Region-based collaborative sparse unmixing of hyperspectral imagery

Jiaojiao Li; Qian Du; Yunsong Li

doi:10.1117/12.2224489

19 May 2016 Region-based collaborative sparse unmixing of hyperspectral imagery

Jiaojiao Li, Qian Du, Yunsong Li

Proceedings Volume 9874, Remotely Sensed Data Compression, Communications, and Processing XII; 98740S (2016) https://doi.org/10.1117/12.2224489
Event: SPIE Commercial + Scientific Sensing and Imaging, 2016, Baltimore, MD, United States

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.

Citation Download Citation

Jiaojiao Li, Qian Du, and Yunsong Li "Region-based collaborative sparse unmixing of hyperspectral imagery", Proc. SPIE 9874, Remotely Sensed Data Compression, Communications, and Processing XII, 98740S (19 May 2016); https://doi.org/10.1117/12.2224489

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