A novel technique for anomalous change detection in hyperspectral images is presented. It adaptively measures the spectral distance between corresponding pixels in multi-temporal images by exploiting the local estimates of the signal to noise ratio for each spectral component of the pixel under test. Different metrics have been compared, based on multidimensional angular distance. Results obtained by applying the new algorithm to real data are presented and discussed. Performance evaluation highlighted the effectiveness of the proposed approach with respect to traditional methods, resulting in a consistent improvement of both the probability of detection of changes and the capability of suppressing the background.

Date Published: 19 November 2012
PDF: 8 pages
Proc. SPIE 8542, Electro-Optical Remote Sensing, Photonic Technologies, and Applications VI, 854220 (19 November 2012);

Published in SPIE Proceedings Vol. 8542:
Electro-Optical Remote Sensing, Photonic Technologies, and Applications VI
Gary W. Kamerman; Gary J. Bishop; Mark T. Gruneisen; Keith L. Lewis; Miloslav Dusek; Richard C. Hollins; Ove Steinvall; John Gonglewski; John G. Rarity; Thomas J. Merlet, Editor(s)