Enhanced disparity maps from multi-view satellite images

Abstract

Multi-view satellite images can form multiple stereo image pairs (MSIPs). Each pair allows generating two types of surface information: surface disparity maps (SDMs) and digital elevation models (DEMs). Both types are important for 3D assisted information extraction applications. Since multi-view images can be used to generate more accurate stereo-based DEMs than using one stereo-pair, it is more computationally cost-effective to generate enhanced SDMs from MSIPs. However, the disparity values from different stereo-pairs are unequal. Hence, redundant data cannot be generated to enhance the SDMs. Therefore, our research focuses on the generation of inter-proportional disparity maps from multi-view very high resolution satellite images. The method uses the sensor model information of the stereo images to construct multiple epipolar images in the object-space. After implementation, the disparity values of the constructed images were found to be proportional. This property allows the generation of SDMs that may replace the DEMs to some extent.