期刊名称:ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
印刷版ISSN:2194-9042
电子版ISSN:2194-9050
出版年度:2006
卷号:XXXVI Part 4
出版社:Copernicus Publications
摘要:Spatial accounting and monitoring of land use and land cover (LULC) systems have become essential for the sustainable development of any country. In order to monitor these LULC systems a multi-temporal study is required at regular intervals. Indian Remote Sensing Satellite RESOURCESAT-1 (IRS-P6) has a unique sensor called AWiFS having medium resolution and wide ground swath and is designed to have around 80% overlap across adjacent paths. Hence this is one of the best-suited sensors for medium scale LULC temporal studies. Standard corrections can not account for the errors caused due to its swath and terrain relief distortions. For getting the accurate registration with respect to temporal layers or maps an ortho-rectification procedure can be adopted. The Rigorous Sensor Model or the Rational Functional Model (RFM) can be used to ortho rectify such images. In relatively flat areas ortho-rectification is not necessary, but in mountainous terrains ortho rectification is essential. Since the AWiFS camera has differing focal lengths between bands, performing ortho rectification using Rigorous Sensor model on these scenes demands more computations. Alternately a projective transformation approach based on RFM can correct the effects of the temporal data acquired through overlapping paths. The projective transformation simulates the sensor orientation while considering the terrain relief without looking for sensor information. Since this approach considers DEM, effects of terrain undulations can be rectified with high precision. This is a better tool for creating the spatio temporal database for carrying out the LULC natural resource studies. The accuracy of DEM and its corresponding ortho rectified data sets play a major role in the accuracy of the temporal registration. This methodology is presented in this paper, along with few case studies by considering the temporal AWiFS data sets over Indian regions