Hyperspectral Image Classification by Exploring Low-Rank Property in Spectral or/and Spatial Domain

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

16 Scopus Citations
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Detail(s)

Original languageEnglish
Article number7835224
Pages (from-to)2910-2921
Number of pages12
Journal / PublicationIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Volume10
Issue number6
Publication statusPublished - Jun 2017

Abstract

Within-class spectral variation, which is caused by varied imaging conditions, such as changes in illumination, environmental, atmospheric, and temporal conditions, significantly degrades the performance of hyperspectral image classification. Recent studies have shown that such spectral variation can be alleviated by exploring the low-rank property in the spectral domain, especially based on the low-rank subspace assumption. In this paper, the low-rank subspace assumption is approached by exploring the low-rank property in the local spectral domain. In addition, the low-rank property in the spatial domain is also explored to alleviate spectral variation. As a result, two novel spectral-spatial low-rank (SSLR) strategies are designed to alleviate spectral variation by exploring the low-rank property in both spectral and spatial domains. Experimental results on two benchmark hyperspectral datasets demonstrate that exploring the low-rank property in local spectral space can help to alleviate spectral variation and improve the performance of classification obviously for all tested data, while exploring the low-rank property in spatial space is more effective for images presenting large homogeneous areas.

Citation Format(s)

Hyperspectral Image Classification by Exploring Low-Rank Property in Spectral or/and Spatial Domain. / Mei, Shaohui; Bi, Qianqian; Ji, Jingyu; Hou, Junhui; Du, Qian.

In: IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol. 10, No. 6, 7835224, 06.2017, p. 2910-2921.

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review