Spectral–Spatial Complementary Decision Fusion for Hyperspectral Anomaly Detection

Abstract

Hyperspectral anomaly detection has become an important branch of remote–sensing image processing due to its important theoretical value and wide practical application prospects. However, some anomaly detection methods mainly exploit the spectral feature and do not make full use of spatial features, thus limiting the performance improvement of anomaly detection methods. Here, a novel hyperspectral anomaly detection method, called spectral–spatial complementary decision fusion, is proposed, which combines the spectral and spatial features of a hyperspectral image (HSI). In the spectral dimension, the three–dimensional Hessian matrix was first utilized to obtain three–directional feature images, in which the background pixels of the HSI were suppressed. Then, to more accurately separate the sparse matrix containing the anomaly targets in the three–directional feature images, low–rank and sparse matrix decomposition (LRSMD) with truncated nuclear norm (TNN) was adopted to obtain the sparse matrix. After that, the rough detection map was obtained from the sparse matrix through finding the Mahalanobis distance. In the spatial dimension, two–dimensional attribute filtering was employed to extract the spatial feature of HSI with a smooth background. The spatial weight image was subsequently obtained by fusing the spatial feature image. Finally, to combine the complementary advantages of each dimension, the final detection result was obtained by fusing all rough detection maps and the spatial weighting map. In the experiments, one synthetic dataset and three real–world datasets were used. The visual detection results, the three–dimensional receiver operating characteristic (3D ROC) curve, the corresponding two–dimensional ROC (2D ROC) curves, and the area under the 2D ROC curve (AUC) were utilized as evaluation indicators. Compared with nine state–of–the–art alternative methods, the experimental results demonstrate that the proposed method can achieve effective and excellent anomaly detection results.

Spectral–Spatial Feature Fusion for Hyperspectral Anomaly Detection

“…Although these approaches mentioned above use spectral and spatial information in a cascaded manner for hyperspectral anomaly detection, they ignore the complementary property between the spectral and spatial dimension. To alleviate this issue, several publications focus on the fusion of spectral–spatial information [ 49 , 50 , 51 ]. They use different spectral–spatial feature extractors and a simple concatenation way for hyperspectral anomaly detection.…”

Hyperspectral Anomaly Detection with Auto-Encoder and Independent Target

“…Moreover, the low-rank constraint in [13] is also converted to compute the F-norm of the low-rank component that also uses the same concept of singular analysis. In [27], truncated nuclear norm (TNN) was adopted to obtain the low-rank matrix for decomposing the original data into low-rank and sparse parts. For BKG and anomaly target representation, multivariate Gaussian distribution assumption was used for modeling [28].…”