Learnable Background Endmember With Subspace Representation for Hyperspectral Anomaly Detection

Hyperspectral anomaly detection (HAD) aims to label each hyperspectral image (HSI) pixel as background or anomaly, in a totally unsupervised manner. Thus, a fine background representation is vital to obtain good HAD performance. This article introduces background endmember representation and proposes a novel HAD method termed learnable background endmember with subspace representation (LEBSR). First, the HSI is unmixed to simultaneously obtain the background endmembers and their abundances. The three constraints of
$p$
-norm, sum-to-one, and nonnegativity work together to promote a more meaningful and accurate background endmember representation. In addition, a mapping matrix with orthogonality is jointly optimized to transform the priori backgrounds into the background endmember subspace, and then the mapped priori backgrounds are approximated to the low-rank representation (LRR) with the background endmembers. With the methodology, the backgrounds can be well reconstructed under the guidance of the priori background information to accurately detect anomalous pixels with the reconstructed residuals. The experimental results on several HSI datasets verify the superior performance of LEBSR than the state-of-the-art methods.
https://github.com/HalongL/HAD-LEBSR