Unsupervised nearest regularized subspace for anomaly detection in hyperspectral imagery

A method of unsupervised nearest regularized subspace is proposed for anomaly detection in hyperspectral imagery. Based on a dual window, an approximation of each testing pixel is a representation of surrounding data via a linear combination, for which the weight vector is calculated by distance-weighted Tikhonov regularization. Proposed detector returns the similarity measurement between the testing pixel and its approximation. Experimental results for real hyperspectral data of proposed approach are demonstrated and compared to other traditional detection techniques.