Remote-sensing image recognition based on wavelet transform and Hausdorff distance

Approaches of image enhancement, edge extraction, and line-based matching for remote sensing images are proposed. In the image enhancement, we propose a wave let shrinkage technique to blur the urban regions (i.e. the small-scale texture regions) while preserving the sharpness of large-scale edges (such as highways and rivers) based on a Teager energy criterion. The edge extraction contains wavelet-based edge detection and tracking. The contextual-filter edge detector generates multiresolution gradient images, and then the multiscale edge tracker refines the results as well as reduces the influence of fragment edges and the broken edges. Each extracted edge segment is represented by the coordinates of its mid-point, the logarithm of its length, and its orientation. Then, the matching algorithm based on the Hausdorff distance is applied twice on the two sets of feature vectors for invariant matching.