EXPLOITING STRUCTURED SPARSITY FOR HYPERSPECTRAL ANOMALY DETECTION

Abstract

Sparse representation-based background modelling facilitates much recent progress in hyperspectral anomaly detection (AD). The sparse representation of background often exhibits underlying structure, which is crucial to distinguish between background and anomaly. However, how to exploit such underlying structure is still challenging. To address this problem, we present a novel hyperspectral AD method, which can exploit the structured sparsity in modeling the background more accurately. With the plausible background area detected by a local RX detector, a robust background spectrum dictionary is learned in a principal component analysis way. A reweighted Laplace priorbased structured sparse representation model is then employed to reconstruct the spectrum of each pixel. With considering the structured sparsity in representation, the background pixels can be reconstructed more accurately than the anomaly ones, which thus can be detected based on the reconstruction error. To further improve the detection performance, an intracluster reconstruction model is developed to exploit the spatial similarity among the background pixels in the same cluster. The anomaly pixels can then be detected based on the cost of intracluster reconstruction error. By linearly combining these two detection results, improvement is obviously achieved on detection accuracy. Experimental results on both simulated and real-world data sets demonstrate that the proposed method outperforms several state of- the-art hyperspectral AD methods.

Existing System

Linear mixing model (LMM), background joint sparse representation (BJSR)

Proposed System

The proposed structured sparse representation model mainly focuses on modeling the spectral anomaly characteristic for detection; however, the spatial anomaly, which can also benefit the detection performance, has not been considered yet. In this paper, we further propose another detector based on spatial anomaly. Concretely, the HSI is divided into several spatially homogeneous clusters by k-means according to the spectral similarity. An intracluster reconstruction model is then introduced to describe the spatial similarity between background pixels that belong to the same cluster. Since the background pixel can be reconstruction accurately by other pixels in the same cluster while the anomaly will not, the cost for violating this constraint can be utilized to formulate another anomaly detector. To simultaneously exploit the spectral anomaly and spatial anomaly, the detection results from the structured sparse representation model and the intracluster reconstruction model are linearly integrated to distinguish the anomaly from background precisely. Extensive experimental results on both simulated and real-world data sets demonstrate that the proposed method outperforms several state-of-the-art hyperspectral AD methods.

Architecture

BLOCK DIAGRAM