Target Detection in Hyperspectral Images using Basic Thresholding Classifier

In this letter, we propose target detector version of recently introduced basic thresholding classifier for hyperspectral images. The proposed technique is a sparsity-based low complexity detector which achieves high detection rates with very low false alarm rates and performs extremely rapidly. We also propose a new decision metric, background model, and spatial smoothing procedure in order to increase the detection probability further. Experiments show that the presented method outperforms the other state-of-the-art sparsity-based approaches.