Remotely sensed change detection using multiresolution analysis and motion estimation

Detection of changes in remotely sensed geographic images is required for a variety of applications including natural disasters. Change detection is an important process utilized for updating the geographic information system (GIS) data, monitoring natural resources and urban developments. It provides quantitative analysis of the spatial distribution of the area of interest. Different types of change detection techniques include: Multi-date visual composite (multitemporal composite), image differencing, post-classification, etc. In this work, we propose a change detection algorithm based on multiresolution analysis and motion estimation. We use multispectral satellite imagery and apply the dual tree complex wavelet transform to get images ensembles. We have also compared our proposed algorithm with some of the efficient methods reported in the literature. Experimental results are given using the IRS images of the Bam city before and after the earthquake.