Automatic change detection of artificial objects in multitemporal high spatial resolution remotely sensed imagery

Change detection is one of the most important processes in various monitoring applications in multi-temporal remote sensed imagery. We focus on changes of artificial objects, including whether new artifical objects occur or existing artificial objects have changes. This paper proposes a new method to discriminate such changes in multi-temporal images using optimal quantization and block-based linear regression techniques. In the method, multi-temporal images are represented by less quantization level through optimal quantization method respectively; consequently, a block-based linear regression model is used to establish the relationship between multi-temporal images getting the changes effectively and automatically. The method is successfully applied to detect the changes of artificial objects without being affected by various vegetation covers for panchromatic high spatial resolution images such as IRS satellite images.