april 2012 volume 5 number 2 ijsthz (issn 1939-1404)

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APRIL 2012
VOLUME 5
NUMBER 2
IJSTHZ
(ISSN 1939-1404)
SPECIAL ISSUE ON HYPERSPECTRAL IMAGE AND SIGNAL PROCESSING
True color composite of a scene collected by the ROSIS hyperspectral sensor over the city of Pavia, Italy (left) and result of a color morphological
area opening on the ROSIS image (right). The scene is available from Prof. Paolo Gamba at the University of Pavia, Italy.
APRIL 2012
VOLUME 5
NUMBER 2
IJSTHZ
(ISSN 1939-1404)
SPECIAL ISSUE ON HYPERSPECTRAL IMAGE AND SIGNAL PROCESSING
Foreword to the Special Issue on Hyperspectral Image and Signal Processing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . A. Plaza, J. M. Bioucas-Dias, A. Simic, and W. J. Blackwell
347
SPECIAL ISSUE PAPERS
Hyperspectral Unmixing Overview: Geometrical, Statistical, and Sparse Regression-Based Approaches . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . J. M. Bioucas-Dias, A. Plaza, N. Dobigeon, M. Parente, Q. Du, P. Gader, and J. Chanussot
Spatial-Spectral Preprocessing Prior to Endmember Identification and Unmixing of Remotely Sensed Hyperspectral
Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . G. Martín and A. Plaza
Automated Extraction of Image-Based Endmember Bundles for Improved Spectral Unmixing . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . B. Somers, M. Zortea, A. Plaza, and G. P. Asner
Interest Segmentation of Large Area Spectral Imagery for Analyst Assistance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . A. Schlamm, D. Messinger, and W. Basener
A Quantitative and Comparative Assessment of Unmixing-Based Feature Extraction Techniques for Hyperspectral Image
Classification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . I. Dópido, A. Villa, A. Plaza, and P. Gamba
Classification in High-Dimensional Feature Spaces—Assessment Using SVM, IVM and RVM With Focus on Simulated
EnMAP Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . A. C. Braun, U. Weidner, and S. Hinz
Compression of Hyperspectral Images Using Discerete Wavelet Transform and Tucker Decomposition . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . A. Karami, M. Yazdi, and G. Mercier
Performance Evaluation of the H.264/AVC Video Coding Standard for Lossy Hyperspectral Image Compression . . . . . . .
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . L. Santos, S. López, G. M. Callicó, J. F. López, and R. Sarmiento
Practical Evaluation of Max-Type Detectors for Hyperspectral Images . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . P. Bajorski
Target Detection Under Misspecified Models in Hyperspectral Images . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . P. Bajorski
Detection of Amorphously Shaped Objects Using Spatial Information Detection Enhancement (SIDE) . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . C. S. Grant, T. K. Moon, J. H. Gunther, M. R. Stites, and G. P. Williams
A Spectral/Spatial CBIR System for Hyperspectral Images . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . M. A. Veganzones and M. Graña
354
380
396
409
421
436
444
451
462
470
478
488
(Contents Continued on Page 346)
(Contents Continued from Page 345)
An Optimization-Based Approach to Fusion of Hyperspectral Images . . . . . . . . . . . . . . . . . . . . . . . K. Kotwal and S. Chaudhuri
Multisource Classification of Color and Hyperspectral Images Using Color Attribute Profiles and Composite Decision
Fusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . G. Thoonen, Z. Mahmood, S. Peeters, and P. Scheunders
EeteS—The EnMAP End-to-End Simulation Tool . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . . K. Segl, L. Guanter, C. Rogass, T. Kuester, S. Roessner, H. Kaufmann, B. Sang, V. Mogulsky, and S. Hofer
Unsupervised Band Selection for Hyperspectral Imagery Classification Without Manual Band Removal . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . S. Jia, Z. Ji, Y. Qian, and L. Shen
Particle Swarm Optimization-Based Hyperspectral Dimensionality Reduction for Urban Land Cover Classification . . . . .
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . H. Yang, Q. Du, and G. Chen
GPU Acceleration of the Updated Goddard Shortwave Radiation Scheme in the Weather Research and Forecasting (WRF)
Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . J. Mielikainen, B. Huang, H.-L. A. Huang, and M. D. Goldberg
Potential Discrimination of Toxic Industrial Chemical Effects on Poplar, Canola and Wheat, Detectable in Optical
Wavelengths 400–2450 nm . . . .. . . . D. Rogge, B. Rivard, M. K. Deyholos, J. Lévesque, J.-P. Ardouin, and A. A. Faust
Using Hyperspectral Remote Sensing Data for Retrieving Canopy Chlorophyll and Nitrogen Content . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . J. G. P. W. Clevers and L. Kooistra
Addressing the Effects of Canopy Structure on the Remote Sensing of Foliar Chemistry of a 3-Dimensional,
Radiometrically Porous Surface . . . . . . . .. . . . . . . K. O. Niemann, G. Quinn, D. G. Goodenough, F. Visintini, and R. Loos
Tree Species Identification in Mixed Baltic Forest Using LiDAR and Multispectral Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . R. Dinuls, G. Erins, A. Lorencs, I. Mednieks, and J. Sinica-Sinavskis
Spectral Discrimination of Mediterranean Maquis and Phrygana Vegetation: Results From a Case Study in Greece . . . . .
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . K. Manevski, I. Manakos, G. P. Petropoulos, and C. Kalaitzidis
Temperature and Power Output of the Lava Lake in Halema’uma’u Crater, Hawaii, Using a Space-Based Hyperspectral
Imager . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . J. J. Cipar, G. P. Anderson, and T. W. Cooley
501
510
522
531
544
555
563
574
584
594
604
617
REGULAR PAPERS
GPU Implementation of Stony Brook University 5-Class Cloud Microphysics Scheme in the WRF . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . J. Mielikainen, B. Huang, H.-L. A. Huang, and M. D. Goldberg
A Semi-Automatic Approach for the Extraction of Sandy Bodies (Sand Spits) From IKONOS-2 Data . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . A. C. Teodoro and H. Gonçalves
Complex Urban Infrastructure Deformation Monitoring Using High Resolution PSI . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . H. Lan, L. Li, H. Liu, and Z. Yang
A Statistical Approach to Mitigating Persistent Clutter in Radar Reflectivity Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . V. Lakshmanan, J. Zhang, K. Hondl, and C. Langston
Scale Correction of Two-Band Ratio of Red to Near-Infrared Using Imagery Histogram Approach: A Case Study on
Indian Remote Sensing Satellite in Yellow River Estuary . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . J. Chen, B. Wang, and J. Sun
663
Information for Authors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
669
625
634
643
652
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