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  Subjects -> ELECTRONICS (Total: 138 journals)
Advances in Biosensors and Bioelectronics     Open Access   (Followers: 2)
Advances in Magnetic and Optical Resonance     Full-text available via subscription   (Followers: 5)
Advances in Microelectronic Engineering     Open Access   (Followers: 1)
Advances in Power Electronics     Open Access   (Followers: 8)
Aerospace and Electronic Systems, IEEE Transactions on     Hybrid Journal   (Followers: 58)
American Journal of Electrical and Electronic Engineering     Open Access   (Followers: 9)
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: 8)
Biomedical Engineering, IEEE Reviews in     Full-text available via subscription   (Followers: 15)
Biomedical Engineering, IEEE Transactions on     Hybrid Journal   (Followers: 12)
Biomedical Instrumentation & Technology     Full-text available via subscription   (Followers: 4)
Broadcasting, IEEE Transactions on     Hybrid Journal   (Followers: 5)
BULLETIN of National Technical University of Ukraine. Series RADIOTECHNIQUE. RADIOAPPARATUS BUILDING     Open Access   (Followers: 1)
Bulletin of the Polish Academy of Sciences : Technical Sciences     Open Access  
Canadian Journal of Remote Sensing     Full-text available via subscription   (Followers: 13)
China Communications     Full-text available via subscription   (Followers: 1)
Circuits and Systems     Open Access   (Followers: 8)
Consumer Electronics Times     Open Access   (Followers: 4)
Control Systems     Hybrid Journal   (Followers: 20)
Electronic Markets     Hybrid Journal   (Followers: 5)
Electronic Materials Letters     Hybrid Journal   (Followers: 3)
Electronics     Open Access   (Followers: 7)
Electronics and Communications in Japan     Hybrid Journal   (Followers: 5)
Electronics Letters     Hybrid Journal   (Followers: 17)
Embedded Systems Letters, IEEE     Hybrid Journal   (Followers: 18)
EPJ Quantum Technology     Open Access  
EURASIP Journal on Embedded Systems     Open Access   (Followers: 8)
Foundations and TrendsĀ® in Communications and Information Theory     Full-text available via subscription   (Followers: 6)
Foundations and TrendsĀ® in Signal Processing     Full-text available via subscription   (Followers: 4)
Frequenz     Full-text available via subscription   (Followers: 2)
Frontiers of Optoelectronics     Hybrid Journal   (Followers: 2)
Geoscience and Remote Sensing, IEEE Transactions on     Hybrid Journal   (Followers: 20)
Haptics, IEEE Transactions on     Hybrid Journal   (Followers: 4)
IEEE Consumer Electronics Magazine     Full-text available via subscription   (Followers: 11)
IEEE Journal of Emerging and Selected Topics in Power Electronics     Hybrid Journal   (Followers: 7)
IEEE Journal of the Electron Devices Society     Open Access   (Followers: 2)
IEEE Power Electronics Magazine     Full-text available via subscription   (Followers: 5)
IEEE Transactions on Audio, Speech, and Language Processing     Hybrid Journal   (Followers: 10)
IEEE Transactions on Automatic Control     Hybrid Journal   (Followers: 20)
IEEE Transactions on Consumer Electronics     Hybrid Journal   (Followers: 13)
IEEE Transactions on Electron Devices     Hybrid Journal   (Followers: 6)
IEEE Transactions on Information Theory     Hybrid Journal   (Followers: 13)
IEEE Transactions on Power Electronics     Hybrid Journal   (Followers: 16)
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 Power Electronics     Hybrid Journal   (Followers: 9)
IET Wireless Sensor Systems     Hybrid Journal   (Followers: 6)
IETE Journal of Education     Open Access   (Followers: 2)
IETE Journal of Research     Open Access   (Followers: 5)
IETE Technical Review     Open Access   (Followers: 1)
Industrial Electronics, IEEE Transactions on     Hybrid Journal   (Followers: 11)
Industry Applications, IEEE Transactions on     Hybrid Journal   (Followers: 5)
Informatik-Spektrum     Hybrid Journal  
Instabilities in Silicon Devices     Full-text available via subscription  
Intelligent Transportation Systems Magazine, IEEE     Full-text available via subscription   (Followers: 1)
International Journal of Advanced Electronics and Communication Systems     Open Access   (Followers: 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: 11)
International Journal of Applied Electronics in Physics & Robotics     Open Access   (Followers: 1)
International Journal of Biomedical Nanoscience and Nanotechnology     Hybrid Journal   (Followers: 5)
International Journal of Computational Vision and Robotics     Hybrid Journal   (Followers: 4)
International Journal of Computer & Electronics Research     Full-text available via subscription   (Followers: 2)
International Journal of Control     Hybrid Journal   (Followers: 11)
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: 2)
International Journal of Nanoscience     Hybrid Journal   (Followers: 1)
International Journal of Numerical Modelling:Electronic Networks, Devices and Fields     Hybrid Journal   (Followers: 2)
International Journal of Power Electronics     Hybrid Journal   (Followers: 4)
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 Systems, Control and Communications     Hybrid Journal   (Followers: 2)
International Journal on Communication     Full-text available via subscription   (Followers: 8)
International Journal on Electrical and Power Engineering     Full-text available via subscription   (Followers: 11)
ISRN Electronics     Open Access   (Followers: 1)
ISRN Signal Processing     Open Access  
Journal of Advanced Dielectrics     Open Access   (Followers: 1)
Journal of Artificial Intelligence     Open Access   (Followers: 5)
Journal of Circuits, Systems, and Computers     Hybrid Journal   (Followers: 1)
Journal of Electrical and Electronics Engineering Research     Open Access   (Followers: 2)
Journal of Electrical Bioimpedance     Full-text available via subscription   (Followers: 2)
Journal of Electrical Engineering & Electronic Technology     Full-text available via subscription   (Followers: 1)
Journal of Electromagnetic Analysis and Applications     Open Access   (Followers: 3)
Journal of Electromagnetic Waves and Applications     Hybrid Journal   (Followers: 2)
Journal of Electronic Design Technology     Full-text available via subscription   (Followers: 2)
Journal of Electronics (China)     Hybrid Journal   (Followers: 2)
Journal of Field Robotics     Hybrid Journal   (Followers: 2)
Journal of Guidance, Control, and Dynamics     Full-text available via subscription   (Followers: 55)
Journal of Intelligent Procedures in Electrical Technology     Open Access  
Journal of Low Power Electronics     Full-text available via subscription   (Followers: 4)
Journal of Low Power Electronics and Applications     Open Access   (Followers: 1)

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Selected Topics in Applied Earth Observations and Remote Sensing, IEEE Journal of
   Journal TOC RSS feeds Export to Zotero [18 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  [174 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: Aug. 2014
      Issue No: Vol. 7, No. 8 (2014)
       
  • IEEE Transactions on Geoscience and Remote Sensing institutional listings
    • Pages: C4 - C4
      Abstract: Advertisement.
      PubDate: Aug. 2014
      Issue No: Vol. 7, No. 8 (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: Aug. 2014
      Issue No: Vol. 7, No. 8 (2014)
       
  • Front cover
    • Pages: C1 - C1
      Abstract: Presents the front cover for this issue of the publication.
      PubDate: Aug. 2014
      Issue No: Vol. 7, No. 8 (2014)
       
  • Table of contents
    • Pages: 3185 - 3186
      Abstract: Presents the table of contents for this issue of this publication.
      PubDate: Aug. 2014
      Issue No: Vol. 7, No. 8 (2014)
       
  • Foreword to the Special Issue on the Analysis of Multitemporal Remote
           Sensing Images
    • Authors: Piwowar; J.M.;Ban, Y.;McDermid, G.;Bruzzone, L.;
      Pages: 3187 - 3189
      Abstract: This special issue contains 19 of the best papers presented at the 7th International Workshop on the Analysis of Multitemporal Remote Sensing Images (MultiTemp 2013) as well as 13 multitemporal analysis papers that were received from a general call for papers published in JSTARS.
      PubDate: Aug. 2014
      Issue No: Vol. 7, No. 8 (2014)
       
  • Monitoring Seasonal Changes in the Water Surface Areas of Reservoirs Using
           TerraSAR-X Time Series Data in Semiarid Northeastern Brazil
    • Authors: Heine; I.;Francke, T.;Rogass, C.;Medeiros, P.H.A.;Bronstert, A.;Foerster, S.;
      Pages: 3190 - 3199
      Abstract: The ${bf 933}~hbox{bf km}^{bf 2}$ Benguê catchment in northeastern Brazil is characterized by distinct rainy and dry seasons. Precipitation is stored in variously sized reservoirs, which is essential for the local population. In this study, we used TerraSAR-X SM (HH) data for an one-year monitoring of seasonal changes in the reservoir areas from July 2011 to July 2012. The monitoring was based on acquisitions in the ascending pass direction, complemented by occasional descending-pass images. To detect water surface areas, a histogram analysis followed by a global threshold classification was performed, and the results were validated using in situ GPS data. Distinguishing between small reservoirs and similar looking dark areas was difficult. Therefore, we tested several approaches for identifying misclassified areas. An analysis of the surface area dynamics of the reservoirs indicated high spatial and temporal heterogeneities and a large decrease in the total water surface area of the reservoirs in the catchment by approximately 30% within one year.
      PubDate: Aug. 2014
      Issue No: Vol. 7, No. 8 (2014)
       
  • Waterline Mapping and Change Detection of Tangjiashan Dammed Lake After
           Wenchuan Earthquake From Multitemporal High-Resolution Airborne SAR
           Imagery
    • Authors: Li; N.;Wang, R.;Deng, Y.;Chen, J.;Liu, Y.;Du, K.;Lu, P.;Zhang, Z.;Zhao, F.;
      Pages: 3200 - 3209
      Abstract: Several dammed lakes caused by landslides and rock avalanches appeared after Wenchuan earthquake. Tangjiashan dammed lake was the largest one among all of them, which resulted in obvious threats to people around. In this paper, multitemporal high-resolution airborne synthetic aperture radar (SAR) images are used to map the waterline of Tangjiashan dammed lake and its change continuously, which is meaningful for flooding assessment and prediction. An approach combining different techniques is proposed for dammed lake segmentation. In this approach, subpatched intensity thresholding segmentation, morphological operators, and local Gaussian distribution fitting energy (LGDF)-based active contours model (ACM) are used in combination to get the final waterline. Flood-affected area is detected and its size is calculated for each segmental result. In addition, the variations of the flood-affected area are also obtained based on the difference of the above-mentioned segmental results. The proposed approach is tested on real SAR imagery acquired from three different dates in May, 2008 with high-temporal resolution in the same area containing Tangjiashan dammed lake. Experimental results demonstrate the effectiveness of the proposed approach.
      PubDate: Aug. 2014
      Issue No: Vol. 7, No. 8 (2014)
       
  • Intertidal Topographic Maps and Morphological Changes in the German Wadden
           Sea between 1996–1999 and 2006–2009 from the Waterline
           Method and SAR Images
    • Authors: Li; Z.;Heygster, G.;Notholt, J.;
      Pages: 3210 - 3224
      Abstract: The Dutch, German, and Danish Wadden Sea is one of the largest tidal flats in the world which has been left in a nearly natural state. Several studies generated the topography of this valuable area, but presented only single maps, which covered partial regions of the Wadden Sea. The objectives of this paper are 1) to use the waterline method to analyze SAR images in order to generate annual topographic maps of the northern parts of the German Wadden Sea for the years 1996–1999 and 2006–2009 and 2) to quantitatively estimate and monitor the development of the tidal flats. The waterlines are detected by a wavelet-based edge detection algorithm, geocoded in Gauss–Krueger coordinates, assigned with water level information, and interpolated into topographic maps. Our results show the development of the tidal flats over the 8 years and illustrate three of the most changing regions: Tertiussand, Gelbsand, and Medemsand. Their area, shape, and sediment volume have changed significantly due to the tide, wave, wind, and river discharge influences. The waterline method is an efficient and economical way to monitor large tidal flat areas, which allows an estimation of long-term morphological development. One potential application is to assimilate the results into morphodynamic models in order to give finer morphological predictions.
      PubDate: Aug. 2014
      Issue No: Vol. 7, No. 8 (2014)
       
  • Multi-temporal Synthetic Aperture Radar Metrics Applied to Map Open Water
           Bodies
    • Authors: Santoro; M.;Wegmuller, U.;
      Pages: 3225 - 3238
      Abstract: Multi-temporal synthetic aperture radar (SAR) metrics are assessed to map open water bodies. High temporal variability and low minimum value in a time series of Envisat Advanced SAR (ASAR) Wide Swath Mode (WSM) backscatter measurements characterize open water bodies with respect to other land cover types. Confusion occurs in the case of steep terrain (slope angle> 10°), less than 10 backscatter observations and for mixed pixels with a water fraction. The behavior of the two SAR multi-temporal metrics is consistent at six study areas in Europe and Central Siberia. A simple thresholding algorithm applied to the multi-temporal SAR metrics to map open water bodies performs with overall accuracies above 90% in the case of pure pixels of water or land. The accuracy decreases when mixed pixels are accounted for in the reference dataset and for increasing land fraction in the reference samples. An overall accuracy of approximately 80% was obtained for a 50% threshold of the water fraction. Omissions of water areas occur mostly along shorelines. Specific conditions of the land surface can distort the minimum, causing commission in the water class. The use of a low order rank or percentile instead of the lowest backscatter value can reduce such commission error.
      PubDate: Aug. 2014
      Issue No: Vol. 7, No. 8 (2014)
       
  • Detection of Multitransition Abrupt Changes in Multitemporal SAR Images
    • Authors: Dogan; O.;Perissin, D.;
      Pages: 3239 - 3247
      Abstract: This paper addresses the detection of abrupt and step-wise changes in multitemporal sequences of synthetic aperture radar (SAR) images. Specifically, the major motivation is to deal with the change detection of urban features with multitemporal step patterns. In contrast to most techniques presented in the past, that only use couples of adjacent images, in this work multi-SAR images are used for the change detection purpose. The proposed method is appropriate for the generation of new SAR satellite systems that has a high revisit time, generating many successive image of the same area. We attacked on two aspects of the problem: detection of multi-transition instants (TIs) of the SAR amplitude time series and detection of the probable changes. For this purpose, the analysis of variance (ANOVA), technique is used. ANOVA achieves a representation of the relation of all group changes. The features extracted by ANOVA are utilized for the supervised classification procedure. The proposed technique provides promising results for the classification of abrupt changes like in urban areas.
      PubDate: Aug. 2014
      Issue No: Vol. 7, No. 8 (2014)
       
  • Unsupervised Change Detection in Multitemporal SAR Images Over Large Urban
           Areas
    • Authors: Hu; H.;Ban, Y.;
      Pages: 3248 - 3261
      Abstract: Unsupervised change detection in multitemporal single-polarization synthetic aperture radar (SAR) images often involves thresholding of the image change indicator. If one class, which is usually the unchanged class, comprises a disproportionately large part of the scene, the image change indicator may have a unimodal histogram. Image thresholding of such a change indicator is a challenging task. In this paper, we present an automatic and effective approach to the thresholding of the log-ratio change indicator whose histogram may have one mode or more than one mode. A bimodality test is performed to determine whether the histogram of the log-ratio image is unimodal or not. If it has more than one mode, the generalized Kittler and Illingworth thresholding (GKIT) algorithm based on the generalized Gaussian model (GG-GKIT) is used to detect the optimal threshold values. If it is unimodal, the log-ratio image is divided into small regions and a multiscale region selection process is carried out to select regions which are a balanced mixture of unchanged and changed classes. The selected regions are combined to generate a new histogram. The optimal threshold value obtained from the new histogram is then used to separate unchanged pixels from changed pixels in the log-ratio image. Experimental results obtained on multitemporal SAR images of Toronto and Beijing demonstrate the effectiveness of the proposed approach.
      PubDate: Aug. 2014
      Issue No: Vol. 7, No. 8 (2014)
       
  • Impact of Topographic Correction on Estimation of Aboveground Boreal
           Biomass Using Multi-temporal, L-Band Backscatter
    • Authors: Atwood; D.K.;Andersen, H.-E.;Matthiss, B.;Holecz, F.;
      Pages: 3262 - 3273
      Abstract: Synthetic aperture radar (SAR) has been shown to be a useful tool for estimating aboveground biomass (AGB), due to the strong correlation between the biomass and backscatter. In particular, L-band SAR is effective for estimating the lower range of biomass that characterizes most boreal forests. Unfortunately, the topographic impact on backscatter can dominate the normal forest signal variation. Since many boreal environments have significant topography, we investigate several topographic correction techniques to determine their effect upon AGB prediction accuracy. Different approaches to addressing the topography include: 1) no correction, 2) local incidence angle (LIA) correction, 3) pixel-area correction, and 4) a novel empirical slope correction. The investigation was performed for a data-rich experimental area near Tok, Alaska, for which Advanced Land Observing Satellite Phased Array type L-Band Synthetic Aperture Radar (ALOS PALSAR), field plots, lidar acquisitions, and a high-quality digital elevation model (DEM) existed. Biomass estimations were performed using both single- and dual-polarization (HH and HV) regressions against field plot data. The biomass estimation for each of the topographic corrections was compared with the field plot biomass, as well as more extensive lidar biomass estimations. The results showed a clear improvement in AGB estimation accuracy from no correction, to LIA, to pixel-area, to the novel pixel-area plus empirical slope correction. Using the field plot data for validation, the SAR root mean square error (RMSE) derived from the best approach was found to be 37.3 Mg/ha over a biomass range of 0–250 Mg/ha, only marginally less accurate than the 33.5 Mg/ha accuracy of the much more expensive lidar technique.
      PubDate: Aug. 2014
      Issue No: Vol. 7, No. 8 (2014)
       
  • Observation of the Argentière Glacier Flow Variability from 2009 to
           2011 by TerraSAR-X and GPS Displacement Measurements
    • Authors: Ponton; F.;Trouve, E.;Gay, M.;Walpersdorf, A.;Fallourd, R.;Nicolas, J.;Vernier, F.;Mugnier, J.;
      Pages: 3274 - 3284
      Abstract: In this paper, 3 years of surface displacement measurements obtained by space-borne synthetic aperture radar (SAR) observations are presented over the Argentière glacier in the Mont-Blanc massif, France. This temperate glacier is instrumented by a network of four Global Positioning System (GPS) stations used as ground truth. Thirty-eight pairs of descending and ascending high-resolution TerraSAR-X (TSX) acquisitions covering the study region are used to derive displacement fields at 11-day intervals in spring and summer 2009 and summer 2011. The combination of ascending and descending pairs acquired over the same period allows 3-D displacement fields to be inverted. Our SAR analysis quantifies displacement rates from 10 cm/day at the altitude of 2600 m to 30 cm/day at the altitude of 1800 m. Time series of SAR displacement results are compared with in situ GPS measurements of a continuous station set up at the altitude of 2441 m. Both data sources present displacement of the same order of magnitude with an average value of 20 cm/day in 3-D and show intra-seasonal variabilities, with fast accelerations over short time intervals.
      PubDate: Aug. 2014
      Issue No: Vol. 7, No. 8 (2014)
       
  • SBAS-DInSAR Parallel Processing for Deformation Time-Series Computation
    • Authors: Casu; F.;Elefante, S.;Imperatore, P.;Zinno, I.;Manunta, M.;De Luca, C.;Lanari, R.;
      Pages: 3285 - 3296
      Abstract: The aim of this paper is to design a novel parallel computing solution for the processing chain implementing the Small BAseline Subset (SBAS) Differential SAR Interferometry (DInSAR) technique. The proposed parallel solution (P-SBAS) is based on a dual-level parallelization approach and encompasses combined parallelization strategies, which are fully discussed in this paper. Moreover, the main methodological aspects of the proposed approach and their implications are also addressed. Finally, an experimental analysis, aimed at quantitatively evaluating the computational efficiency of the implemented parallel prototype, with respect to appropriate metrics, has been carried out on real data; this analysis confirms the effectiveness of the proposed parallel computing solution. In the current scenario, characterized by huge SAR archives relevant to the present and future SAR missions, the P-SBAS processing chain can play a key role to effectively exploit these big data volumes for the comprehension of the surface deformation dynamics of large areas of Earth.
      PubDate: Aug. 2014
      Issue No: Vol. 7, No. 8 (2014)
       
  • Unsupervised Change Detection in SAR Image Based on Gauss-Log Ratio Image
           Fusion and Compressed Projection
    • Authors: Hou; B.;Wei, Q.;Zheng, Y.;Wang, S.;
      Pages: 3297 - 3317
      Abstract: Multitemporal synthetic aperture radar (SAR) images have been successfully used for the detection of different types of terrain changes. SAR image change detection has recently become a challenge problem due to the existence of speckle and the complex mixture of terrain environment. This paper presents a novel unsupervised change detection method in SAR images based on image fusion strategy and compressed projection. First, a Gauss-log ratio operator is proposed to generate a difference image. In order to obtain a better difference map, image fusion strategy is applied using complementary information from Gauss-log ratio and log-ratio difference image. Second, nonsubsampled contourlet transform (NSCT) is used to reduce the noise of the fused difference image, and compressed projection is employed to extract feature for each pixel. The final change detection map is obtained by partitioning the feature vectors into “changed” and “unchanged” classes using simple k-means clustering. Experiment results show that the proposed method is effective for SAR image change detection in terms of shape preservation of the detected change portion and the numerical results.
      PubDate: Aug. 2014
      Issue No: Vol. 7, No. 8 (2014)
       
  • Change Detection in High-Resolution SAR Images Based on
           Jensen–Shannon Divergence and Hierarchical Markov Model
    • Authors: Yang; W.;Song, H.;Huang, X.;Xu, X.;Liao, M.;
      Pages: 3318 - 3327
      Abstract: This paper addresses the problem of change detection in high-resolution multitemporal synthetic aperture radar (SAR) images. We propose to use Jensen–Shannon divergence (JSD) to measure the dissimilarity of the two scenes acquired at different times for deriving the difference map (DM). We figure out this divergence in a nonparametric way by introducing a direct density ratio estimation, making the DM generation free of distribution assumption. We also present a multiscale change detection framework which can capture and combine change cues at different scales. First, the coregistered SAR image pairs are decomposed into different scales by multiscale decimated wavelet transform (DWT). Next, the DM in each scale is generated by computing the local JSD. These DMs are then represented by a hierarchical Markovian model based on a quad-tree structure. The change map is finally inferred relying on hierarchical marginal posterior mode (HMPM). Experimental results on multitemporal TerraSAR-X images demonstrate the effectiveness of the proposed approach.
      PubDate: Aug. 2014
      Issue No: Vol. 7, No. 8 (2014)
       
  • Estimating Ambiguity-Free Motion Parameters of Ground Moving Targets From
           Dual-Channel SAR Sensors
    • Authors: Zhu; S.;Liao, G.;Tao, H.;Yang, Z.;
      Pages: 3328 - 3349
      Abstract: In this paper, we propose an approach for ambiguity-free motion parameter estimation of ground moving targets with arbitrary velocity and constant acceleration from dual-channel synthetic aperture radar (SAR) sensors. For targets with high and low signal-to-clutter-noise ratios (SCNRs), different analytic expressions for along/cross-track velocities and accelerations estimation are derived, respectively. The ambiguities induced by the motion parameters in practical applications are analyzed in detail and appropriate solutions are proposed. We use the $n$ th-order keystone transform to mitigate the range migration induced by the target’s residual motion after the ambiguity removal. The effectiveness of the proposed algorithm is verified by processing both simulated and measured stripmap SAR data.
      PubDate: Aug. 2014
      Issue No: Vol. 7, No. 8 (2014)
       
  • On the Day of Observation in Image Composites and Its Impact on Time
           Series
    • Authors: Colditz; R.R.;
      Pages: 3350 - 3357
      Abstract: Many ecological and agricultural studies require accurate information about the phenological state of the vegetation such as the start, peak/plateau, and end of the growing season. This study investigates the impact of the day of observation in image composites on time-series generation and analysis. It employs daily Moderate Resolution Imaging Spectroradiometer (MODIS) surface reflectance and 16-day vegetation index (VI) composites and combines data streams from Terra and Aqua. Results of a time series that take into account the day of observation and time series that assume the starting, ending, or middle day of the compositing period were compared to a reference of daily observations. A temporal shift of approximately 7.5 days with a high variation in error is introduced if the start of the compositing period is assumed. The middle day mitigates the mean error close to zero but cannot fully compensate for temporal delays. Only a time series that takes into account the actual day of observation can be used for the correct estimation of temporal characteristics. In addition, the study addresses that the day of observation for time-series generation from image composites is imperative when combining data of different data streams with phased production cycles such as MODIS VI composites from Terra and Aqua.
      PubDate: Aug. 2014
      Issue No: Vol. 7, No. 8 (2014)
       
  • Detecting Change Areas in Mexico Between 2005 and 2010 Using 250 m MODIS
           Images
    • Authors: Colditz; R.R.;Llamas, R.M.;Ressl, R.A.;
      Pages: 3358 - 3372
      Abstract: The goal of the North American Land Change Monitoring System (NALCMS) is to provide annually updated land cover maps for the North American continent using satellite information and automated data processing. Current activities of the project aim at the development of an automated algorithm to detect areas of change using 250 m MODIS data. This paper shows the methodology developed for Mexico and demonstrates the resulting change map between the years 2005 and 2010. A data-driven algorithm that builds upon the spectral differences of monthly image composites was developed and critical parameters were defined. Results show that only extreme values of difference images indicate change and that change has to be mapped in at least 25% of all features. The total area of change detected between 2005 and 2010 was 702,331 ha (0.36% of the country) which is in line with other change detection studies in Mexico. Accuracy assessment using higher spatial resolution images accounts for the change fraction in the reference data. The overall accuracy of the change/no change mask is approximately 80%. This is similar to decision tree-based change classification that was developed in other studies and applied to Mexico and significantly better than post-classification change detection. The main limitation is the coarse spatial resolution considering the small-patch landscape structure for large portions of the country, which results in a high omission error (50%) but only 20% commission error for change.
      PubDate: Aug. 2014
      Issue No: Vol. 7, No. 8 (2014)
       
  • Mapping Asian Cropping Intensity With MODIS
    • Authors: Gray; J.;Friedl, M.;Frolking, S.;Ramankutty, N.;Nelson, A.;Gumma, M.K.;
      Pages: 3373 - 3379
      Abstract: Agricultural systems are geographically extensive, have profound significance to society, and affect regional energy, climate, and water cycles. Since most suitable lands worldwide have been cultivated, there is a growing pressure to increase yields on existing agricultural lands. In tropical and subtropical regions, multicropping is widely used to increase food production, but regional-to-global information related to multicropping practices is poor. The high temporal resolution and moderate spatial resolution of the MODIS sensors provide an ideal source of information for characterizing cropping practices over large areas. Relative to studies that document agricultural extensification, however, systematic assessment of agricultural intensification via multicropping has received relatively little attention. The goal of this work was to help close this information gap by developing methods that use multitemporal remote sensing to map multicropping systems in Asia. Image time-series analysis is especially challenging in this part of the world because atmospheric conditions including clouds and aerosols lead to high frequencies of missing or low-quality observations, especially during the Asian Monsoon. The methodology that we developed builds upon the algorithm used to produce the MODIS Land Cover Dynamics product (MCD12Q2), but uses an improved methodology optimized for crops. We assessed our results at the aggregate scale using state, district, and provincial level inventory statistics reporting total cropped and harvested areas, and at the field scale using survey results for 191 field sites in Bangladesh. While the algorithm highlighted the dominant continental-scale patterns in agricultural practices throughout Asia, and produced reasonable estimates of state and provincial level total harvested areas, field-scale assessment revealed significant challenges in mapping high cropping intensity due to abundant missing data.
      PubDate: Aug. 2014
      Issue No: Vol. 7, No. 8 (2014)
       
  • Monitoring Cumulative Long-Term Vegetation Changes Over the Athabasca Oil
           Sands Region
    • Authors: Latifovic; R.;Pouliot, D.;
      Pages: 3380 - 3392
      Abstract: This study uses two remotely sensed vegetation indices to investigate cumulative long-term changes of undisturbed vegetation in the Athabasca Oil Sands region of Alberta, Canada, between 1984 and 2012. The Normalized Difference Vegetation Index (NDVI) and the Normalized Difference Wetness Index (NDWI) were derived from both Landsat and MODIS time series, for comparative purposes and to increase confidence in detected trends. Trend analysis of undisturbed forest areas, i.e., area without abrupt changes revealed a consistent decrease in vegetation condition, quantified by an average reduction of 18.6% ( ${bf SD} = {bf 5.02%} $ ) in NDVI and of 31.0% ( ${bf SD} = {bf 10.06%} $ ) in NDWI, over the 28-year period. The study does not conclusively associate the trends with any single stressor, but seeks to quantify the spatial and temporal distribution of cumulative effects resulting from a variety of natural and anthropogenic causes. Examination of the temporal pattern of trends showed an increase in the occurrence of decreasing trends in the last 10 years. The decreasing trends were more frequent closer to mining developments for both the Landsat and MODIS time series. Climate change was not considered a major causal factor as climate normalized trends had little effect on the results. The trend analysis undertaken can be used to enhance in situ monitoring programs for site selection of additional monitoring facilities particularly regarding potential cumulative effects, provide an indication of likely future short-term changes in the region, and to aid in the development of mitigation measures.
      PubDate: Aug. 2014
      Issue No: Vol. 7, No. 8 (2014)
       
  • Thermal Monitoring of Eyjafjöll Volcano Eruptions by Means of
           Infrared MODIS Data
    • Authors: Lacava; T.;Marchese, F.;Arcomano, G.;Coviello, I.;Falconieri, A.;Faruolo, M.;Pergola, N.;Tramutoli, V.;
      Pages: 3393 - 3401
      Abstract: In the evening of 20 March 2010, after about two centuries of quiescence, an effusive eruption took place at Eyjafjöll (Iceland) volcano, from a small vent localized on the northeast flank (Fimmvörduháls Pass) of the volcano edifice. On 31 March, a new eruptive fissure opened on the same region emitting lava. About 2 weeks later, on 14 April, a strong explosive eruption took place under the Eyjafjallajökull glacier, injecting copious amounts of ash in the atmosphere and causing an unprecedented air traffic disruption in Northern and Central Europe. In this paper, the changes in thermal signals occurring at Eyjafjöll volcano during 1 March–20 April 2010 are investigated, testing the ${bf RST}_{bf VOLC}$ algorithm for the first time in a subpolar environment. Outcomes of this retrospective study, performed by means of infrared Moderate Resolution Imaging Spectroradiometer (MODIS) data, show that both effusive and explosive eruptions of the Eyjafjöll volcano could be identified in a timely manner and well monitored from space. Moreover, in spite of a lack of pre-eruptive hot spots detection, this paper reveals a general increasing trend of the middle infrared signal at crater area, beginning 2 weeks before the explosion, stimulating and suggesting further investigations devoted to better characterize the thermal behavior of the monitored volcano.
      PubDate: Aug. 2014
      Issue No: Vol. 7, No. 8 (2014)
       
  • Multitemporal Wetland Monitoring in Sub-Saharan West-Africa Using Medium
           Resolution Optical Satellite Data
    • Authors: Moser; L.;Voigt, S.;Schoepfer, E.;Palmer, S.;
      Pages: 3402 - 3415
      Abstract: Surface water is a critical resource in semiarid West-African regions that are frequently exposed to droughts. Natural and artificial wetlands are of high importance for different livelihoods, particularly during the dry season, from October/November until May. However, wetlands largely go unmonitored. In this work, remote sensing is used to monitor wetlands in semiarid Burkina Faso over large areal extents along a gradient of different rainfall and land use characteristics. Time series of data from the Moderate Resolution Imaging Spectrometer (MODIS) from 2000 to 2012 is used for near-infrared (NIR)-based water monitoring using a latitudinal threshold gradient approach. The occurrence of 21 new water bodies with a size larger than ${bf{ 0.5} ~{bf km}^{bf 2}} $ over the 13-year analysis period results from a postclassification change detection. Yearly cumulative spatiotemporal analysis shows lower water extents in the drought seasons of 2000–2001, 2004–2005, and 2011–2012. Multiple wetlands indicate a positive trend toward a larger yearly maximum area, but a negative trend toward shorter flooding duration. Such a negative trend is observed particularly for natural wetlands. The temporal behavior of five selected case studies demonstrates that monthly negative anomalies of water-covered areas coincide with the occurrence of drought seasons. The successful application of remote sensing time series as a tool to monitor wetlands in semiarid regions is presented, and the potential of novel early warning indicators of drought from remote sensing is demonstrated.
      PubDate: Aug. 2014
      Issue No: Vol. 7, No. 8 (2014)
       
  • Thermophysical Characterization of the Southwestern U.S. From 5 Years of
           MODIS Land Surface Temperature Observations
    • Authors: Nowicki; S.A.;
      Pages: 3416 - 3420
      Abstract: Five years (2005–2009) of daytime and nighttime 1-km 8-day Moderate Resolution Imaging Spectroradiometer (MODIS) thermal infrared (TIR) observations have been compiled for the southwestern United States to develop a multitemporal dataset used to map the areal extent of surface thermophysical units, identify anomalous surfaces and weather events, and establish temporal and spatial criteria to map and characterize surfaces in arid regions. Results suggest that weather patterns across the Mojave, Sonoran, and Great Basin desert regions produce spring and fall observations that are consistently cloud-free. Sparsely vegetated and bare soil surfaces display temperature patterns that are highly consistent during much of the year and from year to year. The conditions necessary for reliable quantitative temperatures for thermophysical mapping are both spatially and temporally controlled, and commonly occur for observations in the arid Southwest.
      PubDate: Aug. 2014
      Issue No: Vol. 7, No. 8 (2014)
       
  • Mapping Annual Land Use and Land Cover Changes Using MODIS Time Series
    • Authors: Yin; H.;Pflugmacher, D.;Kennedy, R.E.;Sulla-Menashe, D.;Hostert, P.;
      Pages: 3421 - 3427
      Abstract: Mapping land use and land cover change (LULCC) over large areas at regular time intervals is a key requisite to improve our understanding of dynamic land systems. In this study, we developed and tested an automated approach for mapping LULCCs at annual time intervals using data from the Moderate Resolution Imaging Spectroradiometer (MODIS). Our approach characterizes changes between land cover types based on annual time series of per-pixel land cover probabilities. We used the temporal segmentation algorithm MODTrendr to identify trends and changes in the probability time series that were associated with land cover/use conversions. Accuracy assessment revealed good performance of our approach (overall accuracy of 92.0%). The method detected conversions from forest to grassland with a user’s accuracy of ${bf {94.0}}pm {bf {2.0%}}$ and a producer’s accuracy of ${bf {95.6}}pm {bf {1.6%}}$ . Conversions between cropland and grassland were detected with a user’s and a producer’s accuracy of ${bf {65.8}}pm {bf {4.8%}}$ and ${bf {72.2}}pm {bf {9.2%}}$ , respectively. We here present for the first time an approach that combines probabilities derived from machine learning (random forest classification) with time-series-based analysis (MODTrendr) for land cover/use change analysis at MODIS scale.
      PubDate: Aug. 2014
      Issue No: Vol. 7, No. 8 (2014)
       
  • Evaluating an Intra-Annual Time Series for Grassland
           Classification—How Many Acquisitions and What Seasonal Origin Are
           Optimal?
    • Authors: Schmidt; T.;Schuster, C.;Kleinschmit, B.;Forster, M.;
      Pages: 3428 - 3439
      Abstract: The amount of images used in multitemporal classification studies has greatly increased along with enhanced temporal sensor capacities. Handling large intra-annual time series leads to the question of how the selection of image acquisition dates could be optimized. In this study, an empirical approach for evaluating the relative classification power of single acquisition dates is introduced for the differentiation of seminatural grassland vegetation. The main question is how many acquisitions from which phenological origins are preferable to achieve a certain classification accuracy target. The tested time series contains 24 single RapidEye scenes from 2009 to 2011. The vegetation index composites of these images were iteratively classified into different combinations of acquisition dates using the support vector machine (SVM) algorithm. The subsequent results were tested for significant accuracy improvements over single acquisition dates. These acquisition dates are subsumed under phenological seasons to evaluate adequate temporal acquisition windows. The results show that a three-scene composite reaches more than 0.8 overall accuracy (OAA). The best tradeoff amount between number of acquisition dates and classification accuracy is achieved using a seven-scene NDVI composite. The most important season for the differentiation of seminatural grassland is early summer (ESu). Full spring (FuS), late summer (LSu), and midsummer (MSu) can also be identified as influential temporal windows for data acquisition.
      PubDate: Aug. 2014
      Issue No: Vol. 7, No. 8 (2014)
       
  • Landsat Time Series and Lidar as Predictors of Live and Dead Basal Area
           Across Five Bark Beetle-Affected Forests
    • Authors: Bright; B.C.;Hudak, A.T.;Kennedy, R.E.;Meddens, A.J.H.;
      Pages: 3440 - 3452
      Abstract: Bark beetle-caused tree mortality affects important forest ecosystem processes. Remote sensing methodologies that quantify live and dead basal area (BA) in bark beetle-affected forests can provide valuable information to forest managers and researchers. We compared the utility of light detection and ranging (lidar) and the Landsat-based detection of trends in disturbance and recovery (LandTrendr) algorithm to predict total, live, dead, and percent dead BA in five bark beetle-affected forests in Alaska, Arizona, Colorado, Idaho, and Oregon, USA. The BA response variables were predicted from lidar and LandTrendr predictor variables using the random forest (RF) algorithm. RF models explained 28%–61% of the variation in BA responses. Lidar variables were better predictors of total and live BA, whereas LandTrendr variables were better predictors of dead and percent dead BA. RF models predicting percent dead BA were applied to lidar and LandTrendr grids to produce maps, which were then compared to a gridded dataset of tree mortality area derived from aerial detection survey (ADS) data. Spearman correlations of beetle-caused tree mortality metrics between lidar, LandTrendr, and ADS were low to moderate; low correlations may be due to plot sampling characteristics, RF model error, ADS data subjectivity, and confusion caused by the detection of other types of forest disturbance by LandTrendr. Provided these sources of error are not too large, our results show that lidar and LandTrendr can be used to predict and map live and dead BA in bark beetle-affected forests with moderate levels of accuracy.
      PubDate: Aug. 2014
      Issue No: Vol. 7, No. 8 (2014)
       
  • Use of Landsat 5 for Change Detection at 1998 Indian and Pakistani Nuclear
           Test Sites
    • Authors: Zelinski; M.E.;Henderson, J.;Smith, M.;
      Pages: 3453 - 3460
      Abstract: An underground nuclear explosion (UNE) can generate a shock wave that lofts surface material, resulting in surface changes that might be detectable. The Comprehensive Nuclear Test-Ban Treaty (CTBT) allows ground and airborne spectral and thermal imaging to help locate such events. Landsat 5 data on the 1998 Indian and Pakistani tests are used here to demonstrate that there are detectable changes in surface features which might be used to localize an underground nuclear test and to develop change detection techniques specific to the use of satellite data to support a CTBT on-site inspection. Landsat 5 has been active for over 20 years providing repeat coverage of the Earth’s surface every 16 days. Most locations have Landsat data available for a variety of dates, allowing for statistical analysis of the data to understand temporal trends and data variability on a pixel-by-pixel basis. Given the right conditions, these usual patterns of change (such as seasonal changes or weathering) can be discerned from unusual patterns of change, such as features relating to a UNE. This paper extends known change detection techniques to a temporal series of data and shows that multispectral change detection can be used to help localize a UNE.
      PubDate: Aug. 2014
      Issue No: Vol. 7, No. 8 (2014)
       
  • Pan-European Grassland Mapping Using Seasonal Statistics From Multisensor
           Image Time Series
    • Authors: Zillmann; E.;Gonzalez, A.;Montero Herrero, E.J.;van Wolvelaer, J.;Esch, T.;Keil, M.;Weichelt, H.;Garzon, A.M.;
      Pages: 3461 - 3472
      Abstract: Grasslands cover approximately 40% of the Earth’s surface. Low-cost tools for inventory, management, and monitoring are needed because of their great expanse, diversity, and the importance for environmental processes. Remote sensing is a useful technique for providing accurate and reliable information for land use planning and large-scale grassland management. In the context of “GIO land” (Copernicus Initial Operations land program), which is currently contracted by the European Environment Agency, a high-resolution grassland layer of 39 European countries is being created with an overall classification accuracy of better than 80%. Since grassland canopy density, chlorophyll status, and ground cover (GC) are highly dynamic throughout the growing season, no unique spectral signature can be used to map grasslands. Therefore, it is necessary to use image time series to characterize the phenological dynamics of grasslands throughout the year in order to discriminate between grasslands and other vegetation with similar spectral responses. This paper describes an operational approach based on a multisensor concept that employs optical multitemporal and multiscale satellite imagery to generate the high-resolution pan-European grassland layer. The approach is based on the supervised decision tree classifier C5.0 in combination with previous image segmentation and seasonal statistics for various vegetation indices (VIs). Results from the grassland classification for Hungary are presented. The accuracy assessment for this classification was carried out using 328 independent sample points derived from a ground-based European field survey program (LUCAS) and current CORINE Land Cover data. The grassland classification approach is explained in detail on the example of Hungary where an overall accuracy of 92.2% has been reached.
      PubDate: Aug. 2014
      Issue No: Vol. 7, No. 8 (2014)
       
  • Near Real-Time Vegetation Monitoring at Global Scale
    • Authors: Verger; A.;Baret, F.;Weiss, M.;
      Pages: 3473 - 3481
      Abstract: The NRT algorithm for near-real time estimation of global LAI, FAPAR, and FCOVER variables from SPOT/VEGETATION (VGT) satellite data is described here. It consists of three steps: 1) neural networks (NNT) (one for each variable) to provide instantaneous estimates from daily VGT-P reflectances; 2) a multistep filtering approach to eliminate data mainly affected by atmospheric effects and snow cover; and 3) Savitzky–Golay and climatology temporal smoothing and gap filling techniques to ensure consistency and continuity as well as short-term projection of the product dynamics. Performances of NRT estimates were evaluated by comparing with other products over the 2005–2008 period: 1) the offline estimates from the application of the algorithm over historical time series (HIST); 2) the geoland2 version 1 products also issued from VGT (GEOV1/VGT); and 3) ground data. NRT rapidly converges closely to the HIST processing after six dekads (10-day period) with major improvement after two dekads. Successive reprocessing will, therefore, correct for some instabilities observed in the presence of noisy and missing data. The root-mean-square error (RMSE) between NRT and HIST LAI is lower than 0.4 in all cases. It shows a rapid exponential decay with the number of observations in the composition window with convergence when 30 observations are available. NRT products are in good agreement with ground data (RMSE of 0.69 for LAI, 0.09 for FAPAR, and 0.14 for FCOVER) and consistent with GEOV1/VGT products with a significant improvement in terms of continuity (only 1% of missing data) and smoothness, especially at high latitudes, and Equatorial areas.
      PubDate: Aug. 2014
      Issue No: Vol. 7, No. 8 (2014)
       
  • Parcel-Based Change Detection in Land-Use Maps by Adopting the Holistic
           Feature
    • Authors: Bin; W.;Jian, Y.;Zhongming, Z.;Yu, M.;Anzhi, Y.;Jingbo, C.;Dongxu, H.;Xingchun, L.;Shunxi, L.;
      Pages: 3482 - 3490
      Abstract: Updating the existing land-use maps with remote sensing imagery has become a common method to produce the latest land-use database. An important step is to extract the changed information. In this paper, we propose a novel method to extract the changed parcels in land-use maps by using the holistic feature called “Spatial Envelope,” which encodes each parcel without segmenting it into homogeneous objects or small regions. The holistic feature is based on the energy spectrum of the windowed Fourier transform (WFT) of each land-use parcel, which is ideal for scene categorization. Unlike the pixel-based change detection using the difference image (DI) leading to speckled results or object-based method which requires a complicated process to segment the land-use parcel into homogeneous land-cover objects, our parcel-based change detection treats each land-use parcel as an entirety by calculating the holistic feature for the former and latter parcels. Then, the distance between the corresponding former and latter parcels is compared against a threshold to select the changed parcels. Experiments have demonstrated that our procedure can extract the changed parcels with the overall accuracy of more than 92%. The performance of our procedure is reliable not only on the high-resolution (HR) images of the same sensor, but also on the images acquired by different sensors with the same or approximate spatial resolution. Comparative experiments have also proved that the holistic feature is better than conventional spectral and textural features in parcel-based change detection.
      PubDate: Aug. 2014
      Issue No: Vol. 7, No. 8 (2014)
       
  • A Multievidence Approach for Crop Discrimination Using Multitemporal
           WorldView-2 Imagery
    • Authors: Chellasamy; M.;Zielinski, R.T.;Greve, M.H.;
      Pages: 3491 - 3501
      Abstract: Despite using multiple input datasets for effective crop classification, it is important to select an appropriate method that efficiently integrates these multiple datasets to produce accurate classification results. In this paper, we present an endorsement theory-based crop classification approach that considers the qualitative information, in terms of prediction probabilities, from different input datasets and integrates them efficiently to produce final classification results. Three different input datasets are used in this study: 1) spectral; 2) texture; and 3) indices from multitemporal (spring, early summer) WorldView-2 multispectral imagery. A multilayer perceptron classifier is trained with the multitemporal datasets separately using a backpropagation learning algorithm, and prediction probabilities are produced for each pixel as evidence against each crop class. An integration rule based on endorsement theory is applied to these multiple evidence by considering their individual contribution, and the most probable class of a pixel is identified. Integration of the three multidate datasets by the proposed method is found to produce higher overall classification accuracy (91.2%) when compared to conventional winner-takes-all approach (89%). In order to determine which individual dataset is more useful for crop discrimination, the dataset’s performance is compared using evidence and contributions produced in the proposed integration method for four selected crops, for both single- and multidate. The results of this analyses showed that seasonal textures information outperformed both spectral and indices. To verify this finding, results of individual dataset classification are examined. The highest overall classification accuracy of 88.8% is achieved by the use of multidate texture, where multidate spectral and indices resulted in 86.3% and 84.4%, respectively.
      PubDate: Aug. 2014
      Issue No: Vol. 7, No. 8 (2014)
       
  • A Stable Land Cover Patches Method for Automatic Registration of
           Multitemporal Remote Sensing Images
    • Authors: Cao; S.;Zhu, X.;Pan, Y.;Yu, Q.;
      Pages: 3502 - 3512
      Abstract: We propose a stable land cover patches method (SLCPM) for the automatic registration of multitemporal remote sensing images. Our method takes advantage of multispectral features as well as stable and widespread land cover patches in remote sensing images. We tested our method using optical satellite images through four different experiments. In the first and second experiments, we tried to register images acquired on different days but by the same sensor. In the third experiment, we tried to register images acquired on different days and by different sensors (which means different spectral resolutions were also considered). In the fourth experiment, we tried to register images with different spatial resolutions acquired on different dates and by different sensors. Three indices were used in our paper for quality evaluation: overall Root Mean Square Error (RMS $_{rm all}$ ), Root Mean Square Error calculated by the leave-one-out method (RMS $_{rm LOO}$ ), and a statistical evaluation of the goodness of control point (CP) distribution across the image (S $_{rm cat}$ ). Results showed SLCPM could generate sufficient, accurate and well distributed CP pairs. We further compared our method with two other popular automatic image registration methods-scale invariant feature transform (SIFT) and a contour-based approach. The contour-based approach could hardly generate any CPs in all the experiments, while SIFT performed very well in the first three experiments both in accuracy and distribution of CPs but was ineffective in the most complex (i.e., last) experiment, due to the lack of correct CPs.
      PubDate: Aug. 2014
      Issue No: Vol. 7, No. 8 (2014)
       
  • Multitemporal Fusion for the Detection of Static Spatial Patterns in
           Multispectral Satellite Images—With Application to Archaeological
           Survey
    • Authors: Menze; B.H.;Ur, J.A.;
      Pages: 3513 - 3524
      Abstract: We evaluate and further develop a multitemporal fusion strategy that we use to detect the location of ancient settlement sites in the Near East and to map their distribution, a spatial pattern that remains static over time. For each ASTER images that has been acquired in our survey area in north-eastern Syria, we use a pattern classification strategy to map locations with a multispectral signal similar to the one from (few) known archaeological sites nearby. We obtain maps indicating the presence of anthrosol—soils that formed in the location of ancient settlements and that have a distinct spectral pattern under certain environmental conditions—and find that pooling the probability maps from all available time points reduces the variance of the spatial anthrosol pattern significantly. Removing biased classification maps—i.e., those that rank last when comparing the probability maps with the (limited) ground truth we have—reduces the overall prediction error even further, and we estimate optimal weights for each image using a nonnegative least squares regression strategy. The ranking and pooling strategy approach we propose in this study shows a significant improvement over the plain averaging of anthrosol probability maps that we used in an earlier attempt to map archaeological sites in a $20,000hbox{-km}^2$ area in northern Mesopotamia, and we expect it to work well in other surveying tasks that aim in mapping static surface patterns with limited ground truth in long series of multispectral images.
      PubDate: Aug. 2014
      Issue No: Vol. 7, No. 8 (2014)
       
  • Concurrent Self-Organizing Maps for Supervised/Unsupervised Change
           Detection in Remote Sensing Images
    • Authors: Neagoe; V.;Stoica, R.;Ciurea, A.;Bruzzone, L.;Bovolo, F.;
      Pages: 3525 - 3533
      Abstract: This paper proposes two approaches to change detection in bitemporal remote sensing images based on concurrent self-organizing maps (CSOM) neural classifier. The first one performs change detection in a supervised way, whereas the second performs change detection in an unsupervised way. The supervised approach is based on two steps: 1) concatenation (CON); and 2) CSOM classification. CSOM classifier uses two SOM modules: 1) one associated to the class of change; and 2) the other to the class of no-change for the generation of the training set. The unsupervised change detection approach is based on four steps: 1) image comparison (IC), consisting of either computation of difference image (DI) for passive sensors or computation of log-ratio image (LRI) for active sensors; 2) unsupervised selection of the pseudotraining sample set (USPS); 3) concatenation (CON); and 4) CSOM classification. The proposed approaches are evaluated using two datasets. First dataset is a LANDSAT-5 TM bitemporal image over Mexico area taken before and after two wildfires, and the second one is a TerraSAR-X image acquired in the Fukushima region, Japan, before and after tsunami. Experimental results confirm the effectiveness of the proposed approaches.
      PubDate: Aug. 2014
      Issue No: Vol. 7, No. 8 (2014)
       
  • Impact of Temporal Autocorrelation Mismatch on the Assimilation of
           Satellite-Derived Surface Soil Moisture Retrievals
    • Authors: Qiu; J.;Crow, W.T.;Mo, X.;Liu, S.;
      Pages: 3534 - 3542
      Abstract: Satellite-based surface soil moisture retrievals are commonly assimilated into ecohydrological models in order to obtain improved profile soil moisture estimates. However, differences in temporal autocorrelation structure between these retrievals and comparable model-based predictions can potentially undermine the efficiency of such assimilation. Here, we conduct a series of synthetic experiments to examine the magnitude of this problem and the potential for detecting the presence of retrieval/model autocorrelation differences using a simple diagnostic procedure. Our synthetic experiments are based on modifying the observation operator within a data assimilation system to artificially induce differences in temporal autocorrelation between assimilated surface soil moisture retrievals and comparable surface soil moisture estimates made by an off-line ecohydrological assimilation model. Results demonstrate that neglecting a mismatch in retrieval/assimilation model autocorrelation can reduce the benefit of surface soil moisture data assimilation. The impact is especially large for soil profiles with limited vertical coupling. However, the presence of this source of retrieval/model autocorrelation misfit is detectable using a simple diagnostic index derived from a time series of soil moisture retrievals and open loop model predictions. Using relatively short data sets ( ${sim}{bf 2}$ years in length), the diagnostic is capable of identifying worst-case scenarios leading to the most significant degradation of assimilation results.
      PubDate: Aug. 2014
      Issue No: Vol. 7, No. 8 (2014)
       
  • Multiscale Event-Based Mining in Geophysical Time Series: Characterization
           and Distribution of Significant Time-Scales in the Sea Surface Temperature
           Anomalies Relatively to ENSO Periods from 1985 to 2009
    • Authors: Saulquin; B.;Fablet, R.;Mercier, G.;Demarcq, H.;Mangin, A.;Fanton d Andon, O.H.;
      Pages: 3543 - 3552
      Abstract: In this paper, one-dimensional (1-D) geophysical time series are regarded as series of significant time-scale events. We combine a wavelet-based analysis with a Gaussian mixture model to extract characteristic time-scales of 486 144 detected events in the Sea Surface Temperature Anomaly (SSTA) observed from satellite at global scale from 1985 to 2009. We retrieve four low-frequency characteristic time-scales of Niño Southern Oscillation (ENSO) in the 1.5- to 7-year range and show their spatial distribution. High-frequency (HF) SSTA event spatial distribution shows a dependency to the ENSO regimes, pointing out that the ENSO signal also involves specific signatures at these time-scales. These fine-scale signatures can hardly be retrieved from global EOF approaches, which tend to exhibit uppermost the low-frequency influence of ENSO onto the SSTA. In particular, we observe at global scale a major increase by 11% of the number of SSTA HF events during Niño periods, with a local maximum of 80% in Europe. The methodology is also used to highlight an ENSO-induced frequency shift during the major 1997–2000 ENSO event in the intertropical Pacific. We observe a clear shift from the high frequencies toward the 3.36-year scale with a maximum shift occurring 2 months before the ENSO maximum of energy at 3.36-year scale.
      PubDate: Aug. 2014
      Issue No: Vol. 7, No. 8 (2014)
       
  • Blue–Red–NIR Model for Chlorophyll- a
           Retrieval in Hypersaline–Alkaline Water Using Landsat ETM+ Sensor
    • Authors: Singh; K.;Ghosh, M.;Sharma, S.R.;Kumar, P.;
      Pages: 3553 - 3559
      Abstract: A conceptual three-band model has been proposed previously and efficiently used to retrieve the chlorophyll-a (Chl-a) concentration ( ${bf{ C}}_{{bf{ chla}}}$ ) in deeper water bodies. In this study, we have proposed an empirical ${bf{ C}}_{{bf{ chla}}}$ estimation model using Landsat ETM+ image reflectance and laboratory-based ${bf{ C}}_{{bf{ chla}}}$ measurements from hypersaline-alkaline shallow lake (HSAS-lake) water. This study aims to use remote sensing technique to determine the quantity and distribution of chlorophyll (as an indicator of cyanobacterial biomass) rendering an indirect estimate of food availability for flamingos and other aquatic animals, thus providing valuable information for their future conservation. Using proposed empirical method named blue–red–NIR model, it has been found that the ${bf{ C}}_{{bf{ chla}}}$ ranges from 3.43 to ${bf 43.75}~{bm {upmu}}bf{gL}^{-1}$ with the mean Chl-a value of $5.45~upmubf{g L}^{-1}$ , in the lake investigated. A variety of regression functions have been implemented for the single and multiband ratios. The best-fitted regression model was developed for the band combination of $[{bf{ R}}_{{bf{ rs}}}^{-1}({bf 660})-{bf{ R}}_{{bf{ rs}}}^{{bf -1}}({bf 482})]times {bf{ R}}_{{bf{ rs}}}^{{bf -1}}({bf 825})$ having an ${bf{ R}}^2$ of 0.88 and model errors of 0.93, 0.8, and 4.74 for standard error of estimate (SEE), Nash- –Sutcliffe coefficient (E), and mean absolute percentage error (MAPE), respectively. Our finding evinces that the proposed blue–red–NIR model may be appraised as a robust solution for the estimation of ${bf{ C}}_{{bf{ chla}}}$ in optically shallow waters, provided that the local inherent optical properties (IOPs) should be scrutinized and reinitialized.
      PubDate: Aug. 2014
      Issue No: Vol. 7, No. 8 (2014)
       
  • Unsupervised Selection of Training Samples for Tree Species Classification
           Using Hyperspectral Data
    • Authors: Dalponte; M.;Ene, L.T.;Orka, H.O.;Gobakken, T.;Naesset, E.;
      Pages: 3560 - 3569
      Abstract: In this study, we introduced a novel unsupervised selection method for collecting training samples for tree species classification at individual tree crown (ITC) level using hyperspectral data. The selection process is based on a distance metric defined among the spectral signatures of the pixels inside the ITCs, and a search strategy based on the Sequential Forward Floating Selection algorithm. The method was developed using two kinds of samples: plots and ITCs. The experimental results indicated that the method allows reducing the amount of training samples needed for the classification process, without significantly decreasing the classification accuracy. Applying the proposed method, the kappa accuracies obtained using about half of the total number of plots ( $hbox{kappa accuracy}=0.84$ ) and approximately one-third of the total number of ITCs ( $hbox{kappa accuracy} = 0.83$ ) were not statistically different from the results obtained using the full set of training samples ( $hbox{kappa accuracy} = {0.86}$ ). The proposed method demonstrates that using a priori information derived from the hyperspectral data can substantially reduce the amount of field work and, consequently, the forest inventory costs.
      PubDate: Aug. 2014
      Issue No: Vol. 7, No. 8 (2014)
       
  • A Novel Sparsity-Based Framework Using Max Pooling Operation for
           Hyperspectral Image Classification
    • Authors: Yuan; H.;Yan Tang, Y.;
      Pages: 3570 - 3576
      Abstract: Various sparsity-based methods have been widely used in hyperspectral image (HSI) classification. To determine the class label of a test sample, traditional sparsity-based frameworks mainly use the sparse vectors to compute the residual error for classification. In this paper, a novel sparsity-based framework is proposed, which adopts the max pooling operation for HSI classification. Compared with the traditional sparsity-based frameworks using residual error, sparse vectors in our proposed framework are utilized to generate the feature vectors using max pooling operation. Experimental results demonstrate that our proposed framework can achieve the state-of-the-art classification performance.
      PubDate: Aug. 2014
      Issue No: Vol. 7, No. 8 (2014)
       
  • Spatial–Spectral Information-Based Semisupervised Classification
           Algorithm for Hyperspectral Imagery
    • Authors: Wang; L.;Hao, S.;Wang, Y.;Lin, Y.;Wang, Q.;
      Pages: 3577 - 3585
      Abstract: Semisupervised learning has shown its great potential in land cover mapping. It exploits the information of unlabeled training samples and converts those samples to labeled training samples to enhance classification. In this paper, the spatial information extracted by a two-dimensional (2-D) Gabor filter was stacked with spectral information first, and then the spatial neighborhood information of labeled training samples was combined with active learning (AL) algorithm to select the most useful and informative samples, which were used as the unlabeled set to aid the probability model-based supervised support vector machine (SVM). Experiments on two hyperspectral datasets showed that the spatial–spectral information-based semisupervised classification algorithm ( $bf{S}^{bf{2}} bf{ISC}$ ) can produce high classification accuracy with a small number of labeled samples, and outperformed the compared semisupervised algorithms.
      PubDate: Aug. 2014
      Issue No: Vol. 7, No. 8 (2014)
       
  • Leaf Area Index Estimation Using Vegetation Indices Derived From Airborne
           Hyperspectral Images in Winter Wheat
    • Authors: Xie; Q.;Huang, W.;Liang, D.;Chen, P.;Wu, C.;Yang, G.;Zhang, J.;Huang, L.;Zhang, D.;
      Pages: 3586 - 3594
      Abstract: Continuous monitoring leaf area index (LAI) of field crops in a growing season has a great challenge. The development of remote sensing technology provides a good tool for timely mapping LAI regionally. In this study, hyperspectral reflectance data (405–835 nm) obtained from an airborne hyperspectral imager (Pushbroom Hyperspectral Imager) were used to model LAI of winter wheat canopy in the 2002 crop growing season. LAI was modeled based on its semi-empirical relationships with six vegetation indices (VIs), including ratio vegetation index (RVI), modified simple ratio index (MSR), normalized difference vegetation index (NDVI), a newly proposed index NDVI-like (which resembles NDVI), modified triangular vegetation index (MTVI2), and modified soil adjusted vegetation index (MSAVI). To assess the performance of these VIs, root mean square errors (RMSEs) and determination coefficient ( ${bf R}^{bf 2} $ ) between estimated LAI and measured LAI were reported. Our result showed that NDVI-like was the most accurate predictor of LAI. The inclusion of a green band in MTVI2 trended to give a rise to a much quicker saturation with increase of LAI (e.g., over 3.5). MSAVI and MTVI2 showed comparable but lower potential than NDVI-like in estimating LAI. RVI and MSR demonstrated their lowest prediction accuracy, implying that they are more likely to be affected by environmental conditions such as atmosphere and cloud, thus cannot properly reflect the properties of winter wheat canopy. Our results support the use of VIs for a quick assessment of seasonal variations in winter wheat LAI. Among the indices we tested in this study, the newly developed NDVI-like model created the most accurate and reliable results.
      PubDate: Aug. 2014
      Issue No: Vol. 7, No. 8 (2014)
       
  • A Spectral-Unmixing Approach to Estimate Water–Mass Concentrations
           in Case 2 Waters
    • Authors: Burazerovic; D.;Heylen, R.;Raymaekers, D.;Knaeps, E.;Philippart, C.J.M.;Scheunders, P.;
      Pages: 3595 - 3605
      Abstract: In this work, we study the estimation of water-quality parameters from the water-leaving reflectance by means of spectral unmixing. Our starting point is the provision of an analytic model relating the reflectance of water to its masses/bodies or constituents, as specified by their specific inherent optical properties (SIOP) and concentrations. The main objective is to reformulate the estimation of these concentrations as a spectral unmixing problem. We perform this by employing the linear mixing model (LMM), while suitably defining the endmembers (EMs) as representations of different water types dominated by one or more constituents. Each EM is then calculated by substituting the in situ-measured SIOP and combinations of utmost concentrations of each constituent into the water-reflectance model. Such use of unmixing practically enables to maintain an implicitly nonlinear relation between the water reflectance and constituents’ concentrations, without resorting to the use of nonlinear mixing models. Furthermore, we present a method to automatically extract the EMs from the reflectance image. We validate the entire unmixing-based take using the state-of-the-art method as reference that inverts the water reflectance via comparisons (curve matching) with spectra from a lookup table. This validation is done using simulated data derived from the water-reflectance model and real hyperspectral data acquired over coastal waters of a shallow sea.
      PubDate: Aug. 2014
      Issue No: Vol. 7, No. 8 (2014)
       
  • Site-Specific Plant Condition Monitoring Through Hyperspectral Alternating
           Least Squares Unmixing
    • Authors: Tits; L.;Somers, B.;Saeys, W.;Coppin, P.;
      Pages: 3606 - 3618
      Abstract: Alternating least squares (ALS) is a blind source separation method commonly used in chemometrics to simultaneously estimate the absorption spectrum and concentration of different components in a chemical sample. In this study, the transferability of ALS from chemometrics to agricultural remote sensing is evaluated. Due to the subpixel contribution of background components, spectral unmixing has become an indispensable processing step in the spectral analysis of agricultural areas. Yet, traditional unmixing techniques only allow estimating the subpixel cover distribution of different components, but fail to provide an estimate of pure spectral signature of the crop component. This info is, however, highly valuable, as this pure crop signature could be used to monitor the health status of trees. Here, we anticipate that ALS can provide a solution. ALS estimates both the concentration and the absorption spectra of different components in a chemical sample and this can easily be translated into estimating both the subpixel cover fraction and spectral signature of different components in a mixed image pixel. We tested the performance of ALS on binary synthetic mixtures of citrus canopy and soil spectra, as well as on a ray-tracing experiment of a virtual orchard. ALS indeed allowed to simultaneously estimate the subpixel cover distribution ( ${bf {{ RMSE} = 0.05}}$ ), as well as the pure spectral signatures of different endmembers ( ${bf {{RRMSE} lt 0.12}}$ ), and considerably improved the extraction of biophysical parameters ( ${bf {Delta,{R^2}}}$ up to 0.43). Thus, ALS provides a promising new image analysis tool for agricultural remote sensing.
      PubDate: Aug. 2014
      Issue No: Vol. 7, No. 8 (2014)
       
  • A New Hybrid Strategy Combining Semisupervised Classification and Unmixing
           of Hyperspectral Data
    • Authors: Dopido; I.;Li, J.;Gamba, P.;Plaza, A.;
      Pages: 3619 - 3629
      Abstract: Spectral unmixing and classification have been widely used in the recent literature to analyze remotely sensed hyperspectral data. However, few strategies have combined these two approaches in the analysis. In this work, we propose a new hybrid strategy for semisupervised classification of hyperspectral data which exploits both spectral unmixing and classification in a synergetic fashion. During the process, the most informative unlabeled samples are automatically selected from the pool of candidates, thus reducing the computational cost of the process by including only the most informative unlabeled samples. Our approach integrates a well-established discriminative probabilistic classifier—the multinomial logistic regression (MLR) with different spectral unmixing chains, thus bridging the gap between spectral unmixing and classification and exploiting them together for the analysis of hyperspectral data. The effectiveness of the proposed method is evaluated using two real hyperspectral data sets, collected by the NASA Jet Propulsion Laboratory’s airborne visible infrared imaging spectrometer (AVIRIS) over the Indian Pines region, Indiana, and by the reflective optics spectrographic imaging system (ROSIS) over the University of Pavia, Italy.
      PubDate: Aug. 2014
      Issue No: Vol. 7, No. 8 (2014)
       
  • Endmember Extraction Guided by Anomalies and Homogeneous Regions for
           Hyperspectral Images
    • Authors: Erturk; A.;Cesmeci, D.;Gullu, M.K.;Gercek, D.;Erturk, S.;
      Pages: 3630 - 3639
      Abstract: Endmember extraction is the process of selecting pure spectral signatures of materials from hyperspectral data. Most of the endmember extraction methods in the literature use only the spectral information, and disregard the spatial composition of the image. Spatial–spectral preprocessing methods, motivated by the assumption that endmembers are more likely to be located in homogenous regions, can increase the performance of endmember extraction by directing the extraction process to homogenous regions. However, such an approach generally results in a failure of extracting anomalous or scarce endmembers, which can be important in practical applications, e.g., to extract endmembers of materials such as landmines, rare minerals, or stressed crops. Although anomaly detection can be applied in parallel to endmember extraction, the process of endmember extraction and unmixing provides a summary of the data, which is important for concepts such as data scanning and compression, and disregarding anomalous endmembers in such a summary or compression of big data may result in undesired consequences for many application fields. In this paper, an approach that guides the endmember extraction process to spatially homogenous regions instead of transition areas, while also extracting anomalous pixel vectors as endmembers, is proposed. The proposed approach can be used with any spectral-based endmember extraction method. The experimental results validate the approach for both synthetic and real hyperspectral images.
      PubDate: Aug. 2014
      Issue No: Vol. 7, No. 8 (2014)
       
  • Sparse Non-negative Matrix Factorization on GPUs for Hyperspectral
           Unmixing
    • Authors: Wu; Z.;Ye, S.;Liu, J.;Sun, L.;Wei, Z.;
      Pages: 3640 - 3649
      Abstract: Hyperspectral unmixing is a typical problem of blind source separation, which can be solved by non-negative matrix factorization (NMF). NMF based on sparsity, which can increase the efficiency of unmixing, is an important topic in hyperspectral unmixing. In this paper, a novel constrained sparse (CS) NMF (CSNMF) method for hyperspectral unmixing is proposed, where a new sparsity term is introduced to improve the stability and accuracy of unmixing model. The corresponding algorithm is designed based on the alternating direction method of multiplies. In order to further enhance the execution speed, parallel optimization of hyperspectral unmixing based on CSNMF on graphics processing units (CSNMF-GPU) is investigated and compared in terms of both accuracy and speed. The realization of the proposed method using compute unified device architecture (CUDA) on GPUs is described and evaluated. Experimental results based on the simulated hyperspectral datasets show that the proposed CSNMF method can improve the unmixing accuracy steadily. The tests comparing the parallel optimization of CSNMF on GPUs with the serial implementation and the multicore implementation, using both simulated and real hyperspectral data, demonstrate the effectiveness of the CSNMF-GPU approach.
      PubDate: Aug. 2014
      Issue No: Vol. 7, No. 8 (2014)
       
  • Parallel Implementation of the Modified Vertex Component Analysis
           Algorithm for Hyperspectral Unmixing Using OpenCL
    • Authors: Callico; G.M.;Lopez, S.;Aguilar, B.;Lopez, J.F.;Sarmiento, R.;
      Pages: 3650 - 3659
      Abstract: Hyperspectral imaging represents the state-of-the-art technique in those applications related to environmental monitoring, military surveillance, or rare mineral detection. However, one of the requirements of paramount importance when dealing with such scenarios is the ability to achieve real-time constraints taking into account the huge amount of data involved in processing this type of images. In this paper, the authors present for the first time a combination of the newly introduced modified vertex component analysis (MVCA) algorithm for the process of endmembers extraction together with the ability of GPUs to exploit its parallelism, giving, as a result, important speedup factors with respect to its sequential counterpart, while maintaining the same levels of endmember extraction accuracy than the vertex component analysis (VCA) algorithm. Furthermore, OpenCL ensures the use of generic computing platforms without being restricted to a particular vendor. The proposed approach has been assessed on a set of synthetic images as well as on the well-known Cuprite real image, showing that the most time-consuming operations are located on the matrix projection and the maximum search processes. Comparison of the proposed technique with a single-threaded C-based implementation of the MVCA algorithm shows a speedup factor of 8.87 for a ${bf{500}}bf{times} {bf{500}}$ pixel artificial image with 20 endmembers and 7.14 for the well-known Cuprite hyperspectral data set, including in both cases I/O transfers. Moreover, when the proposed implementation is compared with respect to a C-based sequential implementation of the VCA algorithm, a speedup of 115 has been achieved. In all the cases, the results obtained by the MVCA are the same as the ones obtained with the VCA; thus, the accuracy of the proposed algorithm is not compromised.
      PubDate: Aug. 2014
      Issue No: Vol. 7, No. 8 (2014)
       
  • GPU-Accelerated Longwave Radiation Scheme of the Rapid Radiative Transfer
           Model for General Circulation Models (RRTMG)
    • Authors: Price; E.;Mielikainen, J.;Huang, M.;Huang, B.;Huang, H.A.;Lee, T.;
      Pages: 3660 - 3667
      Abstract: Atmospheric radiative transfer models calculate radiative transfer of electromagnetic radiation through a planetary atmosphere. One of such models is the rapid radiative transfer model (RRTM), which evaluates longwave and shortwave atmospheric radiative fluxes and heating rates. The RRTM for general circulation models (GCMs), RRTMG, is an accelerated version based on the single-column reference of RRTM. The longwave radiation scheme of RRTM for GCMs ( ${bf RRTMG_LW}$ ) is one model that utilizes the correlated-k approach to calculate longwave fluxes and heating rates for application to GCMs. In this paper, the feasibility of using graphics processing units (GPUs) to accelerate the ${bf RRTMG_LW}$ in weather research and forecasting (WRF) model is examined. GPUs allow a substantial performance improvement in ${bf RTMG_LW}$ with a large number of parallel compute cores at low cost and power. Our GPU version of ${bf RRTMG_LW}$ yields the bit-exact outputs as its original Fortran code. Our results show that NVIDIA’s K40 GPU achieves a speedup of ${bf 127times}$ as compared to its CPU counterpart running on one CPU core of Intel Xeon E5-2603, whereas the speedup for one CPU socket (4 cores) of the Xeon E5-2603 with respect to one CPU core is only ${bf 3.2times}$ .
      PubDate: Aug. 2014
      Issue No: Vol. 7, No. 8 (2014)
       
  • Mapping of the Solar Irradiance in the UAE Using Advanced Artificial
           Neural Network Ensemble
    • Authors: Alobaidi; M.H.;Marpu, P.R.;Ouarda, T.B.M.J.;Ghedira, H.;
      Pages: 3668 - 3680
      Abstract: Accurate spatial and temporal solar irradiance mapping is important for a wide range of applications related to efficient utilization of solar-based energy harvesting technologies. An improved artificial neural network (ANN) ensemble framework is proposed to estimate the solar irradiance variables from satellite data acquired using the Spinning Enhanced Visible and Infrared Imager (SEVIRI) instrument onboard the Meteosat Second Generation (MSG) satellite. The cloud-free and cloudy observations were clustered in two separate case studies, and for each case, two ANN ensemble models were trained; one for predicting the diffuse horizontal irradiance (DHI) and the other for predicting the direct normal irradiance (DNI). The global horizontal irradiance (GHI) was then computed from DHI and DNI estimates for each cloud condition. The proposed methodology was also applied in a second scheme, where the input and output variables, for each case study at each cloud condition are preprocessed using the Box–Cox transformation. The training and testing of the models were performed using spatially and temporally independent data. The proposed models produced significantly improved generalization ability and superior performance when compared with results from a previous study dealing with solar mapping in the United Arab Emirates (UAE).
      PubDate: Aug. 2014
      Issue No: Vol. 7, No. 8 (2014)
       
 
 
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