Delineation of Impervious Surface from Multispectral Imagery and Lidar Incorporating Knowledge Based Expert System Rules
An attempt to delineate impervious surfaces in the City of Scottsbluff, Nebraska, was made using multispectral high spatial resolution imagery and lidar data. An ISODATA classification was performed and results aggregated into two parent classes, impervious and pervious. The ISODATA classification yielded an overall accuracy of 91.0 percent with a Kappa of 82.0 percent. A Knowledge Based Expert System (KBES) set of rules was designed incorporating the imagery classification with lidar data to derive two models, Cover Height and Cover Slope, to provide critical information not available from multispectral imagery. The rules were applied to the initial ISODATA classification to improve the classification accuracy to an overall accuracy of 94.0 percent with a Kappa of 87.9 percent. In this study, it was shown that lidar holds promise for improving the accuracy of impervious surface measurement, as well as the potential identification and measurement of other significant planimetric features such as buildings and trees.
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Document Type: Research Article
Publication date: January 1, 2011
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- The official journal of the American Society for Photogrammetry and Remote Sensing - the Imaging and Geospatial Information Society (ASPRS). This highly respected publication covers all facets of photogrammetry and remote sensing methods and technologies.
Founded in 1934, the American Society for Photogrammetry and Remote Sensing (ASPRS) is a scientific association serving over 7,000 professional members around the world. Our mission is to advance knowledge and improve understanding of mapping sciences to promote the responsible applications of photogrammetry, remote sensing, geographic information systems (GIS), and supporting technologies.
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