Hyperspectral Anomaly Detection Based on Multi-Feature Joint Trilateral Filtering and Cooperative Representation

Abstract

The hyperspectral anomaly detection algorithm based on collaborative representation does not fully utilize the two-dimensional spatial features in hyperspectral images. It also has the problem that anomalous pixels will pollute the background dictionary and induce bad detection performance. Based on these, this paper proposes a hyperspectral anomaly detection algorithm based on multiple feature joint trilateral filtering and collaborative representation. The algorithm first introduces an improved trilateral filtering algorithm, which utilizes the spatial features of hyperspectral images. The preliminary positions of possible abnormal objects are determined. On this basis, abnormal removal and background filling are performed to obtain a purified background. Finally, the purified background and the original hyperspectral image are used for joint collaborative representation to complete the detection. Experimental results show that the detection accuracy of the algorithm proposed in this paper was efficiently improved by introducing multiple feature joint trilateral filtering, where multiple spatial spectrum features are utilized.

Exploration of Data Scene Characterization and 3D ROC Evaluation for Hyperspectral Anomaly Detection

“…As noted above, both the P D and P F of a 2D ROC inter-act each other and cannot stand alone to be used for analysis. For example, it is often the case that two detectors may have very close AUC (D,F) values within a negligible difference but behave quite differently as shown in [23][24][25]. This says that using only AUC (D,F) cannot evaluate the effectiveness of anomaly detectability and BS.…”