Robust stereo analysis

A new multistage adaptive robust (MAR) algorithm combined with a multiresolution coarse to fine matching model is developed for robust stereo analysis. Stereo analysis of remotely sensed images is useful for a variety of applications including cloud height measurement and digital terrain models. Time sequential stereoscopic observations of clouds from meteorological satellites provide a basic analysis tool for a broad spectrum of applications including numerical weather prediction, cloud modeling, and global climate understanding. A two step robust stereo analysis algorithm is developed and applied to the satellite based cloud height estimation problem. The first step involves estimating initial disparities (depth map or height field) provided by a hierarchical coarse to fine stereo analysis algorithm that uses regularization and image warping. For the second step the matching model using to derive robust estimators is described, followed by a description of the multi stage robust estimation process for handling outliers and irregularities in the initial stereo disparity field. The incorporation of a multistage robust statistical process using a general matching model leads to improved performance