Hyperspectral Anomaly Detection with Attribute and Edge-Preserving Filters

Xudong Kang; Xiangping Zhang; Shutao Li; Kenli Li; Jun Li; Jón Atli Benediktsson

doi:10.1109/TGRS.2017.2710145

Hyperspectral Anomaly Detection with Attribute and Edge-Preserving Filters

Xudong Kang
, Xiangping Zhang
, Shutao Li
, Kenli Li
, Jun Li
, Jón Atli Benediktsson

University of Iceland

University of Iceland

Research output: Contribution to journal › Article › peer-review

Abstract

A novel method for anomaly detection in hyperspectral images is proposed. The method is based on two ideas. First, compared with the surrounding background, objects with anomalies usually appear with small areas and distinct spectral signatures. Second, for both the background and the objects with anomalies, pixels in the same class are usually highly correlated in the spatial domain. In this paper, the pixels with specific area property and distinct spectral signatures are first detected with attribute filtering and a Boolean map-based fusion approach in order to obtain an initial pixel-wise detection result. Then, the initial detection result is refined with edge-preserving filtering to make full use of the spatial correlations among adjacent pixels. Compared with other widely used anomaly detection methods, the experimental results obtained on real hyperspectral data sets including airport, beach, and urban scenes demonstrate that the performance of the proposed method is quite competitive in terms of computing time and detection accuracy.

Original language	English
Article number	7994698
Pages (from-to)	5600-5611
Number of pages	12
Journal	IEEE Transactions on Geoscience and Remote Sensing
Volume	55
Issue number	10
DOIs	https://doi.org/10.1109/TGRS.2017.2710145
Publication status	Published - Oct 2017

Bibliographical note

Funding Information: Manuscript received December 19, 2016; revised April 6, 2017 and May 15, 2017; accepted May 24, 2017. Date of publication July 27, 2017; date of current version September 25, 2017. This paper was supported in part by the National Natural Science Foundation of China under Grant 61601179, in part by the National Natural Science Fund of China for Distinguished Young Scholars under Grant 61325007, in part by the National Natural Science Fund of China for International Cooperation and Exchanges under Grant 61520106001, and in part by the Science and Technology Plan Projects Fund of Hunan Province under Project 2015WK3001. (Corresponding author: Shutao Li.) X. Kang, X. Zhang, and S. Li are with the College of Electrical and Information Engineering, Hunan University, Changsha 410082, China (e-mail: [email protected]). Publisher Copyright: © 2017 IEEE.

Other keywords

Anomaly detection
Boolean map
attribute filtering
edge-preserving filtering
hyperspectral image

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10.1109/TGRS.2017.2710145

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title = "Hyperspectral Anomaly Detection with Attribute and Edge-Preserving Filters",

abstract = "A novel method for anomaly detection in hyperspectral images is proposed. The method is based on two ideas. First, compared with the surrounding background, objects with anomalies usually appear with small areas and distinct spectral signatures. Second, for both the background and the objects with anomalies, pixels in the same class are usually highly correlated in the spatial domain. In this paper, the pixels with specific area property and distinct spectral signatures are first detected with attribute filtering and a Boolean map-based fusion approach in order to obtain an initial pixel-wise detection result. Then, the initial detection result is refined with edge-preserving filtering to make full use of the spatial correlations among adjacent pixels. Compared with other widely used anomaly detection methods, the experimental results obtained on real hyperspectral data sets including airport, beach, and urban scenes demonstrate that the performance of the proposed method is quite competitive in terms of computing time and detection accuracy.",

keywords = "Anomaly detection, Boolean map, attribute filtering, edge-preserving filtering, hyperspectral image",

author = "Xudong Kang and Xiangping Zhang and Shutao Li and Kenli Li and Jun Li and Benediktsson, \{J{\'o}n Atli\}",

note = "Funding Information: Manuscript received December 19, 2016; revised April 6, 2017 and May 15, 2017; accepted May 24, 2017. Date of publication July 27, 2017; date of current version September 25, 2017. This paper was supported in part by the National Natural Science Foundation of China under Grant 61601179, in part by the National Natural Science Fund of China for Distinguished Young Scholars under Grant 61325007, in part by the National Natural Science Fund of China for International Cooperation and Exchanges under Grant 61520106001, and in part by the Science and Technology Plan Projects Fund of Hunan Province under Project 2015WK3001. (Corresponding author: Shutao Li.) X. Kang, X. Zhang, and S. Li are with the College of Electrical and Information Engineering, Hunan University, Changsha 410082, China (e-mail: shutao\[email protected]). Publisher Copyright: {\textcopyright} 2017 IEEE.",

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AU - Benediktsson, Jón Atli

N1 - Funding Information: Manuscript received December 19, 2016; revised April 6, 2017 and May 15, 2017; accepted May 24, 2017. Date of publication July 27, 2017; date of current version September 25, 2017. This paper was supported in part by the National Natural Science Foundation of China under Grant 61601179, in part by the National Natural Science Fund of China for Distinguished Young Scholars under Grant 61325007, in part by the National Natural Science Fund of China for International Cooperation and Exchanges under Grant 61520106001, and in part by the Science and Technology Plan Projects Fund of Hunan Province under Project 2015WK3001. (Corresponding author: Shutao Li.) X. Kang, X. Zhang, and S. Li are with the College of Electrical and Information Engineering, Hunan University, Changsha 410082, China (e-mail: [email protected]). Publisher Copyright: © 2017 IEEE.

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N2 - A novel method for anomaly detection in hyperspectral images is proposed. The method is based on two ideas. First, compared with the surrounding background, objects with anomalies usually appear with small areas and distinct spectral signatures. Second, for both the background and the objects with anomalies, pixels in the same class are usually highly correlated in the spatial domain. In this paper, the pixels with specific area property and distinct spectral signatures are first detected with attribute filtering and a Boolean map-based fusion approach in order to obtain an initial pixel-wise detection result. Then, the initial detection result is refined with edge-preserving filtering to make full use of the spatial correlations among adjacent pixels. Compared with other widely used anomaly detection methods, the experimental results obtained on real hyperspectral data sets including airport, beach, and urban scenes demonstrate that the performance of the proposed method is quite competitive in terms of computing time and detection accuracy.

AB - A novel method for anomaly detection in hyperspectral images is proposed. The method is based on two ideas. First, compared with the surrounding background, objects with anomalies usually appear with small areas and distinct spectral signatures. Second, for both the background and the objects with anomalies, pixels in the same class are usually highly correlated in the spatial domain. In this paper, the pixels with specific area property and distinct spectral signatures are first detected with attribute filtering and a Boolean map-based fusion approach in order to obtain an initial pixel-wise detection result. Then, the initial detection result is refined with edge-preserving filtering to make full use of the spatial correlations among adjacent pixels. Compared with other widely used anomaly detection methods, the experimental results obtained on real hyperspectral data sets including airport, beach, and urban scenes demonstrate that the performance of the proposed method is quite competitive in terms of computing time and detection accuracy.

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Hyperspectral Anomaly Detection with Attribute and Edge-Preserving Filters

Abstract

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