Hyperspectral Anomaly Detection via a Sparsity Score Estimation Framework

Anomaly detection has become an important topic in hyperspectral imagery (HSI) analysis over the last 20 years. HSIs usually possess complexly cluttered spectral signals due to the complicated conditions of the land-cover distribution. This in turn makes it difficult to obtain an accurate background estimation to distinguish the anomaly targets. The sparse learning technique provides a way to obtain an implicit background representation with the learned dictionary and corresponding sparse codes. In this paper, we explore the background/anomaly information content for each atom of the learned dictionary, from an analysis based on the frequency of the dictionary atoms for HSI reconstruction. From this perspective, we propose a novel sparsity score estimation framework for hyperspectral anomaly detection. First, an overcomplete dictionary and the corresponding sparse code matrix are obtained from the HSI. The frequency of each dictionary atom for reconstruction, which is also called the atom usage probability, is then estimated from the sparse code matrix. Finally, the estimated frequencies are transformed to the sparsity score for each pixel, which can be seen as the degree of anomalousness. In the proposed detection framework, two strategies are proposed to enhance the diversity between the background and anomaly information in the learned dictionary: 1) dictionary-based background feature transformation and 2) dictionary iterative reweighting. A series of real-world HSI data sets is utilized to evaluate the performance of the proposed framework. The experimental results show that the proposed framework achieves a superior performance compared to some of the state-of-the-art anomaly detection methods.