Anomaly detection using morphology-based collaborative representation in hyperspectral imagery

Abstract

A nonparametric anomaly detection method is proposed in this paper which does not consider any probability density function for data, and so can work well for real hyperspectral image containing complicated and non-normal background. Since the predominant part of an image is composed by background pixels and because of similarity of neighboring pixels in a local region, each background pixel can be approximated from its surrounding samples. To this end, the collaborative representation with a simple and closed form solution is used. To more benefit from the spatial information of hyperspectral image, the morphological filters are applied for extraction of contextual features and a decision fusion rule is used to utilize the spatial information of all principal components of data. The experimental results show the superior performance of the proposed detector compared to several state-of-the-art anomalous target detection methods. The area under ROC curve (AUC) values achieved by the proposed detector are 95.53, 96.34 and 99.72 for San Diego hyperspectral image, Indian hyperspectral image and the WorldView-2 multispectral image, respectively.

Ensemble and Random Collaborative Representation-Based Anomaly Detector for Hyperspectral Imagery

“…The proposed ERCRD shows its advantages over many existing HAD methods, such as GRX [15], LRX [15], SSRX [18], CBAD [17], and LSMAD [34]. In comparison with the CRD [38] and its variants [39], [41], [42], we also validate that our ERCRD method is able to attain considerable or better detection accuracy with even much less implementation time.…”

“…Local PCAroCRD [41], MCRD [39], and RCRD [42]. It can be seen that the detection performance of the CRD, Local PCAroCRD, MCRD and RCRD methods are sensitive to the inner window size w in and the outer window size w out .…”

“…On the basis of the CRD, many variants have been proposed recently. To make better use of the spatial features from HSI, a morphology-based collaborative representation detector (MCRD) [39] was presented. Principal component analysis was applied in CRD method to remove outliers (PCAro-CRD) [41] first uses PCA to extract the main background pixel information, and then predicts the anomalies with collaborative representation.…”

Polarimetric SAR image classification using binary coding‐based polarimetric‐morphological features

“…At first, a set of polarimetric features obtained from the coherency matrix is introduced. The morphological operators have shown great efficiency for remote sensing image analysis [28,29]. Morphological filters not only are effective for noise decreasing but also they superiorly extract spatial features such as shape and geometrical characteristics from the scene.…”