In recent years, high resolution remote sensing imagery has been widely utilized in change detection, especially in urban residential areas. The quality of image features has great influence on detection results, and each kind of feature has its own unique advantage in the process of change detection. It is hard to obtain satisfying results if we only use one kind of feature. This paper extracts image gray scale feature, edge feature and geometric feature for change detection. Firstly, according to the image gray scale feature of the house, K-means classification and binarization are utilized to extract house areas roughly. After that, Hough transformation is used to detect the straight lines and obtain the difference map. To improve the accuracy, minor changes, such as spot-like and linear changes are eliminated by using the geometric shape and size of the objects. At last, we conducted accuracy assessment for the final result. Experiments show that geometric feature plays an important role in extracting target objects and improving the final result, and has obvious advantages and feasibility.