{"id":{"repo_id":"vt","oai_identifier":"oai:vtechworks.lib.vt.edu:10919/53080"},"canonical_url":"https://search.dev.ndltd.org/etd/vt/oai:vtechworks.lib.vt.edu:10919/53080","repository":{"repo_id":"vt","name":"Virginia Tech","base_url":"https://vtechworks.lib.vt.edu/oai/request"},"display":{"title":"Road detection on radar imagery","abstract":"A facet based road network detection procedure is described for radar imagery. The procedure includes a line detection part and a road detection and connection part. The line detection part analytically detects linear features using a facet Valley finding technique. Valleys are defined as zero crossings of the first directional derivatives of a bicubic facet model taken in a direction extremizing the second directional derivative. The road detection and connection part statistically screens the linear features on a component by component basis, and then optimally connects the screened linear features using a dynamic programming algorithm. This thesis also includes as a preprocessing technique for noisy images, an adaptive noise removal algorithm, and suggests a practical method of estimating a local noise variance.","abstract_html":"A facet based road network detection procedure is described for radar imagery. The procedure includes a line detection part and a road detection and connection part. The line detection part analytically detects linear features using a facet Valley finding technique. Valleys are defined as zero crossings of the first directional derivatives of a bicubic facet model taken in a direction extremizing the second directional derivative. The road detection and connection part statistically screens the linear features on a component by component basis, and then optimally connects the screened linear features using a dynamic programming algorithm. 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