Title :
Enhancing Spectral Unmixing by Local Neighborhood Weights
Author :
Liu, Junmin ; Zhang, Jiangshe ; Gao, Yuelin ; Zhang, Chunxia ; Li, Zhihua
Author_Institution :
Sch. of Math. & Stat., Xi´´an Jiaotong Univ., Xi´´an, China
Abstract :
Spectral unmixing is an effective technique to remotely sensed data exploitation. In this paper, appropriate weights in a local neighborhood are designed to enhance spectral unmixing. The weights integrate the spectral and spatial information, and can effectively segment the homogenous and transition areas between different ground cover types. Based on this region-segmentation, pure-pixel-based end-member extraction algorithms are insensitive to the anomalous pixel, and thus perform more robust. In addition, the weights can be used to regularize non-pure-pixel-based unmixing methods, such as nonnegative matrix factorization (NMF). By incorporating the designed local neighborhood weights, a weighted nonnegative matrix factorization (WNMF) algorithm for spectral unmixing is proposed in this paper. Meanwhile, a multiplicative update rule for WNMF is presented, and the monotonic convergence of the rule is proved. Experiments on synthetic and real hyperspectral data validate the effectiveness of the designed weights.
Keywords :
geophysical image processing; image segmentation; terrain mapping; ground cover; local neighborhood weights; multiplicative update rule; nonnegative matrix factorization; nonpure-pixel-based unmixing methods; real hyperspectral data; region-segmentation pure-pixel-based endmember extraction algorithms; remotely sensed data exploitation; remotely sensed spectral images; spatial information; spectral information; spectral unmixing technique; synthetic hyperspectral data; weighted nonnegative matrix factorization algorithm; Algorithm design and analysis; Correlation; Earth; Feature extraction; Hyperspectral imaging; Image segmentation; Spectral unmixing; nonnegative matrix factorization; spatial and spectral information; weights;
Journal_Title :
Selected Topics in Applied Earth Observations and Remote Sensing, IEEE Journal of
DOI :
10.1109/JSTARS.2012.2199282