Change Detection is one of the most popular research fields in Remote Sensing Image Processing. It has broad applications in land use planning, vegetation overlay investigate, damage prevention and map updating. In this dissertation, two specific methods are proposed, and we build up a change detection system for urban changes. The main contributions are as follows: (1)Based on a brief review of the existing change detection methods, their advantages and disadvantages as well as application situation are compared, which will help us to extract appropriate features and put up new methods. (2)We use the texture features extracted from SAR images. To select the appropriate features, the Adaboost algorithm is adopted. Based on the changed and unchanged pixels in the training set images, we present a change detection method that is quite robust to the speckle noise that is common in SAR images. (3)Based on remote sensing images of the city zone, we present a change detection method, which uses edge line and color information as the features in urban area. This approach uses the edge and color information to identify changes between two remote sensing images. Based on this method, we build up a real change detection system to detect changed regions in remote sensing images, which can find relatively obvious changed regions in city automaticlly.
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