Coherent change detection (CCD) provides a way for analysts and detectors to find ephemeral features that would otherwise be invisible in traditional synthetic aperture radar (SAR) imagery. However, CCD can produce false alarms in regions of the image that have low SNR and high vegetation areas. The method proposed looks to eliminate these false alarm regions by creating a mask which can then be applied to change products. This is done by utilizing both the magnitude and coherence feature statistics of a scene. For each feature, the image is segmented into groups of similar pixels called superpixels. The method then utilizes a training phase to model each terrain that the user deems as capable of supporting change and statistically comparing superpixels in the image to the modeled terrain types. Finally, the method combines the features using probabilistic fusion to create a mask that a user can threshold and apply to a change product for human analysis or automatic feature detectors.

Date Published: 12 May 2016
PDF: 14 pages
Proc. SPIE 9829, Radar Sensor Technology XX, 98291N (12 May 2016); doi: 10.1117/12.2222185

Published in SPIE Proceedings Vol. 9829:
Radar Sensor Technology XX
Kenneth I. Ranney; Armin Doerry, Editor(s)