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This paper presents a computationally efficient method for stereo image matching of remote sensing images. Traditional correlation methods often fail to give correct matching when the slope of the terrain is very high. A novel technique called aspect-based estimation is proposed here to find the best match points and to estimate the slope of the terrain. Stereo image matching is implemented in a hierarchical manner using wavelets to reduce the computational cost. A robust technique for identifying outliers based on local statistics is also presented here. Experimental results with CARTOSAT-1 images indicate that the aspect-based correlation and blunder detection works very efficiently and effectively in stereo image matching of remote sensing images. It is observed that this algorithm is able to give a high density of reliable match points of the order of 1 for every 5 times 5 pixels. The technique is demonstrated here for CARTOSAT-1 images. However, the proposed algorithm can be applied to a remote sensing stereo pair of any spaceborne imagery.