IMPROVING SEMI-GLOBAL MATCHING: COST AGGREGATION AND CONFIDENCE MEASURE

Digital elevation models are one of the basic products that can be generated from remotely sensed imagery. The Semi Global Matching (SGM) algorithm is a robust and practical algorithm for dense image matching. The connection between SGM and Belief Propagation was recently developed, and based on that improvements such as correction of over-counting the data term, and a new confidence measure have been proposed. Later the MGM algorithm has been proposed, it aims at improving the regularization step of SGM, but has only been evaluated on the Middlebury stereo benchmark so far. This paper evaluates these proposed improvements on the ISPRS satellite stereo benchmark, using a Pleiades Triplet and a Cartosat-1 Stereo pair. The over-counting correction slightly improves matching density, at the expense of adding a few outliers. The MGM cost aggregation shows leads to a slight increase of accuracy