Hyperspectral Anomaly Detection with Attribute and Edge-Preserving Filters

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

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

119 Citations (Scopus)


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 languageEnglish
Article number7994698
Pages (from-to)5600-5611
Number of pages12
JournalIEEE Transactions on Geoscience and Remote Sensing
Issue number10
Publication statusPublished - 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: shutao_li@hnu.edu.cn).

Publisher Copyright:
© 2017 IEEE.

Other keywords

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


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