Abstract

Generalized Kittler and Illingworth minimum-error thresholding (GKIT) algorithm was proposed by G.Moser for change detection in synthetic aperture radar (SAR) images with non-Gussion distribution. In this paper, we present an improved GKIT approach for unsupervised change detection from synthetic aperture radar (SAR) amplitude images by relaxing the demand of the same equivalent number of looks (ENL) in the GKIT approach based on Nakagami model. Experimental results on actual SAR images are given to demonstrate the validity of our proposed method.

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