparison, and ranking of various supervised or unsupervised static or dynamic image segmenters. PTSDB supports rapid verification and development of new segmentation approaches. The RS datasets contain ten spectral Advanced Land Imager (ALI) satellite images, their RGB subsets, and very-high-resolution GeoEye RGB images, with optional additive-noise-resistance checking. Alternative setting options allow us to also test scale, rotation, or illumination invariance. The meaningfulness of the newly proposed dataset is demonstrated by testing and comparing several RS segmentation algorithms, and showing that the benchmark figures provide a solid framework for the fair and critical comparison among different techniques.
      PubDate: May 2015
      Issue No: Vol. 8, No. 5 (2015)
  • Efficient Parallel GPU Design on WRF Five-Layer Thermal Diffusion Scheme
    • Authors: Huang; M.;Huang, B.;Chang, Y.;Mielikainen, J.;Huang, H.A.;Goldberg, M.D.;
      Pages: 2249 - 2259
      Abstract: Satellite remote-sensing observations and ground-based radar can detect the weather conditions from a distance and are widely used to monitor the weather all around the globe. The assimilated satellite/radar data are passed through the weather models for weather forecasting. The five-layer thermal diffusion scheme is one of the weather models, handling with an energy budget made up of sensible, latent, and radiative heat fluxes. The model feature of no interactions among horizontal grid points makes this scheme very favorable for parallel processing. This study demonstrates implementation of this scheme using graphics processing unit (GPU) massively parallel architecture. By employing one NVIDIA Tesla K40 GPU, our GPU optimization effort on this scheme achieves a speedup of $311 times$ with respect to its CPU counterpart Fortran code running on one CPU core of Intel Xeon E5-2603, whereas the speedup for one CPU socket (four cores) with respect to one CPU core is only $3.1 times$ . We can even boost the speedup of this scheme to $398 times$ with respect to one CPU core when two NVIDIA Tesla K40 GPUs are applied.
      PubDate: May 2015
      Issue No: Vol. 8, No. 5 (2015)
  • Massive Parallelization of the WRF GCE Model Toward a GPU-Based End-to-End
           Satellite Data Simulator Unit
    • Authors: Huang; M.;Huang, B.;Li, X.;Huang, A.H.;Goldberg, M.D.;Mehta, A.;
      Pages: 2260 - 2272
      Abstract: Modern weather satellites provide more detailed observations of cloud and precipitation processes. To harness these observations for better satellite data assimilations, a cloud-resolving model, known as the Goddard Cumulus Ensemble (GCE) model, was developed and used by the Goddard Satellite Data Simulator Unit (G-SDSU). The GCE model has also been incorporated as part of the widely used weather research and forecasting (WRF) model. The computation of the cloud-resolving GCE model is time-consuming. This paper details our massively parallel design of GPU-based WRF GCE scheme. With one NVIDIA Tesla K40 GPU, the GPU-based GCE scheme achieves a speedup of ${bf 361} times $ as compared to its original Fortran counterpart running on one CPU core, whereas the speedup for one CPU socket (four cores) with respect to one CPU core is only ${bf 3}.{bf 9} times $ .
      PubDate: May 2015
      Issue No: Vol. 8, No. 5 (2015)
  • Fire Disturbance in Tropical Forests of Myanmar—Analysis Using
           MODIS Satellite Datasets
    • Authors: Biswas; S.;Lasko, K.D.;Vadrevu, K.P.;
      Pages: 2273 - 2281
      Abstract: In this study, we quantified the relationship between fires and vegetation disturbance at varied spatial scales using moderate resolution imaging spectroradiometer (MODIS) datasets for the period 2003–2012. We report satellite-derived fire characteristics (frequency, extent, seasonality, and type of vegetation burnt) in Myanmar, the extent of fire disturbance, and the impact of the fires on gross primary productivity (GPP) at multiple scales. Results suggested March as the peak fire season with burnt areas (BAs) of $textbf{12,900};textbf{km}^textbf{2}$ and 95 000 fire counts. Forests accounted for 41.3% of the total BAs followed by shrub lands (33.6%) and agriculture (24.7%). The “low” vegetation disturbance category accounted for 9.2% of total fires, whereas the medium and high categories accounted for about 89.7%. We found relatively higher negative correlation between BA and GPP for deciduous forests ( $textbf{r} = textbf{0.49}, textbf{p} sim textbf{0}$ ) than for evergreen forests ( $textbf{r} = textbf{0.36}, textbf{p} sim textbf{0}$ ). A maximum decrease in 29% of original GPP (2007–2012) was observed in the evergreen forest patches. The scale-dependent correlation analysis suggested significant BA–GPP correlation at $textbf{1} times textbf{1}$ degree compared to finer resolutions. Our results highlight the impact of fire disturbance on vegetation greenness and GPP in tropical forests of Myanmar.
      PubDate: May 2015
      Issue No: Vol. 8, No. 5 (2015)
  • Integration of COSMO-SkyMed and GeoEye-1 Data With Object-Based Image
    • Authors: Gianinetto; M.;Rusmini, M.;Marchesi, A.;Maianti, P.;Frassy, F.;Dalla Via, G.;Dini, L.;Rota Nodari, F.;
      Pages: 2282 - 2293
      Abstract: This paper describes the potentialities of data integration of high spatial resolution multispectral (MS) and single-polarization X-band radar for object-based image analysis (OBIA) using already available algorithms and techniques. GeoEye-1 (GE1) MS images (0.5/2.0 m) and COSMO-SkyMed (CSK®) stripmap images (3.0 m) were collected over a complex test site in the Venetian Lagoon, made up of an intricate mixture of settlements, cultivations, channels, roads, and marshes. The validation confirmed that the integration of optical and radar data substantially increased the thematic accuracy [about 20%–30% for overall accuracy (OA) and about 25%–35% for k coefficient] of MS data, and unlike the outcomes of some new researches, also confirmed that, with appropriate preprocessing, traditional OBIA could also be applied to X-band radar data without the need of developing ad hoc algorithms.
      PubDate: May 2015
      Issue No: Vol. 8, No. 5 (2015)
  • Detecting the Phenology and Discriminating Mediterranean Natural Habitats
           With Multispectral Sensors—An Analysis Based on Multiseasonal Field
    • Authors: Feret; J.;Corbane, C.;Alleaume, S.;
      Pages: 2294 - 2305
      Abstract: Due to their high degree of vegetation heterogeneity, fragmentation, and biodiversity, Mediterranean natural habitats are difficult to assess and monitor with in situ observations solely. Together with standardized ground plots and regular in situ measurements, remote sensing contributes to better understand the diversity of these habitats and their phenology. We used field spectroradiometry to simulate the radiometric signal corresponding to six multispectral satellites: 1) IKONOS, 2) Landsat 5 TM, 3) Landsat 8, 4) Pléiades, 5) Sentinel-2, and 6) WorldView-2. We compared the suitability of each sensor for the estimation of the cover fraction of photosynthetic vegetation (PV) observed for five types of habitats during a vegetation cycle from February to October 2013. We also analyzed the contribution of multiseasonal satellite acquisitions for habitat discrimination. We showed that multivariate regression applied to Worldview-2 reflectance produces the most accurate PV. This was explained by the higher number of spectral bands in the visible domain. Habitat discrimination based on monotemporal acquisitions showed better performances when PV was higher. Sentinel-2 and WorldView-2 outperformed other sensors for each individual date. Multitemporal acquisitions outperformed monotemporal acquisition for habitat discrimination. However, selecting all reflectance data acquired during the season resulted in suboptimal performances compared to more parsimonious combinations. Finally, all of them ranged between 86.6% and 89.2% classification accuracy with multiseasonal acquisitions. New strategies need to be designed to identify individual habitats of particular interest. Defining optimal multiseasonal remote-sensing acquisitions specific to each habitat and appropriate spectral and spatial resolution will contribute to improved discrimination of Mediterranean natural ha- itats.
      PubDate: May 2015
      Issue No: Vol. 8, No. 5 (2015)
  • Multicore Processors and Graphics Processing Unit Accelerators for
           Parallel Retrieval of Aerosol Optical Depth From Satellite Data:
           Implementation, Performance, and Energy Efficiency
    • Authors: Liu; J.;Feld, D.;Xue, Y.;Garcke, J.;Soddemann, T.;
      Pages: 2306 - 2317
      Abstract: Quantitative retrieval is a growing area in remote sensing due to the rapid development of remote instruments and retrieval algorithms. The aerosol optical depth (AOD) is a significant optical property of aerosol which is involved in further applications such as the atmospheric correction of remotely sensed surface features, monitoring of volcanic eruptions or forest fires, air quality, and even climate changes from satellite data. The AOD retrieval can be computationally expensive as a result of huge amounts of remote sensing data and compute-intensive algorithms. In this paper, we present two efficient implementations of an AOD retrieval algorithm from the moderate resolution imaging spectroradiometer (MODIS) satellite data. Here, we have employed two different high performance computing architectures: multicore processors and a graphics processing unit (GPU). The compute unified device architecture C (CUDA-C) has been used for the GPU implementation for NVIDIA’s graphic cards and open multiprocessing (OpenMP) for thread-parallelism in the multicore implementation. We observe for the GPU accelerator, a maximal overall speedup of 68.x for the studied data, whereas the multicore processor achieves a reasonable 7.x speedup. Additionally, for the largest benchmark input dataset, the GPU implementation also shows a great advantage in terms of energy efficiency with an overall consumption of 3.15 kJ compared to 58.09 kJ on a CPU with 1 thread and 38.39 kJ with 16 threads. Furthermore, the retrieval accuracy of all implementations has been checked and analyzed. Altogether, using the GPU accelerator shows great advantages for an application in AOD retrieval in both performance and energy efficiency metrics. Nevertheless, the multicore processor provides the easier programmability for the majority of today’s programmers. Our work exploits the parallel implementations, the performance, and the energy efficiency features of GPU accele- ators and multicore processors. With this paper, we attempt to give suggestions to geoscientists demanding for efficient desktop solutions.
      PubDate: May 2015
      Issue No: Vol. 8, No. 5 (2015)
  • Assessing the Impact of Urban Sprawl on Net Primary Productivity of
           Terrestrial Ecosystems Using a Process-Based Model—A Case Study in
           Nanjing, China
    • Authors: Zhou; Y.;Xing, B.;Ju, W.;
      Pages: 2318 - 2331
      Abstract: Urban sprawl/urbanization has large impacts on the structure and function of terrestrial ecosystems. Net primary production (NPP) is an important indicator for estimating the earth’s ability to support life and aids the evaluation of sustainable development of the terrestrial ecosystem. In this study, the process-based boreal ecosystem productivity simulator (BEPS) model was used in conjunction with leaf area index (LAI) dataset, land cover, and meteorological and soil data to simulate daily NPP at spatial resolution 250 m in Nanjing, a representative region within the Yangtze Delta, for the period 2001–2010. Effects of urbanization on land-cover change and regional NPP are quantitatively evaluated. The results show that during this period, urbanization caused significant land-cover change. Compared with 2001, urbanized area and area covered by water bodies increased significantly, while vegetated area declined greatly. The greatest loss was cropland, followed by evergreen coniferous and closed deciduous forests. There were obvious spatial differences in NPP variations. The reduction rate of annual NPP in the major city of Nanjing, Jiangning District, and Gaochun County was much higher than that in Pukou and Luhe district, and Lishui County. These results indicate that a process-based model driven by remote sensing is useful in assessing the impact of urban sprawl on NPP, and urbanization, not climate factors, is a main factor for NPP reduction for an urbanizing region.
      PubDate: May 2015
      Issue No: Vol. 8, No. 5 (2015)
  • IEEE Open Access Publishing [Advertisement]
    • Pages: 2332 - 2332
      Abstract: Advertisement: This publication offers open access options for authors. IEEE open access publishing.
      PubDate: May 2015
      Issue No: Vol. 8, No. 5 (2015)
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