In this paper, we propose a new anomaly detection method for hyperspectral images based on two well-designed dictionaries: background dictionary and potential anomaly dictionary. In order to effectively detect an anomaly and eliminate the influence of noise, the original image is decomposed into three components: background, anomalies, and noise. In this way, the anomaly detection task is regarded as a problem of matrix decomposition. Considering the homogeneity of background and the sparsity of anomalies, the low-rank and sparse constraints are imposed in our model. Then, the background and potential anomaly dictionaries are constructed using the background and anomaly priors. For the background dictionary, a joint sparse representation (JSR)-based dictionary selection strategy is proposed, assuming that the frequently used atoms in the overcomplete dictionary tend to be the background. In order to make full use of the prior information of anomalies hidden in the scene, the potential anomaly dictionary is constructed. We define a criterion, i.e., the anomalous level of a pixel, by using the residual calculated in the JSR model within its local region. Then, it is combined with a weighted term to alleviate the influence of noise and background. Experiments show that our proposed anomaly detection method based on potential anomaly and background dictionaries construction can achieve superior results compared with other state-of-the-art methods.
|Number of pages||14|
|Journal||IEEE Transactions on Geoscience and Remote Sensing|
|Publication status||Published - 01 Nov 2018|
Bibliographical noteFunding Information:
Manuscript received June 2, 2018; revised August 15, 2018; accepted September 17, 2018. Date of publication November 1, 2018; date of current version March 25, 2019. This work was supported in part by the National Natural Science Foundation of China under Grant 61772400, Grant 61501353, Grant 61772399, Grant 91438201, and Grant 61573267. The work of H. Zhou was supported in part by U.K. EPSRC under Grant EP/N508664/1, Grant EP/R007187/1, and Grant EP/N011074/1 and in part by the Royal Society-Newton Advanced Fellowship under Grant NA160342. (Corresponding author: Xiangrong Zhang.) N. Huyan, X. Zhang, and L. Jiao are with Xidian University, Xi’an 710071, China (e-mail: firstname.lastname@example.org).
© 2018 IEEE.
Copyright 2019 Elsevier B.V., All rights reserved.
- Anomaly detection
- background dictionary
- hyperspectral images (HSIs)
- joint sparse representation (JSR)
- low rank
- potential anomaly dictionary
ASJC Scopus subject areas
- Electrical and Electronic Engineering
- Earth and Planetary Sciences(all)