Multispectral vegetation detection for improved SAR CCD

Synthetic Aperture Radar Coherent Change Detection's (SAR CCD) sensitivity to changes in ground surface height is coupled with sensitivity to other environmental changes such as minor movement in vegetation. The CCD Clutter Location, Estimation and Negation (CLEAN) algorithm decreases the false alarm rate in SAR CCD change pattern detection algorithms using intensity information in SAR images to discriminate false alarms from changes of interest. Unfortunately, CLEAN has difficulty identifying vegetation using only SAR imagery and vegetation is problematic in SAR CCD. In this paper, we propose an extension to CLEAN that fuses information from multispectral imagery with SAR intensity information for more robust vegetation classification. Experimental results show that our algorithm significantly improves change identification in SAR CCD.