Change detection for high resolution image based on pyramid mean shift smoothness and morphology

Change detection techniques for remote sensing images are increasingly applied to many fields, such as disaster monitoring, vegetation coverage analysis and so on. With the increase of image spatial resolution, noises and details increased significantly, compared with the low-resolution image. How to improve the accuracy of change detection for high resolution image has been a critical topic. In this paper, a new method for high resolution image change detection based on pyramid mean shift smoothness and morphology is proposed. Firstly, the difference image is generated by fusing the difference feature and log difference feature based on stationary wavelet transform. Secondly, two-layer pyramid mean shift smoothness algorithm is applied to highlight the objects that may be changed and to eliminate interference regions, meanwhile, to retain the obviously different features. Thirdly, in order to enhance the contrast between the change objects and unchanged regions, the improved frequency-tuned saliency detection strategy is utilized to further enhance the change objects. Lastly, change objects are extracted by the fuzzy local C-means cluster algorithm and the final change map is generated by morphological operation. The method has been tested on four-temporal datasets, meanwhile, compared with other typical methods.