DEM GENERATION FROM SATELLITE DATA USING RPC

Abstract

A Digital Elevation Model (DEM) is a regularly spaced raster grid of elevation values representing the surface terrain. A DEM has wide application in surveying, mapping, urban planning, and engineering. In remote sensing, DEMs are extensively used in mapping, orthorectification, GIS, and land classification. Conventional methods of DEM acquisition includes field surveying and digitizing contours maps. Apart from conventional methods of DEM acquisition, producing DEM from satellite data has been a vibrant research and development topic for the last thirty years. One way to acquire DEM data is to generate it from stereo imagery using photogrammetry. The fundamental task of photogrammetry is to rigorously establish the geometric relationship between the sensor image spaces and ground object space, which can be achieved by physical sensor model. Once this relationship is correctly recovered, one can derive information about the object strictly from its imagery. The primary drawbacks of the physical sensor model are that its application requires explicit understanding of each of the physical parameters and a high level of expertise, and also the intentional concealment of the physical sensor model by the data provider. A generalized sensor model viz. Rational Polynomial Coefficients (RPC) Model have recently drawn considerable interest in the remote sensing community, especially in light of the trend that some commercial high resolution satellite imagery data are supplied with RPC without disclosing the physical sensor model. RPCs with stereo pairs, provides full photogrammetric processing including 3-D reconstruction, DEM generation, orthorectification, block adjustment and feature extraction. This thesis report presents a complete methodology for DEM generation from stereo satellite images using Rational Polynomial Coefficients of the imaging geometry. Different commercial software's accuracy and performance are checked for DEM generation from stereo images using RPC approach and the results are evaluated through sample captured by IKONOS. iii