for Journals by Title or ISSN
for Articles by Keywords

 A  B  C  D  E  F  G  H  I  J  K  L  M  N  O  P  Q  R  S  T  U  V  W  X  Y  Z  

        1 2 | Last

  Subjects -> ELECTRONICS (Total: 146 journals)
Advanced Electromagnetics     Open Access   (9 followers)
Advances in Biosensors and Bioelectronics     Open Access   (1 follower)
Advances in Magnetic and Optical Resonance     Full-text available via subscription   (4 followers)
Advances in Microelectronic Engineering     Open Access   (1 follower)
Advances in Power Electronics     Open Access   (7 followers)
Aerospace and Electronic Systems, IEEE Transactions on     Hybrid Journal   (47 followers)
American Journal of Electrical and Electronic Engineering     Open Access   (7 followers)
Annals of Telecommunications     Hybrid Journal   (3 followers)
APL : Organic Electronics and Photonics     Hybrid Journal   (1 follower)
APSIPA Transactions on Signal and Information Processing     Open Access   (2 followers)
Archives of Electrical Engineering     Open Access   (8 followers)
Autonomous Mental Development, IEEE Transactions on     Hybrid Journal   (5 followers)
Bell Labs Technical Journal     Hybrid Journal   (8 followers)
Biomedical Engineering, IEEE Reviews in     Full-text available via subscription   (14 followers)
Biomedical Engineering, IEEE Transactions on     Hybrid Journal   (11 followers)
Biomedical Instrumentation & Technology     Full-text available via subscription   (4 followers)
Broadcasting, IEEE Transactions on     Hybrid Journal   (5 followers)
BULLETIN of National Technical University of Ukraine. Series RADIOTECHNIQUE. RADIOAPPARATUS BUILDING     Open Access   (1 follower)
Bulletin of the Polish Academy of Sciences : Technical Sciences     Open Access  
Canadian Journal of Remote Sensing     Full-text available via subscription   (12 followers)
China Communications     Full-text available via subscription  
Circuits and Systems     Open Access   (6 followers)
Consumer Electronics Times     Open Access   (3 followers)
Control Systems     Hybrid Journal   (18 followers)
Electronic Markets     Hybrid Journal   (5 followers)
Electronic Materials Letters     Hybrid Journal   (2 followers)
Electronics     Open Access   (3 followers)
Electronics and Communications in Japan     Hybrid Journal   (4 followers)
Electronics Letters     Hybrid Journal   (15 followers)
Embedded Systems Letters, IEEE     Hybrid Journal   (14 followers)
EURASIP Journal on Embedded Systems     Open Access   (8 followers)
EURASIP Journal on Image and Video Processing     Open Access   (6 followers)
EURASIP Journal on Wireless Communications and Networking     Open Access   (8 followers)
Foundations and TrendsĀ® in Communications and Information Theory     Full-text available via subscription   (5 followers)
Foundations and TrendsĀ® in Signal Processing     Full-text available via subscription   (4 followers)
Frequenz     Full-text available via subscription   (1 follower)
Frontiers of Optoelectronics     Hybrid Journal  
Geoscience and Remote Sensing, IEEE Transactions on     Hybrid Journal   (19 followers)
Haptics, IEEE Transactions on     Hybrid Journal   (4 followers)
IEEE Consumer Electronics Magazine     Full-text available via subscription   (7 followers)
IEEE Electromagnetic Compatibility Magazine     Full-text available via subscription   (2 followers)
IEEE Journal of Emerging and Selected Topics in Power Electronics     Hybrid Journal   (3 followers)
IEEE Journal of the Electron Devices Society     Open Access   (1 follower)
IEEE Transactions on Audio, Speech, and Language Processing     Hybrid Journal   (9 followers)
IEEE Transactions on Automatic Control     Hybrid Journal   (18 followers)
IEEE Transactions on Consumer Electronics     Hybrid Journal   (10 followers)
IEEE Transactions on Electromagnetic Capability     Hybrid Journal   (10 followers)
IEEE Transactions on Electron Devices     Hybrid Journal   (6 followers)
IEEE Transactions on Information Theory     Hybrid Journal   (12 followers)
IEEE Transactions on Power Electronics     Hybrid Journal   (10 followers)
IEICE - Transactions on Electronics     Full-text available via subscription   (7 followers)
IEICE - Transactions on Information and Systems     Full-text available via subscription   (7 followers)
IET Power Electronics     Hybrid Journal   (7 followers)
IET Wireless Sensor Systems     Hybrid Journal   (5 followers)
IETE Journal of Education     Open Access   (2 followers)
IETE Journal of Research     Open Access   (5 followers)
IETE Technical Review     Open Access   (1 follower)
Industrial Electronics, IEEE Transactions on     Hybrid Journal   (9 followers)
Industry Applications, IEEE Transactions on     Hybrid Journal   (5 followers)
Informatik-Spektrum     Hybrid Journal  
Instabilities in Silicon Devices     Full-text available via subscription  
Intelligent Transportation Systems Magazine, IEEE     Full-text available via subscription   (1 follower)
International Journal of Advanced Electronics and Communication Systems     Open Access   (3 followers)
International Journal of Advanced Research in Computer Science and Electronics Engineering     Open Access   (18 followers)
International Journal of Advances in Telecommunications, Electrotechnics, Signals and Systems     Open Access   (1 follower)
International Journal of Aerospace Innovations     Full-text available via subscription   (10 followers)
International Journal of Applied Electronics in Physics & Robotics     Open Access  
International Journal of Biomedical Nanoscience and Nanotechnology     Hybrid Journal   (3 followers)
International Journal of Computational Vision and Robotics     Hybrid Journal   (4 followers)
International Journal of Computer & Electronics Research     Full-text available via subscription   (2 followers)
International Journal of Control     Hybrid Journal   (9 followers)
International Journal of Electronics     Hybrid Journal   (2 followers)
International Journal of Electronics & Data Communication     Open Access   (3 followers)
International Journal of Electronics and Telecommunications     Open Access   (3 followers)
International Journal of Granular Computing, Rough Sets and Intelligent Systems     Hybrid Journal   (1 follower)
International Journal of High Speed Electronics and Systems     Hybrid Journal  
International Journal of Microwave and Wireless Technologies     Hybrid Journal   (1 follower)
International Journal of Nano Devices, Sensors and Systems     Open Access   (1 follower)
International Journal of Nanoscience     Hybrid Journal   (1 follower)
International Journal of Numerical Modelling:Electronic Networks, Devices and Fields     Hybrid Journal   (2 followers)
International Journal of Power Electronics     Hybrid Journal   (3 followers)
International Journal of Power Management Electronics     Open Access  
International Journal of Review in Electronics & Communication Engineering     Open Access   (1 follower)
International Journal of Sensors, Wireless Communications and Control     Hybrid Journal   (1 follower)
International Journal of Systems, Control and Communications     Hybrid Journal   (2 followers)
International Journal on Communication     Full-text available via subscription   (6 followers)
International Journal on Electrical and Power Engineering     Full-text available via subscription   (11 followers)
ISRN Communications and Networking     Open Access   (4 followers)
ISRN Electronics     Open Access   (1 follower)
ISRN Signal Processing     Open Access  
Journal of Advanced Dielectrics     Open Access   (1 follower)
Journal of Artificial Intelligence     Open Access   (5 followers)
Journal of Circuits, Systems, and Computers     Hybrid Journal   (1 follower)
Journal of Electrical and Electronics Engineering Research     Open Access   (1 follower)
Journal of Electrical Bioimpedance     Full-text available via subscription   (2 followers)
Journal of Electrical Engineering & Electronic Technology     Full-text available via subscription   (1 follower)
Journal of Electromagnetic Analysis and Applications     Open Access   (3 followers)
Journal of Electromagnetic Waves and Applications     Hybrid Journal   (2 followers)
Journal of Electronic Design Technology     Full-text available via subscription  
Journal of Electronics (China)     Hybrid Journal   (2 followers)

        1 2 | Last

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  [171 journals]   [SJR: 1.232]   [H-I: 14]
  • 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: Jan. 2014
      Issue No: Vol. 7, No. 1 (2014)
  • IEEE Transactions on Geoscience and Remote Sensing institutional listings
    • Pages: C4 - C4
      Abstract: Provides a listing of current staff, committee members and society officers.
      PubDate: Jan. 2014
      Issue No: Vol. 7, No. 1 (2014)
  • 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: Jan. 2014
      Issue No: Vol. 7, No. 1 (2014)
  • [Front cover]
    • Pages: C1 - C1
      Abstract: Presents the front cover or splash screen of the proceedings record.
      PubDate: Jan. 2014
      Issue No: Vol. 7, No. 1 (2014)
  • Table of contents
    • Pages: 1 - 2
      Abstract: Presents the table of contents for this issue of the periodical.
      PubDate: Jan. 2014
      Issue No: Vol. 7, No. 1 (2014)
  • The GMES Space Component Data Access System: Harmonizing Earth Observation
           Products Flows and Services for GMES
    • Authors: Tassa; A.;Vingione, G.;Knowelden, R.;Martin, J.;Ottavianelli, G.;Lopes, C.;Barois, O.;Mougnaud, P.;Amans, V.;Monjoux, E.;
      Pages: 3 - 16
      Abstract: The Global Monitoring for Environment and Security (GMES) Space Component relies on a constellation of dedicated space missions (called the Sentinels, scheduled for first launch by 2013), as well as on a set of space missions not dedicated to GMES but contributing to it. In charge of optimizing access and usability of the available space-based remote sensing resources for the pre-operations, the European Space Agency has elaborated the GMES Space Component Data Access concept, implementing mechanisms for aligning the data access across the GMES contributing missions, harmonising user information services, ordering and data delivery processes, and monitoring the data and service quality. Overall, this is a large-scale integrated system of systems, involving today more than 10 different ground segments and 20 space missions, serving users according to different modes of operations derived from pre-identified users' needs.
      PubDate: Jan. 2014
      Issue No: Vol. 7, No. 1 (2014)
  • Mapping Bugweed (Solanum mauritianum) Infestations in Pinus patula
           Plantations Using Hyperspectral Imagery and Support Vector Machines
    • Authors: Atkinson; J.T.;Ismail, R.;Robertson, M.;
      Pages: 17 - 28
      Abstract: The invasive plant known as bugweed (Solanum mauritianum) is a notorious invader of forestry plantations in the eastern parts of South Africa. Not only is bugweed considered to be one of five most widespread invasive alien plant (IAP) species in the summer rainfall regions of South Africa but it is also one of the worst invasive alien plants in Africa. It forms dense infestations that not only impacts upon commercial forestry activities but also causes significant ecological and environment damage within natural areas. Effective weed management efforts therefore require robust approaches to accurately detect; map and monitor weed distribution in order to mitigate the impact on forestry operations. The main objective of this research was to determine the utility of support vector machines (SVMs) with a 272-waveband AISA Eagle image to detect and map the presence of co-occurring bugweed within mature Pinus patula compartments in KwaZulu Natal. The SVM when utilized with a recursive feature elimination (SVM-RFE) approach required only 17 optimal wavebands from the original image to produce a classification accuracy of 93% and True Skills Statistic of 0.83. Results from this study indicate that (1) there is definite potential for using SVMs for the accurate detection and mapping of bugweed in commercial plantations and (2) it is not necessary to use the entire 272-waveband dataset because the SVM-RFE approach identified an optimal subset of wavebands for weed detection thus enabling improved data processing and analysis.
      PubDate: Jan. 2014
      Issue No: Vol. 7, No. 1 (2014)
  • Super-Resolution Mapping of Forests With Bitemporal Different Spatial
           Resolution Images Based on the Spatial-Temporal Markov Random Field
    • Authors: Xiaodong Li;Yun Du;Feng Ling;
      Pages: 29 - 39
      Abstract: High deforestation rates necessitate satellite images for the timely updating of forest maps. Coarse spatial resolution remotely sensed images have wide swath and high temporal resolution. However, the mixed pixel problem lowers the mapping accuracy and hampers the application of these images. The development of remote sensing technology has enabled the storage of a great amount of medium spatial resolution images that recorded the historical conditions of the earth. The combination of timely updated coarse spatial resolution images and previous medium spatial resolution images is a promising technique for mapping forests in large areas with instant updating at low expense. Super-resolution mapping (SRM) is a method for mapping land cover classes with a finer spatial resolution than the input coarse resolution image. This method can reduce the mixed pixel problem of coarse spatial resolution images to a certain extent. In this paper, a novel spatial-temporal SRM based on a Markov random field, called STMRF_SRM, is proposed using a current coarse spatial resolution Moderate-Resolution Imaging Spectroradiometer image and a previous medium spatial resolution Landsat Thematic Mapper image as input. The proposed model encourages the spatial smoothing of land cover classes for spatially neighboring subpixels and keeps temporal links between temporally neighboring subpixels in bitemporal images. Results show that the proposed STMRF_SRM model can generate forest maps with higher overall accuracy and kappa value.
      PubDate: Jan. 2014
      Issue No: Vol. 7, No. 1 (2014)
  • Hyperspectral Image Classification Using Band Selection and Morphological
    • Authors: Kun Tan;Erzhu Li;Qian Du;Peijun Du;
      Pages: 40 - 48
      Abstract: In this paper, we propose a simple unsupervised framework to effectively select and combine spectral information and spatial features for Support Vector Machine (SVM)-based classification when spatial features are the widely used morphological profiles (MPs). To overcome the difficulty of high dimensionality of resulting features, it is a common practice that MPs are extracted from principal components (PCs). In this paper, we investigate another technique on spectral feature selection, which is unsupervised band selection (BS). We find out that using selected bands as spectral features can improve classification performance because they contain more critical characteristics for classification; in particular, using the selected bands, combined with the MPs extracted from PCs, can yield the highest accuracy, due to the fact that major PCs contain less noise for extracting more reliable MPs. The overall unsupervised nature of feature selection provides the flexibility of implementation. We believe that such finding is instructive to feature selection and extraction for spectral/spatial-based hyperspectral image classification.
      PubDate: Jan. 2014
      Issue No: Vol. 7, No. 1 (2014)
  • The Effects of Solar Irradiance Spectra on Calculation of Narrow Band
           Top-of-Atmosphere Reflectance
    • Authors: Lifu Zhang;Shunshi Hu;Hang Yang;Taixia Wu;Qingxi Tong;Feizhou Zhang;
      Pages: 49 - 58
      Abstract: Extraterrestrial solar irradiance spectra detail the solar energy distribution over wavelengths, and numerous solar irradiance models are available within the remote sensing community. However, reference spectra may differ widely owing to differences in solar activity, measurement instruments and calibration. Six widely referenced solar spectra were selected in this work to examine their differences and the impacts of these differences on calculations of narrow band top-of-atmosphere reflectance using MERIS and Hyperion hyperspectral sensor spectral configurations. Mean solar exoatmospheric irradiance (MSEI) was computed using the different solar irradiance models and spectral response functions of the MERIS and Hyperion hyperspectral sensors. Then, the effects of MSEI on top-of-atmosphere (TOA) reflectance and the normalized difference vegetation index (NDVI) and atmospherically resistant vegetation index (ARVI) were investigated. The results show that the six selected solar irradiance models have significant differences from 350 to 2500 nm, which in turn result in differences in the MSEI derived from MERIS and Hyperion observations. These differences have a less significant effect on the TOA reflectance in the visible and near-infrared bands and on NDVI. However, the differences result in large differences in TOA reflectance in the infrared bands and in ARVI.
      PubDate: Jan. 2014
      Issue No: Vol. 7, No. 1 (2014)
  • Fast Constrained Least Squares Spectral Unmixing Using Primal-Dual
           Interior-Point Optimization
    • Authors: Chouzenoux; E.;Legendre, M.;Moussaoui, S.;Idier, J.;
      Pages: 59 - 69
      Abstract: Hyperspectral data unmixing aims at identifying the components (endmembers) of an observed surface and at determining their fractional abundances inside each pixel area. Assuming that the spectral signatures of the surface components have been previously determined by an endmember extraction algorithm, or to be part of an available spectral library, the main problem is reduced to the estimation of the fractional abundances. For large hyperspectral image data sets, the estimation of the abundance maps requires the resolution of a large-scale optimization problem subject to linear constraints such as non-negativity and sum less or equal to one. This paper proposes a primal-dual interior-point optimization algorithm allowing a constrained least squares estimation approach. In comparison with existing methods, the proposed algorithm is more flexible since it can handle any linear equality and/or inequality constraint and has the advantage of a reduced computational cost. It also presents an algorithmic structure suitable for a parallel implementation on modern intensive computing devices such as Graphics Processing Units (GPU). The implementation issues are discussed and the applicability of the proposed approach is illustrated with the help of examples on synthetic and real hyperspectral data.
      PubDate: Jan. 2014
      Issue No: Vol. 7, No. 1 (2014)
  • Progress in Hyperspectral Remote Sensing Science and Technology in China
           Over the Past Three Decades
    • Authors: Qingxi Tong;Yongqi Xue;Lifu Zhang;
      Pages: 70 - 91
      Abstract: This paper reviews progress in hyperspectral remote sensing (HRS) in China, focusing on the past three decades. China has made great achievements since starting in this promising field in the early 1980s. A series of advanced hyperspectral imaging systems ranging from ground to airborne and satellite platforms have been designed, built, and operated. These include the field imaging spectrometer system (FISS), the Modular Airborne Imaging Spectrometer (MAIS), and the Chang'E-I Interferometer Spectrometer (IIM). In addition to developing sensors, Chinese scientists have proposed various novel image processing techniques. Applications of hyperspectral imaging in China have been also performed including mineral exploration in the Qilian Mountains and oil exploration in Xinjiang province. To promote the development of HRS, many generic and professional software tools have been developed. These tools such as the Hyperspectral Image Processing and Analysis System (HIPAS) incorporate a number of special algorithms and features designed to take advantage of the wealth of information contained in HRS data, allowing them to meet the demands of both common users and researchers in the scientific community.
      PubDate: Jan. 2014
      Issue No: Vol. 7, No. 1 (2014)
  • Testing the Top-Down Model Inversion Method of Estimating Leaf Reflectance
           Used to Retrieve Vegetation Biochemical Content Within Empirical
    • Authors: Simic; A.;Chen, J.M.;Leblanc, S.G.;Dyk, A.;Croft, H.;Tian Han;
      Pages: 92 - 104
      Abstract: A top-down model inversion method of estimating leaf reflectance from hyperspectral remote sensing measurements has been tested with an empirical approach used to estimate chlorophyll content. Leaf reflectance is obtained by inverting a geometric-optical model, 5-Scale, validated using hyperspectral AVIRIS data. The shaded scene fractions and the M factor, which includes both the multiple scattering effect and the shaded components, are computed for inverting canopy reflectance into leaf reflectance. The inversion is based on the look-up tables (LUTs) approach. The simulated leaf reflectance values are combined in hyperspectral indices for leaf chlorophyll retrieval and compared against the measured leaf chlorophyll content in the Greater Victoria Watershed District (GVWD), British Columbia (BC). The results demonstrate that the modeled canopy reflectance and AVIRS data are in good agreement for all locations. The regressions of the modified simple ratio [(R728 - R434)/(R720 - R434)] and modified normalized difference index [(R728 - R720)/(R728 + R720 -2R434)] against chlorophyll content exhibit the best fit using second-order polynomial functions with the root-mean-square errors (RMSE) of 4.434 and 4.247, and coefficients of determination of 0.588 and 0.588, respectively. Larger RMSE are observed when the direct canopy-level retrieval, using canopy-level generated vegetation indices, is considered, suggesting the importance of the proposed canopy-to-level reflectance inversion step in chlorophyll retrieval based on hyperspectral vegetation indices. This approach allows for estimation of leaf level information in the absence of leaf spectra field measurements, and simplifies further applications of hyperspectral imagery at the regional scale.
      PubDate: Jan. 2014
      Issue No: Vol. 7, No. 1 (2014)
  • Building Change Detection From Multitemporal High-Resolution Remotely
           Sensed Images Based on a Morphological Building Index
    • Authors: Xin Huang;Liangpei Zhang;Tingting Zhu;
      Pages: 105 - 115
      Abstract: In this study, urban building change detection is investigated, considering that buildings are one of the most dynamic structures in urban areas. To this aim, a novel building change detection approach for multitemporal high-resolution images is proposed based on a recently developed morphological building index (MBI), which is able to automatically indicate the presence of buildings from high-resolution images. In the MBI-based change detection framework, the changed building information is decomposed into MBI, spectral, and shape conditions. A variation of the MBI is a basic condition for the indication of changed buildings. Besides, the spectral information is used as a mask since the change of buildings is primarily related to the spectral variation, and the shape condition is then used as a post-filter to remove irregular structures such as noise and road-like narrow objects. The change detection framework is carried out based on a threshold-based processing at both the feature and decision levels. The advantages of the proposed method are that it does not need any training samples and it is capable of reducing human labor, considering the fact that the current building change detection methods are totally reliant on visual interpretation. The proposed method is evaluated with a QuickBird dataset from 2002 and 2005 covering Hongshan District of Wuhan City, China. The experiments show that the proposed change detection algorithms can achieve satisfactory correctness rates (over 80%) with a low level of total errors (less than 10%), and give better results than the supervised change detection using the support vector machine (SVM).
      PubDate: Jan. 2014
      Issue No: Vol. 7, No. 1 (2014)
  • Automatic Classification of Offshore Wind Regimes With Weather Radar
    • Authors: Trombe; P.-J.;Pinson, P.;Madsen, H.;
      Pages: 116 - 125
      Abstract: Weather radar observations are called to play an important role in offshore wind energy. In particular, they can enable the monitoring of weather conditions in the vicinity of large-scale offshore wind farms and thereby notify the arrival of precipitation systems associated with severe wind fluctuations. The information they provide could then be integrated into an advanced prediction system for improving offshore wind power predictability and controllability. In this paper, we address the automatic classification of offshore wind regimes (i.e., wind fluctuations with specific frequency and amplitude) using reflectivity observations from a single weather radar system. A categorical sequence of most likely wind regimes is estimated from a wind speed time series by combining a Markov-Switching model and a global decoding technique, the Viterbi algorithm. In parallel, attributes of precipitation systems are extracted from weather radar images. These attributes describe the global intensity, spatial continuity and motion of precipitation echoes on the images. Finally, a CART classification tree is used to find the broad relationships between precipitation attributes and wind regimes.
      PubDate: Jan. 2014
      Issue No: Vol. 7, No. 1 (2014)
  • Development of a Remote Sensing-Based Method to Map Likelihood of Common
           Ragweed (Ambrosia artemisiifolia) Presence in Urban Areas
    • Authors: Ngom; R.;Gosselin, P.;
      Pages: 126 - 139
      Abstract: Common Ragweed (Ambrosia artemisiifolia) is a plant that constitutes an important and growing public health concern worldwide as it is probably expanding with climate change, which brings forward the need for improved mapping tools. Our final purpose is to operationalize the use of optical remote sensing for the automated mapping and surveillance of Ambrosia artemisiifolia. Analyses considering the probable spectral instability originating from the variability of the urban landscape and from that of sensors characteristics were developed. Worldview 2, Rapid Eye and SPOT 4 HRVIR sensors were used together with geolocalized surveys of Common Ragweed in Montréal and Valleyfield (Quebec, Canada). Images were standardized and various derivatives variables such as multiple vegetation indexes were created. Spectral confusion, statistical analyses, object-oriented technology and Fuzzy-logic functions were used to develop predictive risks maps of Common Ragweed potential presence. The results showed that the green bands (510-590 nm) of higher spatial resolutions sensors had a higher potential to cope with spectral confusions and changing landscape characteristics and to predict the likelihood of Ambrosia artemisiifolia presence with a recurrent stability. The good agreement between observed and predicted ragweed revealed an important potential for the operationalization of this method.
      PubDate: Jan. 2014
      Issue No: Vol. 7, No. 1 (2014)
  • Forest Biomass Mapping of Northeastern China Using GLAS and MODIS Data
    • Authors: Yuzhen Zhang;Shunlin Liang;Guoqing Sun;
      Pages: 140 - 152
      Abstract: In this study, several major issues associated with forest biomass mapping have been investigated using an integrated dataset, and a preliminary forest biomass map of northeastern China is presented. Three biomass regression models, stepwise regression (SR), partial least-squares regression (PLSR), and support vector regression (SVR), were developed based on field biomass data, Geoscience Laser Altimeter System (GLAS) data, and Moderate Resolution Imaging Spectroradiometer (MODIS) data. The biomass estimates using the SVR model were the most reasonable. The accuracy of the biomass predictions was improved through a combination of bootstrapping and the SVR method. The rich temporal information in MODIS data and the multiple-angle information in Multi-angle Imaging Spectro Radiometer (MISR) data were also explored for forest biomass mapping. Results indicated that a MODIS time series data alone, without MISR data, was capable of mapping forest biomass. A forest biomass map was generated using the optimal biomass regression model and the MODIS time series data. Finally, an uncertainty analysis of the biomass map was carried out and a comparison with published results using other methods was made.
      PubDate: Jan. 2014
      Issue No: Vol. 7, No. 1 (2014)
  • A Prototype Software Package to Retrieve Soil Moisture From Sentinel-1
           Data by Using a Bayesian Multitemporal Algorithm
    • Authors: Pierdicca; N.;Pulvirenti, L.;Pace, G.;
      Pages: 153 - 166
      Abstract: The Sentinel-1 mission will offer the opportunity to obtain C-band radar data characterized by short revisit time, thus allowing for the generation of frequent soil moisture maps. This work presents a prototype software implementing a multitemporal approach to the problem of soil moisture retrieval using Synthetic Aperture Radar (SAR) data. The approach exploits the short revisit time of Sentinel-1 data by assuming the availability of a time series of SAR images that is integrated within a retrieval algorithm based on the Bayesian maximum a posteriori probability statistical criterion. The paper focuses on the combination of on-line and off-line processing that has been designed in order to decrease the time necessary to produce a soil moisture map, which may be a critical aspect of multitemporal approaches. It describes also the optimization of the algorithm carried out to find the set of algorithm parameters that allow obtaining the best tradeoff between accuracy of the estimates and computational efficiency. A set of simulations of C-band SAR data, produced by applying a well-established radar-backscattering model, is used to perform the optimization. The designed system is tested on a series of ERS-1 SAR data acquired on February-April 1994 in Central Italy with a revisit time of three days. The results indicate that the temporal trend of estimated soil moisture is consistent with the succession of rain events occurred throughout the period of ERS-1 acquisitions over the observed geographic area.
      PubDate: Jan. 2014
      Issue No: Vol. 7, No. 1 (2014)
  • ISAR Imaging of Non-Uniformly Rotating Target via
           Range-Instantaneous-Doppler-Derivatives Algorithm
    • Authors: Yong Wang;Yanchao Lin;
      Pages: 167 - 176
      Abstract: Inverse synthetic aperture radar (ISAR) imaging of non-uniformly rotating target is very significant in many applications. In this case, the Doppler frequency for a scatterer is time-varying, and the traditional Range-Doppler (RD) algorithm is not appropriate for the azimuth focusing. In this paper, the received signal in a range bin is assumed to be multi-component polynomial phase signal (PPS), and a novel approach for ISAR imaging of non-uniformly rotating target via Range-Instantaneous-Doppler-Derivatives (RIDD) algorithm is proposed. For the RIDD algorithm, the cross-range resolution is derived by the arbitrary order of Doppler derivatives, and it is the generalization and extension form of the traditional Range-Instantaneous-Doppler (RID) and Range-Instantaneous-Chirp-Rate (RIC) algorithms. Results of simulated and raw data demonstrate the effectiveness of the new method proposed.
      PubDate: Jan. 2014
      Issue No: Vol. 7, No. 1 (2014)
  • Spectral Discrimination of Insect Defoliation Levels in Mopane Woodland
           Using Hyperspectral Data
    • Authors: Adelabu; S.;Mutanga, O.;Adam, E.;Sebego, R.;
      Pages: 177 - 186
      Abstract: Mopane woodland are a source of valuable resources that contribute substantially to rural economies and nutrition across Southern Africa. However, a number of factors have, of late, brought the sustainability of the mopane woodland resources into question. One of such factors is the difficulty in monitoring of defoliation process within the woodland. In this study we set out to discriminate the levels of change in forest canopy cover detectable after insect defoliation using ground based hyperspectral measurements in mopane woodland. Canopy spectral measurements were taken from three levels of defoliation: Undefoliated (UD), Partly defoliated (PD) and Refoliating plants (R) using ASD FieldSpec HandHeld 2. A pre-filtering approach (ANOVA) was compared with random forest independent variable selector in selecting the significant wavelengths for classification. Furthermore, a backward feature elimination method was used to select optimal wavelengths for discriminating the different levels of defoliation in mopane woodland. Results show that optimal wavelengths located at 707 nm, 710 nm, 711 nm, 712 nm, 713 nm, 714 nm, 727 nm, and 1066 nm were able to discriminate between the three levels of defoliation. The results further show that there was no significant difference in the overall accuracy of classification when random forest variable selector was used 82.42% (Kappa = 0.64) and the pre-filtering approach (ANOVA) 81.21% (Kappa = 0.68) used before building the classification. Overall, the study clearly demonstrated that the dynamic process of defoliation in mopane woodland can be assessed and detected using hyperspectral dataset and effective algorithm for discrimination.
      PubDate: Jan. 2014
      Issue No: Vol. 7, No. 1 (2014)
  • Incorporating Sub-Dominant Classes in the Accuracy Assessment of
           Large-Area Land Cover Products: Application to GlobCover, MODISLC, GLC2000
           and CORINE in Spain
    • Authors: Perez-Hoyos; A.;Garcia-Haro, F.J.;Valcarcel, N.;
      Pages: 187 - 205
      Abstract: Various global land cover (LC) datasets have been produced from remote sensing data in response to the need for information about LC. Nevertheless, the potential use of global LC products is often very limited by the lack of detailed accuracy information at regional to national level. This paper proposes a methodology for performing accuracy assessment of large-area LC products, which takes into account a number of factors arising from intrinsic characteristics of LC, such as thematic uncertainty that results from the partial overlap in legend definitions and lack of homogeneity within reference and classification data. The approach compares the LC pixel label not only with the dominant reference label but also with sub-dominant LC types within the extent of the sampling unit. The methodology was illustrated in Spain using four LC datasets (GlobCover, MODIS land cover (MODISLC), GLC2000 and CORINE). The variety of reference label data offered by a detailed national database, namely SIOSE, supported several different fuzzy agreement definitions in order to derive unbiased estimates of accuracy measures. CORINE followed by GLC2000 showed the highest accuracy scores, whereas GlobCover and MODISLC showed the lowest scores. Nevertheless, the fuzzy approach revealed that a great amount of disagreement in GlobCover and MODISLC datasets does not actually correspond to classification errors, but it can be associated to legend ambiguity and mixed coverage pixels.
      PubDate: Jan. 2014
      Issue No: Vol. 7, No. 1 (2014)
  • Brightness of Nighttime Lights as a Proxy for Freight Traffic: A Case
           Study of China
    • Authors: Jingru Tian;Naizhuo Zhao;Samson; E.L.;Shuliang Wang;
      Pages: 206 - 212
      Abstract: Since economic reforms in 1978, China's huge growth has led to a rapid increase in demand for freight traffic. Timely assessments of past and current amounts of freight traffic are basis for predicting future demands of freight traffic and appropriately allocating transportation resources. Sum lights (summed digital number (DN) value of pixels of nighttime light imagery) for years 2000, 2004, and 2008 respectively are extracted from corresponding Defense Meteorological Satellite Program's Operational Linescan System (DMSP-OLS) stable lights annual image composites. The sum lights are then regressed on total freight traffic (TFT), railway freight traffic (RFT), and highway freight traffic (HFT), respectively, at the province level. Results show that sum light strongly correlates to TFT and HFT, so sum light can be used as a proxy for TFT and HFT at the province level. However, due to lack of strong correlations between RFT and GDP, sum light is not appropriate to be as a proxy of RFT. Finally we disaggregate each province/municipality's HFT to each pixel in proportion to the DN value of the pixel of the nighttime light image to produce a Chinese HFT map of 2008 with 1 km × 1 km resolution. Compared to traditional census-based freight traffic data, the freight traffic data derived from the nighttime light imagery contain more spatial information.
      PubDate: Jan. 2014
      Issue No: Vol. 7, No. 1 (2014)
  • An Azimuth Frequency Non-Linear Chirp Scaling (FNCS) Algorithm for TOPS
           SAR Imaging With High Squint Angle
    • Authors: Yufeng Wu;Guang-Cai Sun;Xiang-Gen Xia;Mengdao Xing;Jun Yang;Zheng Bao;
      Pages: 213 - 221
      Abstract: During the data acquisition of a squint terrain observation by progressive scan (TOPS) synthetic aperture radar (SAR), the steering of the antenna main beam increases the azimuth bandwidth and results in the azimuth signal aliasing in the Doppler domain. Besides, the range curvature and the Doppler frequency modulation (FM) rates after linear range walk correction are azimuth-variant for squint TOPS SAR. These problems may cause some difficulties for the SAR data processing. To deal with the problems, a new imaging algorithm for the squint TOPS SAR is introduced in this paper. After linear range walk correction, the azimuth preprocessing is implemented to achieve the two-dimensional frequency spectrum without aliasing. Then, utilizing a modified chirp scaling algorithm, we complete the range cell migration correction (RCMC) and range compression to the TOPS SAR data without subaperture. Finally, the frequency nonlinear chirp scaling (FNCS) is proposed to correct the variation of the FM rates and the azimuth signal is focused in the Doppler domain via the spectral analysis (SPECAN) method. Both simulation and real data results show the effectiveness of the proposed algorithm.
      PubDate: Jan. 2014
      Issue No: Vol. 7, No. 1 (2014)
  • Retrieval of 30-m-Resolution Leaf Area Index From China HJ-1 CCD Data and
           MODIS Products Through a Dynamic Bayesian Network
    • Authors: Yonghua Qu;Yuzhen Zhang;Huazhu Xue;
      Pages: 222 - 228
      Abstract: The leaf area index (LAI) is a characteristic parameter of vegetation canopies. This parameter is significant in research on global climate change and ecological environments. The China HJ-1 satellite has a revisit cycle of four days and provides CCD (HJ-1 CCD) data with a resolution of 30 m. However, the HJ-1 CCD is incapable of obtaining observations at multiple angles. This is problematic because single-angle observations provide insufficient data for determining the LAI. This article proposes a new method for determining the LAI using the HJ-1 CCD data. The proposed method uses background knowledge of the dynamic land surface processes that is extracted from MODerate resolution Imaging Spectroradiometer (MODIS) LAI data with a resolution of 1 km. The proposed method was implemented in a dynamitic Bayesian network scheme by integrating an LAI dynamic process model and a canopy reflectance model with the remotely sensed data. The validation was conducted using field LAI data collected in the Guantao County of the Hebei Province in China. The results showed that the determination coefficient between the estimated and the measured LAI was 0.791, and the RMSE was 0.61. The results suggest that this algorithm can be widely applied to determine high-resolution leaf area indexes using data from the China HJ-1 satellite even if the information from single-angle observations are insufficient for quantitative application.
      PubDate: Jan. 2014
      Issue No: Vol. 7, No. 1 (2014)
  • Modeling Hotspots of Climate Change in the Sahel Using Object-Based
           Regionalization of Multidimensional Gridded Datasets
    • Authors: Hagenlocher; M.;Lang, S.;Holbling, D.;Tiede, D.;Kienberger, S.;
      Pages: 229 - 234
      Abstract: The population of subsaharan Africa, and particularly of the countries of the Sahel and western Africa, is one of the most vulnerable to climate change and climate-related extreme events. To provide updated information for targeted climate change adaptation measures, we modeled hotspots of climate change and related extreme events in an integrative manner. This was achieved by constructing a spatial composite indicator of cumulative climate change impact, which integrates four climate- and hazard-related subindicators: seasonal temperature trends, seasonal precipitation trends, drought occurrences, and major flood events. The analysis is based on time-series of freely available continuous, gridded geo-spatial datasets, including remote sensing data. The aggregation of the four subindicators was performed by making use of a regionalization approach, based on segmentation techniques widely used in the remote sensing community in the field of object-based image analysis. Following the approach presented in this paper, 19 hotspots with most severe climatic changes were identified, evaluated, and mapped. The method enables not only the prioritization of intervention areas, but also allows decomposing the identified hotspots into their underlying subindicators, and thus additional information for effective climate change adaptation measures can be provided.
      PubDate: Jan. 2014
      Issue No: Vol. 7, No. 1 (2014)
  • TOPS-Mode Raw Data Processing Using Chirp Scaling Algorithm
    • Authors: Wei Xu;Pingping Huang;Wang; R.;Yunkai Deng;Youchun Lu;
      Pages: 235 - 246
      Abstract: This paper presents four imaging approaches for terrain observation by progressive scans (TOPS)-mode SAR focusing, all of which take advantage of modified chirp scaling (CS) algorithms according to echo properties in TOPS. After adopting different azimuth preprocessing steps to resolve the aliased Doppler spectra, the resulting TOPS raw data can be handled by a classic CS processor. Unfortunately, the resulting images would appear back folding, if only a standard stripmap processor is adopted. To avoid or resolve azimuth back folding in the final focused TOPS SAR images, four modified CS algorithms are presented according to special properties of the slow time-frequency diagram in TOPS. Imaging results on point targets validate the presented imaging approaches.
      PubDate: Jan. 2014
      Issue No: Vol. 7, No. 1 (2014)
  • Estimation of the Number of Endmembers Using Robust Outlier Detection
    • Authors: Andreou; C.;Karathanassi, V.;
      Pages: 247 - 256
      Abstract: This paper introduces a novel approach for estimating the numbers of endmembers in hyperspectral imagery. It exploits the geometrical properties of the noise hypersphere and considers the signal as outlier of the noise hypersphere. The proposed method, called outlier detection method (ODM), is automatic and non-parametric. In a principal component space, noise is spherically symmetric in all directions and lies on the surface of a hypersphere with a constant radius. Reversely, signal radiuses are much larger that noise radius and vary in all directions, thus signal lies in a hyperellipsoid. The proposed method involves three steps: 1) noise estimation; 2) minimum noise fraction transformation; and 3) outlier detection using inter quartile range. Estimation of the number of endmembers is accomplished by the estimation of the number of noise hypersphere outliers using a robust outlier detection method. The ODM was evaluated using simulated and real hyperspectral data, and it was also compared with well-known methods for estimating the number of endmembers. Evaluation of the method showed that the method produces robust and satisfactory results, and outperforms in relation to its competitors.
      PubDate: Jan. 2014
      Issue No: Vol. 7, No. 1 (2014)
  • LandEx—A GeoWeb Tool for Query and Retrieval of Spatial Patterns in
           Land Cover Datasets
    • Authors: Stepinski; T.F.;Netzel, P.;Jasiewicz, J.;
      Pages: 257 - 266
      Abstract: The vast amount of data collected by satellites via remote sensing is a valuable resource, however, it lacks machine search capabilities. In particular, large land cover datasets, such as the 30-m/cell NLCD 2006 covering the entire conterminous United States, are rarely analyzed as a whole due to the lack of tools beyond the basic statistics and SQL queries. Consequently, the NLCD is underutilized relative to its potential. We address this issue by introducing LandEx-a GeoWeb application for real time, content-based exploration and mining of land cover patterns in large datasets. By combining the functionality of online computerized maps with the power of the pattern recognition algorithm, LandEx provides an easy to use visual search engine for the entire extent of the NLCD at its full resolution. The user selects a pattern of interest (a query) and the tool produces a similarity map indicating the spatial distribution of locations having patterns of land cover similar to that in the query. Pattern-based query and retrieval addresses the issue of structural similarity between landscapes. The core of the method is the similarity function between two patterns which is based on 2D land cover class/clump size histograms and the Jensen-Shannon divergence. The search relies on exhaustive evaluation using an overlapping sliding window approach. LandEx is implemented using Free Open Source Software (FOSS) software and adheres to the Open Geospatial Consortium (OGC) standards. The wait time for an answer to a query is only several seconds due to the high level of system optimization. The methodology and implementation of LandEx are described in detail and illustrative examples of its application to different domains, including agriculture, forestry, and urbanization are given.
      PubDate: Jan. 2014
      Issue No: Vol. 7, No. 1 (2014)
  • Polarimetric Properties of Burned Forest Areas at C- and L-Band
    • Authors: Tanase; M.A.;Santoro, M.;Aponte, C.;de la Riva, J.;
      Pages: 267 - 276
      Abstract: Fully polarimetric C- and L-band synthetic aperture radar (SAR) data have been investigated to determine the relationship between polarimetric target decomposition components and forest burn severity over two sites located in a Mediterranean environment. The dependence of the polarimetric decomposition metrics on SAR acquisition geometry and environmental conditions was also analyzed at C-band. Multiple linear regression models with interactions (i.e., the incidence angle was included as a predictor variable and its interaction with the radar metrics was accounted for as a multiplicative effect) were used to quantify burn severity retrieval accuracy. According to our experiment, we found that for steep SAR acquisition geometries C-band polarimetric components related to surface scattering mechanisms had increased sensitivity to burn severity levels, while for datasets acquired with more grazing geometries the polarimetric components related to volume scattering and dihedral scattering mechanisms were more correlated with burn severity levels. At L-band only volume and dihedral scattering related decomposition components provided significant relationships with burn severity levels. Relatively low burn severity estimation errors (less than 20% of burn severity range) were obtained for all datasets, with L-band data presenting the highest sensitivity to fire effects. Using a single regression model provided sufficient accuracy for burn severity estimation when taking into account the local incidence angle. The use of fully polarimetric data improved the estimation accuracy of forest burn severity with respect to backscatter intensities by a small margin for our study sites. However, since backscatter intensity metrics already provide high retrieval accuracies, whatever improvement was bound to be low.
      PubDate: Jan. 2014
      Issue No: Vol. 7, No. 1 (2014)
  • Assessment of Soil Moisture Data Requirements by the Potential SMAP Data
           User Community: Review of SMAP Mission User Community
    • Authors: Brown; M.E.;Escobar, V.M.;
      Pages: 277 - 283
      Abstract: NASA's Soil Moisture Active and Passive (SMAP) mission is planned for launch in October 2014 and will provide global measurements of soil moisture and freeze/thaw state. The project is driven by both basic research and applied science goals. Understanding how application driven end-users will apply SMAP data, prior to the satellite's launch, is an important goal of NASA's applied science program and SMAP mission success. Because SMAP data are unique, there are no direct proxy datasets that can be used in research and operational studies to determine how the data will interact with existing processes. The objective of this study is to solicit data requirements, accuracy needs, and current understanding of the SMAP mission from the potential user community. This study showed that the data to be provided by the SMAP mission did substantially meet the user community needs. Although there was a broad distribution of requirements stated, the SMAP mission fit within these requirements.
      PubDate: Jan. 2014
      Issue No: Vol. 7, No. 1 (2014)
  • Mineral Exploration and Alteration Zone Mapping Using Mixture Tuned
           Matched Filtering Approach on ASTER Data at the Central Part of
           Dehaj-Sarduiyeh Copper Belt, SE Iran
    • Authors: Hosseinjani Zadeh; M.;Tangestani, M.H.;Velasco Roldan, F.;Yusta, I.;
      Pages: 284 - 289
      Abstract: This paper focuses on mapping jarosite and different types of alteration minerals for mineral exploration, particularly porphyry copper deposits and discriminating alteration zones with high-potential mineralization from those showing low potentials. The study area is situated at the Central Iranian Volcano-Sedimentary Complex, where the large copper deposits like Sarcheshmeh as well as numerous occurrences of copper exist. The visible near infrared and shortwave infrared (VNIR-SWIR) bands of Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) data were used for mineral mapping. The spectra of diagnostic alteration mineral groups, including sericite-illite, pyrophyllite-alunite, kaolinite-dickite, chlorite-calcite-epidote, and jarosite were extracted from VNIR-SWIR bands of the ASTER imagery. These spectra were used for mineral identification through mixture tuned matched filtering (MTMF) algorithm. Results showed that alteration minerals, and the areas containing jarosite were discriminated from the surrounding districts, which illustrate the ASTER ability to provide information on the occurrence of these minerals. Identification of these areas is valuable for mineral exploration, discrimination of strong pyritization, gossans, and the mine tailings. Results also support the role of MTMF as an effective image processing technique for mineral mapping and exploration.
      PubDate: Jan. 2014
      Issue No: Vol. 7, No. 1 (2014)
  • Testing Different Methods of Forest Height and Aboveground Biomass
           Estimations From ICESat/GLAS Data in Eucalyptus Plantations in Brazil
    • Authors: Baghdadi; N.;le Maire, G.;Fayad, I.;Bailly, J.S.;Nouvellon, Y.;Lemos, C.;Hakamada, R.;
      Pages: 290 - 299
      Abstract: The Geoscience Laser Altimeter System (GLAS) has provided a useful dataset for estimating forest heights in many areas of the globe. Most of the studies on GLAS waveforms have focused on natural forests and only a few were conducted over forest plantations. This work set out to estimate the stand-scale dominant height and aboveground biomass of intensively managed Eucalyptus plantations in Brazil using the most commonly used models developed for natural forests. These forest plantations are valuable case studies, with large and numerous stands that are very uniform, in which field measurements are precise compared to natural forests. The height of planted Eucalyptus forest stands estimated from waveforms acquired by GLAS were compared with in situ measurements in order to determine the model that produced the best forest height estimates. For our slightly sloping study site , the direct method defined as the difference between the signal begin and the ground peak provided forest height estimates with an accuracy of 2.2 m. The use of statistical models based on waveform metrics and digital elevation models provided slightly better results (1.89 m accuracy) in comparison with the direct method and the most relevant metrics proved to be the trailing edge extent and the waveform extent. Moreover, a power law model was used to fit in situ aboveground biomass to in situ forest height. The results using this model with GLAS-derived heights showed an accuracy for biomass of 16.1 Mg/ha.
      PubDate: Jan. 2014
      Issue No: Vol. 7, No. 1 (2014)
  • SAR Image Classification Based on CRFs With Integration of Local Label
           Context and Pairwise Label Compatibility
    • Authors: Yongke Ding;Yuanxiang Li;Wenxian Yu;
      Pages: 300 - 306
      Abstract: Context information plays a critical role in SAR image classification, as high-resolution SAR data provides more information on scene context and visual structures. This paper presents a novel classification method for SAR images based on conditional random fields (CRFs) with integration of low-level features, local label context, and pairwise label compatibility. First, we extract the low-level features used in the SVM-based unary classifier for SAR images. The supertexture is newly introduced as one of the low-level features to model the texture context between image patches. Then, we describe the context information, including local context potential and pairwise potential. Incorporation of the category context helps to resolve the ambiguities of the unary classifier. The performance of our approach in both accuracy and visual appearance for high-resolution SAR image classification is proved in the experiments.
      PubDate: Jan. 2014
      Issue No: Vol. 7, No. 1 (2014)
  • Investigating the Capability of Few Strategically Placed Worldview-2
           Multispectral Bands to Discriminate Forest Species in KwaZulu-Natal, South
    • Authors: Peerbhay; K.Y.;Mutanga, O.;Ismail, R.;
      Pages: 307 - 316
      Abstract: WorldView-2 multispectral wavebands (8 wavebands; 427-908 nm spectral range; 2 m spatial resolution) were utilized to classify six commercial forest species (Eucalyptus grandis, Eucalyptus nitens, Eucalyptus smithii, Pinus patula, Pinus elliotii and Acacia mearnsii) in South Africa using the partial least squares discriminant analysis (PLS-DA) technique. Results indicate that the WorldView-2 imagery produced an overall accuracy of 85.42% and a kappa statistic value of 0.83, with individual forest species accuracies ranging between 63% and 100%. The variable importance in the projection (VIP) method was then used to identify the most important wavebands that were most effective in discriminating the forest species. Four VIP bands were ranked with thresholds greater than one and produced an overall accuracy of 84.38% and kappa value of 0.81, with individual forest species accuracies between 69% and 100%. More specifically, the VIP bands that were found to be important in the classification were the coastal blue (427 nm), blue (478 nm), green (546 nm) and red (659 nm) and confirmed the relative importance of the visible region of the electromagnetic spectrum in discriminating forest species. Overall, results indicate that multispectral information characterized by greater spatial resolution can successfully discriminate between and within forest species, thus providing an accurate framework for commercial forest species mapping.
      PubDate: Jan. 2014
      Issue No: Vol. 7, No. 1 (2014)
  • A Kernel-Based Feature Selection Method for SVM With RBF Kernel for
           Hyperspectral Image Classification
    • Authors: Bor-Chen Kuo;Hsin-Hua Ho;Cheng-Hsuan Li;Chih-Cheng Hung;Jin-Shiuh Taur;
      Pages: 317 - 326
      Abstract: Hyperspectral imaging fully portrays materials through numerous and contiguous spectral bands. It is a very useful technique in various fields, including astronomy, medicine, food safety, forensics, and target detection. However, hyperspectral images include redundant measurements, and most classification studies encountered the Hughes phenomenon. Finding a small subset of effective features to model the characteristics of classes represented in the data for classification is a critical preprocessing step required to render a classifier effective in hyperspectral image classification. In our previous work, an automatic method for selecting the radial basis function (RBF) parameter (i.e., σ) for a support vector machine (SVM) was proposed. A criterion that contains the between-class and within-class information was proposed to measure the separability of the feature space with respect to the RBF kernel. Thereafter, the optimal RBF kernel parameter was obtained by optimizing the criterion. This study proposes a kernel-based feature selection method with a criterion that is an integration of the previous work and the linear combination of features. In this new method, two properties can be achieved according to the magnitudes of the coefficients being calculated: the small subset of features and the ranking of features. Experimental results on both one simulated dataset and two hyperspectral images (the Indian Pine Site dataset and the Pavia University dataset) show that the proposed method improves the classification performance of the SVM.
      PubDate: Jan. 2014
      Issue No: Vol. 7, No. 1 (2014)
  • Indicator Cokriging-Based Subpixel Land Cover Mapping With Shifted Images
    • Authors: Qunming Wang;Wenzhong Shi;Liguo Wang;
      Pages: 327 - 339
      Abstract: Subpixel mapping (SPM) is a technique for predicting the spatial distribution of land cover classes in remote sensing images at a finer spatial resolution level than those of the input images. Indicator cokriging (ICK) has been found to be an effective and efficient SPM method. The accuracy of this model, however, is limited by insufficient constraints. In this paper, the accuracy of the ICK-based SPM model is enhanced by using additional information gained from multiple shifted images (MSIs). First, each shifted image is utilized to compute the conditional probability of class occurrence at any fine spatial resolution pixel (i.e., subpixel) using ICK, and a set of conditional probability maps for all classes are generated for each image. The multiple ICK-derived conditional probability maps are then integrated, according to the estimated subpixel shifts of MSI. Lastly, class allocation at the subpixel scale is implemented to produce SPM results. The proposed algorithm was tested on two synthetic coarse spatial resolution remote sensing images and a set of real Moderate Resolution Imaging Spectroradiometer (MODIS) data. It was evaluated both visually and quantitatively. The experimental results showed that more accurate SPM results can be generated with MSI than with a single observed coarse image in conventional ICK-based SPM. In addition, the accuracy of the proposed method is higher than that of the existing Hopfield neural network (HNN)-based SPM and the HNN with MSI.
      PubDate: Jan. 2014
      Issue No: Vol. 7, No. 1 (2014)
  • Computationally Efficient Method for the Generation of a Digital Terrain
           Model From Airborne LiDAR Data Using Connected Operators
    • Authors: Mongus; D.;Zalik, B.;
      Pages: 340 - 351
      Abstract: This paper proposes a new mapping schema, named Θ mapping, for filtering nonground objects from LiDAR data, and the generation of a digital terrain model. By extending the CSL model, Θ mapping extracts the most contrasted connected-components from top-hat scale-space and attributes them for an adaptive multicriterion filter definition. Areas of the most contrasted connected-components and the standard deviations of contained points' levels are considered for this purpose. Computational efficiency is achieved by arranging the input LiDAR data into a grid, represented by a Max-Tree. Since a constant number of passes over the grid is required, the time complexity of the proposed method is linear according to the number of grid-cells. As confirmed by the experiments, the average CPU execution time decreases by nearly 98%, while the average accuracy improves by up to 10% in comparison with the related method.
      PubDate: Jan. 2014
      Issue No: Vol. 7, No. 1 (2014)
  • Fast Compressed Sensing SAR Imaging Based on Approximated Observation
    • Authors: Jian Fang;Zongben Xu;Bingchen Zhang;Wen Hong;Yirong Wu;
      Pages: 352 - 363
      Abstract: In recent years, compressed sensing (CS) has been applied in the field of synthetic aperture radar (SAR) imaging and shows great potential. The existing models are, however, based on application of the sensing matrix acquired by the exact observation functions. As a result, the corresponding reconstruction algorithms are much more time consuming than traditional matched filter (MF)-based focusing methods, especially in high resolution and wide swath systems. In this paper, we formulate a new CS-SAR imaging model based on the use of the approximated SAR observation deducted from the inverse of focusing procedures. We incorporate CS and MF within an sparse regularization framework that is then solved by a fast iterative thresholding algorithm. The proposed model forms a new CS-SAR imaging method that can be applied to high-quality and high-resolution imaging under sub-Nyquist rate sampling, while saving the computational cost substantially both in time and memory. Simulations and real SAR data applications support that the proposed method can perform SAR imaging effectively and efficiently under Nyquist rate, especially for large scale applications.
      PubDate: Jan. 2014
      Issue No: Vol. 7, No. 1 (2014)
  • An Evaluation of Models for the Satellite-Estimation of Phytoplankton
           Absorption Coefficients in Coastal/ Oceanic Waters
    • Authors: Tiwari; S.P.;Shanmugam, P.;
      Pages: 364 - 371
      Abstract: Several semi-analytical models for the satellite-based estimation of absorption coefficients of phytoplankton aph (λ) have been used to routinely produce aph (λ) product from satellite ocean color data. However, these models are generally applicable for clear ocean waters where they produce aph (λ) values only at a few wavelengths in the blue-green domain; this causes the main difficulty in making these models more usable with any suite of wavelengths. Further, recent studies have shown the performance of these models to be highly questionable in optically complex waters. This emphasized the need for developing a more accurate model for the satellite-based estimation of aph (λ) in a wide range of oceanic waters. In our previous study, we developed an empirical model (hereafter referred as Tiwari-Shanmugam model - “TS model”) based on the relationship of the in situ remote sensing reflectance ratio Rrs (670)/Rrs (490) and in situ aph(λ) which is best fit to a third order polynomial. In the present study, we rigorously test this model along with three global inversion models (e.g., Constrained Linear Matrix (LM) model, Quasi-analytical algorithm (QAA), and GSM semi-analytical model which are often used by the ocean color community) using three independent in situ data sets from clear to turbid coastal waters and satellite match-ups data from global waters. When applied to these data sets, the TS model produces more accurate aph values across the entire visible wavelengths (400-700 nm) in all these waters, whereas LM, QAA and GSM models yield significant errors in addition to being restricted to produce aph values only in the blue-green wavelengths (LM and GSM). Though the TS model is mathematically simple, it overcomes such limitations and yields excellent results in terms of reproducing measured aph spe- tra that are highly desired by the ocean color community for inputting in various bio-optical models and studying the spatial structure of the different phytoplankton communities from satellite remote sensing observations in global waters.
      PubDate: Jan. 2014
      Issue No: Vol. 7, No. 1 (2014)
  • IEEE Open Access Publishing
    • Pages: 372 - 372
      Abstract: Advertisement: This publication offers open access options for authors. IEEE open access publishing.
      PubDate: Jan. 2014
      Issue No: Vol. 7, No. 1 (2014)
School of Mathematical and Computer Sciences
Heriot-Watt University
Edinburgh, EH14 4AS, UK
Tel: +00 44 (0)131 4513762
Fax: +00 44 (0)131 4513327
About JournalTOCs
News (blog, publications)
JournalTOCs on Twitter   JournalTOCs on Facebook

JournalTOCs © 2009-2014