Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLI-B1, 299-304, 2016
http://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLI-B1/299/2016/
doi:10.5194/isprs-archives-XLI-B1-299-2016
 
03 Jun 2016
IMPROVING SEMI-GLOBAL MATCHING: COST AGGREGATION AND CONFIDENCE MEASURE
Pablo d’Angelo German Aerospace Center (DLR), Remote Sensing Technology Institute D-82234 Wessling, Germany
Keywords: Dense Matching, Digital Elevation Model, Stereo, Benchmark, Accuracy Abstract. 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.
Conference paper (PDF, 3052 KB)


Citation: d’Angelo, P.: IMPROVING SEMI-GLOBAL MATCHING: COST AGGREGATION AND CONFIDENCE MEASURE, Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLI-B1, 299-304, doi:10.5194/isprs-archives-XLI-B1-299-2016, 2016.

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