Small object recognition techniques based on structured template matching for high-resolution satellite images

We are developing infrastructure tools of wide-area monitoring used for such as disaster damaged areas or traffic conditions, using Earth observation satellite images. Especially, we are focusing on developing a small object recognition tool for satellite images, which enables extract automobile patterns in high-resolution satellite images such as QuickBird panchromatic images, for example. Although, resolution of optical sensors installed in the current earth observation satellites has been highly advanced, their pixel resolution is not enough for identifying each small object such as an automobile by the currently available pattern matching techniques. Whereas, the pattern matching calculation load of high-resolution images becomes bigger, it will take tremendous time for searching whole objects included in a slice of satellite images. In order to overcome these problems, we propose a structured template matching technique for recognizing small objects in satellite images, which consists of a micro-template matching, clustered micro-template matching and macro-template matching. In this paper, we describe an abstract of our proposed method and present its experimental results.