Learning discriminative sparse representations for modeling, source separation, and mapping of hyperspectral imagery

IEEE Transactions on Geoscience and Remote Sensing

Volumn 49, Issue 11 PART 1, 2011, Pages 4263-4281

  Castrodad, Alexey ^a,b   Xing, Zhengming ^c   Greer, John B ^b   Bosch, Edward ^b   Carin, Lawrence ^c   Sapiro, Guillermo ^a  

^a University of Minnesota   (United States)

^b National Geospatial Intelligence Agency   (United States)

^c Duke University   (United States)

Author keywords

Hyperspectral imaging; scene classification; sparse modeling; spectral unmixing

Indexed keywords

HYPERSPECTRAL IMAGERY; HYPERSPECTRAL IMAGING; LINEAR COMBINATIONS; LINEAR MIXTURES; MODELING TECHNIQUE; PIXEL CLASSIFICATION; REGULARIZER; SCENE CLASSIFICATION; SOURCE SEPARATION; SPARSE MODELING; SPARSE REPRESENTATION; SPECTRAL UNMIXING; SPECTRAL VARIABILITY; SUB PIXELS; UNSUPERVISED CLASSIFICATION;

OPTIMIZATION; PIXELS;

REMOTE SENSING;

COMPARATIVE STUDY; IMAGE CLASSIFICATION; LEARNING; LINEARITY; NUMERICAL MODEL; OPTIMIZATION; PERFORMANCE ASSESSMENT; PIXEL; REMOTE SENSING; SPECTRAL ANALYSIS;

EID: 80455122805     PISSN: 01962892     EISSN: None     Source Type: Journal    
DOI: 10.1109/TGRS.2011.2163822     Document Type: Article

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