Disparity map generation from satellite stereo pair images and estimating height information using machine learning algorithms

Abstract

Stereo vision is generally used in depth information generation. The classical approach involves imaging geometry for generating digital elevation model (DEM) or digital surface model(DSM) from a pair of satellite stereo images. In the present study a software tool has been developed to extract relative depth information of earth features, viz. buildings, in terms of disparity map, from a pair of images with different viewing angles.   Any images of same area acquired in different perspective can be used to generate disparity map using this tool without the knowledge of their imaging parameter. The disparity values are subsequently compared with the LiDAR generated DSM values and a strong correlation has been found between them.Using machine learning algorithm viz. ANN and SVR, heights of unknown ground objects have been predicted from disparity values with an appreciable accuracy.