Wide baseline stereo matching is one of the essential problems in computer vision and finds many applications such as 3D reconstruction, object recognition and remote sensing image processing. Although a variety of works have been done in the field, wide baseline stereo matching is still a challenging problem, and many difficult problems persist. This thesis is focused on some practical issues on wide baseline stereo matching, and the main work is summarized as follows: A MSER (Maximally Stable Extremal Regions) based wide baseline stereo matching algorithm is implemented. It is composed of the following three steps: (1) Feature detection. MSER is used as the feature. (2) Feature description. Each MSER is described by SIFT (Scale Invariant Feature Transform) descriptor. (3) MSER matching. Corresponding MSERs are obtained via SIFT descriptor. The experimental results with typical images show that algorithm can satisfactorily find correspondences across two images and perform well under viewpoint change, illumination variation and large scale change. A method based on image feature matching is proposed for detecting changed buildings in high spatial resolution remote sensing images. The method examines the possible correspondences of the MSER from the current image to the reference image, and by which to infer whether a specified building has changed. If unchanged, computes its location in the reference image. The use of the feature matching for building change detection can well overcome the problems caused by significant changes such as viewpoint and illumination changes, as well as occlusions. The preliminary experimental results with Ikonos satellite images，Quickbird satellite images as well as aerial images show that the proposed method can satisfactorily detect building changes. A novel approach for detecting urban changes from a pair of high spatial resolution remote sensing images is explored. The basic idea of the approach is that: MSERs can be used to represent urban change contents. MSERs changes can be considered as the urban change under this basic principle, and the change detection is converted to a MSER matching problem. Varying experiments with satellite images under various conditions such as geometric distortion, illumination variation and resolution difference show that our explored change detection approach performs well, and could be used as a feasible way to solve the problems in remote sensing applications.
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