Blind spectral unmixing based on sparse nonnegative matrix factorization

IEEE Transactions on Image Processing

Volumn 20, Issue 4, 2011, Pages 1112-1125

  Yang, Zuyuan ^a   Zhou, Guoxu ^a   Xie, Shengli ^a   Ding, Shuxue ^b,c   Yang, Jun Mei ^a   Zhang, Jun ^d  

^a SOUTH CHINA UNIVERSITY OF TECHNOLOGY   (China)

^b UNIVERSITY OF AIZU   (Japan)

^c RIKEN BRAIN SCIENCE INSTITUTE   (Japan)

^d BEIHANG UNIVERSITY   (China)

Author keywords

Blind spectral unmixing; nonnegative matrix factorization (NMF); sparseness measure

Indexed keywords

DIMENSION REDUCTION; ENDMEMBERS; GRADIENT BASED; HIGHER ORDER; LEARNING RATES; NONNEGATIVE MATRIX FACTORIZATION; NONNEGATIVE MATRIX FACTORIZATION (NMF); PHYSICAL SIGNIFICANCE; REAL-WORLD IMAGE; SIGNAL VECTORS; SPARSE DISTRIBUTION; SPARSE NON-NEGATIVE MATRIX FACTORIZATIONS; SPARSENESS MEASURE; SPECTRAL DATA; SPECTRAL UNMIXING; SYNTHETIC MIXTURES;

FACTORIZATION;

MATRIX ALGEBRA;

ALGORITHM; ARTICLE; AUTOMATED PATTERN RECOGNITION; COMPUTER ASSISTED DIAGNOSIS; IMAGE ENHANCEMENT; METHODOLOGY; REPRODUCIBILITY; SENSITIVITY AND SPECIFICITY;

ALGORITHMS; IMAGE ENHANCEMENT; IMAGE INTERPRETATION, COMPUTER-ASSISTED; PATTERN RECOGNITION, AUTOMATED; REPRODUCIBILITY OF RESULTS; SENSITIVITY AND SPECIFICITY;

EID: 79952957026     PISSN: 10577149     EISSN: None     Source Type: Journal    
DOI: 10.1109/TIP.2010.2081678     Document Type: Article

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