How to fully explore the low-rank property for data recovery of hyperspectral images

Research output: Chapters, Conference Papers, Creative and Literary Works (RGC: 12, 32, 41, 45)32_Refereed conference paper (with ISBN/ISSN)peer-review

1 Scopus Citations
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Author(s)

Detail(s)

Original languageEnglish
Title of host publicationInternational Geoscience and Remote Sensing Symposium (IGARSS)
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages3314-3317
Volume2016-November
ISBN (Print)9781509033324
Publication statusPublished - 1 Nov 2016
Externally publishedYes

Publication series

Name
Volume2016-November

Conference

Title36th IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2016
PlaceChina
CityBeijing
Period10 - 15 July 2016

Abstract

The performance of hyperspectral classification is affected by within-class spectral variation since different materials may present similar spectral signatures. In this paper, we investigate how to fully use the low-rank property of hyperspectral images to alleviate spectra variation. Particulary, two effective strategies that explore the low-rank property in local spectral and spatial space are proposed. According to experimental results, we conclude that exploring the low-rank property in local spectral-spatial 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.

Research Area(s)

  • hyperspectral classification, Low-rank, Robust Principal Component Analysis (R-PCA), spectral variation

Citation Format(s)

How to fully explore the low-rank property for data recovery of hyperspectral images. / Mei, Shaohui; Bi, Qianqian; Ji, Jingyu; Hou, Junhui; Du, Qian.

International Geoscience and Remote Sensing Symposium (IGARSS). Vol. 2016-November Institute of Electrical and Electronics Engineers Inc., 2016. p. 3314-3317 7729857.

Research output: Chapters, Conference Papers, Creative and Literary Works (RGC: 12, 32, 41, 45)32_Refereed conference paper (with ISBN/ISSN)peer-review