Deriving terrain and textural information from stereo RADARSAT data for mountainous land cover mapping
Abstract:A new procedure is proposed for land cover classification in a mountainous area using stereo RADARSAT‐1 data. The method integrates a few types of information that can be extracted from the same stereo RADARSAT images: (1) the Digital Elevation Model (DEM) generated from the stereo RADARSAT images; (2) terrain information (elevation, slope and aspect) extracted from the derived DEM; and (3) textural information derived from the same RADARSAT images. An Artificial Neural Network (ANN) classifier is applied for the land cover classification. Performance of the proposed method is evaluated using a mountainous study area in Southern Argentina, where there is a lack of up‐to‐date information for environmental monitoring. The results show that the integration of textural and terrain information can greatly improve the accuracy of the classification using the ANN classifier. It demonstrates that stereo RADARSAT images provide valuable data sources for land cover mapping, especially in mountainous areas where cloud cover is a problem for optical data collection and topographical data are not always available.
Document Type: Research Article
Affiliations: 1: Mid‐America Remote Sensing Center, Murray State University, Murray, KY 42071‐3309 2: Department of Geography, the University of Western Ontario, London, ON N6A 5C2, Canada 3: Department of Geosciences, Murray State University, Murray, KY 42071‐3311
Publication date: November 20, 2005