Specific changes detection in visible-band VHR images using classification likelihood space

Object-based post-classification change detection methods are effective for very high resolution images, but their effectiveness is limited by incomplete class hierarchy and complex image object comparison. In this paper, a novel Classification Likelihood Space (CLS) is proposed to synthesize the effective object-based image analysis and easy-to-implement post-classification comparison, serving as a well tradeoff between performance and complexity. The proposed algorithm is tested on a dataset which comprises 102 pairs of visible-band very high resolution real satellite images, and a great improvement is observed over traditional post-classification comparison.