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Proceedings Paper

Region-based collaborative sparse unmixing of hyperspectral imagery
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Paper Abstract

Sparse unmixing (SU) has been investigated to select a small number of endmembers from a large spectral library, which is a pixel-based technique. In image-based collaborative sparse unmxing (CSU) techniques, pixels are forced to select the same small set of endmembers. In reality, the same small set of endmembers may be responsible for pixel construction within a homogeneous area. For an entire image, the endmember sets are often different. So, in this paper, we propose a region-based collaborative sparse unmixing (RCSU) algorithm, and the region may include nonlocal areas as long as they belong to the same type of homogeneous segments. Experimental results show that the overall performance of the proposed RCSU algorithm is better than that of image-based CSU or pixel-based SU.

Paper Details

Date Published: 19 May 2016
PDF: 6 pages
Proc. SPIE 9874, Remotely Sensed Data Compression, Communications, and Processing XII, 98740S (19 May 2016); doi: 10.1117/12.2224489
Show Author Affiliations
Jiaojiao Li, Xidian Univ. (China)
Qian Du, Mississippi State Univ. (United States)
Yunsong Li, Xidian Univ. (China)

Published in SPIE Proceedings Vol. 9874:
Remotely Sensed Data Compression, Communications, and Processing XII
Bormin Huang; Chein-I Chang; Chulhee Lee, Editor(s)

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