Hyperspectral Anomaly Detection via Dictionary Construction-Based Low-Rank Representation and Adaptive Weighting

Abstract

Anomaly detection (AD), which aims to distinguish targets with significant spectral differences from the background, has become an important topic in hyperspectral imagery (HSI) processing. In this paper, a novel anomaly detection algorithm via dictionary construction-based low-rank representation (LRR) and adaptive weighting is proposed. This algorithm has three main advantages. First, based on the consistency with AD problem, the LRR is employed to mine the lowest-rank representation of hyperspectral data by imposing a low-rank constraint on the representation coefficients. Sparse component contains most of the anomaly information and can be used for anomaly detection. Second, to better separate the sparse anomalies from the background component, a background dictionary construction strategy based on the usage frequency of the dictionary atoms for HSI reconstruction is proposed. The constructed dictionary excludes possible anomalies and contains all background categories, thus spanning a more reasonable background space. Finally, to further enhance the response difference between the background pixels and anomalies, the response output obtained by LRR is multiplied by an adaptive weighting matrix. Therefore, the anomaly pixels are more easily distinguished from the background. Experiments on synthetic and real-world hyperspectral datasets demonstrate the superiority of our proposed method over other AD detectors.

Deep Learning for Land Cover Classification Using Only a Few Bands

“…Hyperspectral data have been used for chemical agent detection and classification [1,2], small target detection [3,4], fire damage assessment [5,6], anomaly detection [7][8][9][10][11][12][13], border monitoring [14], change detection [15][16][17][18], and mineral map abundance estimation on Mars [19,20]. Land cover classification is another application area where hyperspectral data are used extensively [21].…”

Hyperspectral Anomaly Detection via Graphical Connected Point Estimation and Multiple Support Vector Machines

“…Specifically, the collaborative representation-based detector (CRD) [13] assumes that background pixels can be well approximated by their spatial neighborhoods, and Chen et al [14] has proposed an approach based on a sparse representation (SRD), which assumes that each test pixel can be represented by a few atoms in the dictionary. Another popular method called low rank representation (LRR) [15]- [17] assumes that the hyperspectral dataset can be represented by a constructed dictionary with various constraints on the coefficient matrix. A series of atoms can be combined to represent the pixels within a small neighborhood.…”

A Hyperspectral Anomaly Detection Algorithm Based on Morphological Profile and Attribute Filter with Band Selection and Automatic Determination of Maximum Area

“…Target detection methods in HSI are mainly classified into supervised and unsupervised categories, based on the utilization of prior information. Anomaly detection is an unsupervised target detection method that aims to identify interesting objects that are different from their surroundings, in terms of spatial and spectral domain, without any prior information about the objects [11][12][13]. These objects consist of a relatively small number of pixels compared to the total number of pixels in the scene.…”