Hyperspectral Anomaly Detection via Sparse Representation and Collaborative Representation

Abstract

Sparse representation (SR)-based approaches and collaborative representation (CR)-based methods are proved to be effective to detect the anomalies in a hyperspectral image (HSI). Nevertheless, the existing methods for achieving hyperspectral anomaly detection (HAD) generally only consider one of them, failing to comprehensively exploit them to further promote the detection performance. To address the issue, a novel HAD method, which integrates both sparse representation and collaborative representation (SRCR), is proposed in this paper. To be specific, a SR model, whose overcomplete dictionary is generated by means of the density-based clustering algorithm and superpixel segmentation method, is firstly constructed for each pixel in an HSI. Then, for each pixel in an HSI, the used atoms in SR model are sifted to form the background dictionary corresponding to the CR model. To fully exploit both SR and CR information, we further combine the residual features obtained from both SR and CR model by the nonlinear transformation function to generate the response map. Finally, to preserve contour information of the objects, a postprocessing operation with guided filter is imposed into the response map to acquire the detection result. Experiments conducted on simulated and real data sets demonstrate that the proposed SRCR outperforms the state-of-the-art methods.

SAM-Induced Pseudo Fully Supervised Learning for Weakly Supervised Object Detection in Remote Sensing Images

“…Compared with fully supervised object detection (FSOD) [1][2][3][4][5][6][7][8], the major advantage of weakly supervised object detection (WSOD) is that only image-level category annotations are necessary for training the WSOD model. Considering the low cost of data labeling, WSOD has been widely researched in recent years [9][10][11][12][13][14][15][16][17] and has been applied in scene classification [18,19], disaster detection [20,21], military [22,23], and other applications [24][25][26][27][28][29].…”

Recursive RX with Extended Multi-Attribute Profiles for Hyperspectral Anomaly Detection

“…A hyperspectral image (HSI) provides a rich source of spectral and spatial information about the materials in the scene [1], and it has been widely applied in many remote sensing areas [2][3][4], including classification [5][6][7][8][9], clustering [10][11][12], unmixing [13][14][15], image denoising [16,17], band selection [18,19], change detection [20], and target detection [21][22][23][24][25] or anomaly detection [26][27][28][29][30][31][32][33]. Among these applications, anomaly detection (AD) plays a significant role in military surveillance [34], agriculture [35], mineral exploration [36], environmental monitoring [34], maritime rescue [37], and so on [27,[38][39][40][41].…”

Dual-View Hyperspectral Anomaly Detection via Spatial Consistency and Spectral Unmixing

“…Density-based methods, such as local outlier factor [30], connectivity-based outlier factor [31], etc., assume that the cluster density of the normal sample points is higher than that of the abnormal sample points. Moreover, the collaborative representation detection (CRD) for anomalies has received significant attention [32]. Li et al [33] proposed a collaborative representation and a kernel version for hyperspectral anomaly detection.…”