Integration of Spatial and Spectral Information by Means of Sparse Representation-Based Classification for Hyperspectral Imagery View Full Text


Ontology type: schema:Chapter     


Chapter Info

DATE

2015

AUTHORS

Sen Jia , Yao Xie , Zexuan Zhu

ABSTRACT

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. Meanwhile, spatial information, that 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 spatial-neighborhood-integrated SRC method, abbreviated as SN-SRC, to jointly consider the spectral and spatial neighborhood information of each pixel to explore the spectral and spatial coherence by the SRC method. Experimental results have shown that the proposed SN-SRC approach could achieve better performance than the other state-of-the-art methods, especially with limited training samples. More... »

PAGES

117-126

Book

TITLE

Proceedings of the 18th Asia Pacific Symposium on Intelligent and Evolutionary Systems - Volume 2

ISBN

978-3-319-13355-3
978-3-319-13356-0

Author Affiliations

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/978-3-319-13356-0_10

DOI

http://dx.doi.org/10.1007/978-3-319-13356-0_10

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1003333992


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