Journal
SOFT COMPUTING
Volume 20, Issue 12, Pages 4659-4668Publisher
SPRINGER
DOI: 10.1007/s00500-014-1505-4
Keywords
Hyperspectral imagery; Sparse representation-based classification; Spatial information
Categories
Funding
- National Natural Science Foundation of China [61271022]
- Guang-dong College Excellent Young Teacher Training Program [Yq2013143]
- Shenzhen Scientific Research and Development Funding Program [JCYJ20140418095735628, ZDSY20121019111146499, JSGG2012 1026111056204, JCYJ20120817163755063]
- National Basic Research and Development Program [2012CB719905]
Ask authors/readers for more resources
Recently, sparse representation-based classification (SRC), which assigns a test sample to the class with minimum representation error via a sparse linear combination of all the training samples, has successfully been applied to hyperspectral imagery. Alternatively, spatial information, which means the adjacent pixels belong to the same class with a high probability, is a valuable complement to the spectral information. In this paper, we have presented a new spectral-spatial-combined SRC method, abbreviated as SSSRC or , to jointly consider the spectral and spatial neighborhood information of each pixel to explore the spectral and spatial coherence by the SRC method. Furthermore, a fast interference-cancelation operation is adopted to accelerate the classification procedure of , named . Experimental results have shown that both the proposed SRC-based approaches, and , could achieve better performance than the other state-of-the-art methods.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available