Change Detection Based on Gabor Wavelet Features for Very High Resolution Remote Sensing Images

Abstract

In this letter, we propose a change detection method based on Gabor wavelet features for very high resolution (VHR) remote sensing images. First, Gabor wavelet features are extracted from two temporal VHR images to obtain spatial and contextual information. Then, the Gabor-wavelet-based difference measure (GWDM) is designed to generate the difference image. In GWDM, a new local similarity measure is defined, in which the Markov random field neighborhood system is incorporated to obtain a local relationship, and the coefficient of variation method is applied to discriminate contributions from different features. Finally, the fuzzy c-means cluster algorithm is employed to obtain the final change map. Experiments employing QuickBird and SPOT5 images demonstrate the effectiveness of the proposed approach.