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.