A subpixel spatial-spectral feature mining for hyperspectral image classification

Xiang Xu; Jun Li; Yanning Zhang; Shutao Li

doi:10.1109/IGARSS.2018.8518605

A subpixel spatial-spectral feature mining for hyperspectral image classification

Xiang Xu, Jun Li, Yanning Zhang, Shutao Li

School of Computer Science

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

6 Scopus citations

Abstract

This paper presents a subpixel spatial-spectral feature minin approach for hyperspectral image classification. First, a re gional clustering-based spatial preprocessing (RCSPP) strat egy is introduced to identify the endmember signatures from the original image. Then, a partial unmixing model of mix ture tuned matched filtering (MTMF) is adopted to estimat the abundance maps. Finally, the morphological componen analysis (MCA) is adopted to decompose the abundance ma into different spatial morphological components, and the s moothness components are chosen for classification. The ex perimental results reveal that the obtained subpixel spatial spectral feature can lead to very good classification accura cies.

Original language	English
Title of host publication	2018 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2018 - Proceedings
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	8476-8479
Number of pages	4
ISBN (Electronic)	9781538671504
DOIs	https://doi.org/10.1109/IGARSS.2018.8518605
State	Published - 31 Oct 2018
Event	38th Annual IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2018 - Valencia, Spain Duration: 22 Jul 2018 → 27 Jul 2018

Publication series

Name	International Geoscience and Remote Sensing Symposium (IGARSS)
Volume	2018-July

Conference

Conference	38th Annual IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2018
Country/Territory	Spain
City	Valencia
Period	22/07/18 → 27/07/18

Keywords

Hyperspectral imag classification
Mor phological component analysis (MCA)
Partial unmixing
Regional clusteri based spatial preprocessing (RCSPP)
subpixel feature mining

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10.1109/IGARSS.2018.8518605

Cite this

Xu, X., Li, J., Zhang, Y., & Li, S. (2018). A subpixel spatial-spectral feature mining for hyperspectral image classification. In 2018 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2018 - Proceedings (pp. 8476-8479). Article 8518605 (International Geoscience and Remote Sensing Symposium (IGARSS); Vol. 2018-July). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/IGARSS.2018.8518605

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title = "A subpixel spatial-spectral feature mining for hyperspectral image classification",

abstract = "This paper presents a subpixel spatial-spectral feature minin approach for hyperspectral image classification. First, a re gional clustering-based spatial preprocessing (RCSPP) strat egy is introduced to identify the endmember signatures from the original image. Then, a partial unmixing model of mix ture tuned matched filtering (MTMF) is adopted to estimat the abundance maps. Finally, the morphological componen analysis (MCA) is adopted to decompose the abundance ma into different spatial morphological components, and the s moothness components are chosen for classification. The ex perimental results reveal that the obtained subpixel spatial spectral feature can lead to very good classification accura cies.",

keywords = "Hyperspectral imag classification, Mor phological component analysis (MCA), Partial unmixing, Regional clusteri based spatial preprocessing (RCSPP), subpixel feature mining",

author = "Xiang Xu and Jun Li and Yanning Zhang and Shutao Li",

note = "Publisher Copyright: {\textcopyright} 2018 IEEE.; 38th Annual IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2018 ; Conference date: 22-07-2018 Through 27-07-2018",

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Xu, X, Li, J, Zhang, Y & Li, S 2018, A subpixel spatial-spectral feature mining for hyperspectral image classification. in 2018 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2018 - Proceedings., 8518605, International Geoscience and Remote Sensing Symposium (IGARSS), vol. 2018-July, Institute of Electrical and Electronics Engineers Inc., pp. 8476-8479, 38th Annual IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2018, Valencia, Spain, 22/07/18. https://doi.org/10.1109/IGARSS.2018.8518605

A subpixel spatial-spectral feature mining for hyperspectral image classification. / Xu, Xiang; Li, Jun; Zhang, Yanning et al.
2018 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2018 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2018. p. 8476-8479 8518605 (International Geoscience and Remote Sensing Symposium (IGARSS); Vol. 2018-July).

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

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AB - This paper presents a subpixel spatial-spectral feature minin approach for hyperspectral image classification. First, a re gional clustering-based spatial preprocessing (RCSPP) strat egy is introduced to identify the endmember signatures from the original image. Then, a partial unmixing model of mix ture tuned matched filtering (MTMF) is adopted to estimat the abundance maps. Finally, the morphological componen analysis (MCA) is adopted to decompose the abundance ma into different spatial morphological components, and the s moothness components are chosen for classification. The ex perimental results reveal that the obtained subpixel spatial spectral feature can lead to very good classification accura cies.

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KW - Mor phological component analysis (MCA)

KW - Partial unmixing

KW - Regional clusteri based spatial preprocessing (RCSPP)

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Xu X, Li J, Zhang Y, Li S. A subpixel spatial-spectral feature mining for hyperspectral image classification. In 2018 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2018 - Proceedings. Institute of Electrical and Electronics Engineers Inc. 2018. p. 8476-8479. 8518605. (International Geoscience and Remote Sensing Symposium (IGARSS)). doi: 10.1109/IGARSS.2018.8518605

A subpixel spatial-spectral feature mining for hyperspectral image classification

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