A variational method for target detection in hyperspectral images

Abstract

A novel variational method using level sets that incorporate spectral angle distance in the model for automatic target detection is presented. Algorithms are presented for detecting both spatial and pixel targets. The new method is tested in tasks of unsupervised target detection in hyperspectral images with more than 100 bands, and the results are compared with a widely used region-based level sets algorithm. Additionally, techniques of band subset selection are evaluated for the reduction of data dimensionality. The proposed method is adapted for supervised target detection and its performance is compared with traditional orthogonal subspace projection and constrained signal detector for the detection of pixel targets. The method is evaluated with different complexity such as noise levels and target sizes.