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

Shaohui Mei; Qianqian Bi; Jingyu Ji; Junhui Hou; Qian Du

doi:10.1109/IGARSS.2016.7729857

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

Shaohui Mei, Qianqian Bi, Jingyu Ji, Junhui Hou, Qian Du

School of Electronics and Information

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

1 Scopus citations

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.

Original language	English
Title of host publication	2016 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2016 - Proceedings
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	3314-3317
Number of pages	4
ISBN (Electronic)	9781509033324
DOIs	https://doi.org/10.1109/IGARSS.2016.7729857
State	Published - 1 Nov 2016
Event	36th IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2016 - Beijing, China Duration: 10 Jul 2016 → 15 Jul 2016

Publication series

Name	International Geoscience and Remote Sensing Symposium (IGARSS)
Volume	2016-November

Conference

Conference	36th IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2016
Country/Territory	China
City	Beijing
Period	10/07/16 → 15/07/16

Keywords

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

Access to Document

10.1109/IGARSS.2016.7729857

Cite this

Mei, S., Bi, Q., Ji, J., Hou, J., & Du, Q. (2016). How to fully explore the low-rank property for data recovery of hyperspectral images. In 2016 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2016 - Proceedings (pp. 3314-3317). Article 7729857 (International Geoscience and Remote Sensing Symposium (IGARSS); Vol. 2016-November). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/IGARSS.2016.7729857

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title = "How to fully explore the low-rank property for data recovery of hyperspectral images",

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.",

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author = "Shaohui Mei and Qianqian Bi and Jingyu Ji and Junhui Hou and Qian Du",

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Mei, S, Bi, Q, Ji, J, Hou, J & Du, Q 2016, How to fully explore the low-rank property for data recovery of hyperspectral images. in 2016 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2016 - Proceedings., 7729857, International Geoscience and Remote Sensing Symposium (IGARSS), vol. 2016-November, Institute of Electrical and Electronics Engineers Inc., pp. 3314-3317, 36th IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2016, Beijing, China, 10/07/16. https://doi.org/10.1109/IGARSS.2016.7729857

How to fully explore the low-rank property for data recovery of hyperspectral images. / Mei, Shaohui; Bi, Qianqian; Ji, Jingyu et al.
2016 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2016 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2016. p. 3314-3317 7729857 (International Geoscience and Remote Sensing Symposium (IGARSS); Vol. 2016-November).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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Mei S, Bi Q, Ji J, Hou J, Du Q. How to fully explore the low-rank property for data recovery of hyperspectral images. In 2016 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2016 - Proceedings. Institute of Electrical and Electronics Engineers Inc. 2016. p. 3314-3317. 7729857. (International Geoscience and Remote Sensing Symposium (IGARSS)). doi: 10.1109/IGARSS.2016.7729857

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

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