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ABSTRACT:Hexagon satellite data acquired as a part of USA Corona program has been declassified and is accessible to general public. This image data was acquired in high resolution much before the launch of civilian satellites. However the non availability of interior and exterior orientation parameters is the main bottle neck in photogrammetric processing of this data. In the present study, an attempt was made to orient and adjust Hexagon stereo pair through Rigorous Sensor Model (RSM) and Rational Function Models (RFM). The study area is part of Western Ghats in India. For rigorous sensor modelling an arbitrary camera file is generated based on the information available in the literature and few assumptions. A terrain dependent RFM was generated for the stereo data using Cartosat-1 reference data. The model accuracy achieved for both RSM and RFM was better than one pixel. DEM and orthoimage were generated with a spacing of 50 m and Ground Sampling Distance (GSD) of 6 m to carry out the change detection with a special emphasis on water bodies with reference to recent Cartosat-1 data. About 72 new water bodies covering an area of 2300 hectares (23 sq. km) were identified in Cartosat-1 orthoimage that were not present in Hexagon data. The image data from various Corona programs like Hexagon provide a rich source of information for temporal studies. However photogrammetric processing of the data is a bit tedious due to lack of information about internal sensor geometry.

Sensor models are required to establish the relationship between 3D object space and 2D image space. Traditionally this is done using the physical sensor model where the complete parameters of physical imaging system are known. The replacement sensor models are required to establish this relation without the knowledge of the physical sensor model. The rational function model (RFM) is one of the replacement model used in remote sensing with 78 rational polynomial coefficients (RPCs). RFM is a complete mathematical model, which approximately describes the physical imaging process in photogrammetry and remote sensing. In the absence of interior and exterior orientation such as camera model, position and orientation information of specific sensor, large number of ground control points (GCPs) are needed to solve all the unknown coefficients of the RFM and to achieve higher accuracies in the photogrammetric processing. The rational function model(RFM) can be used either as a replacement for physical sensor model ( terrain dependent) or to express the physical model in the form of RPCs ( terrain independent) for further processing.In this paper the implementation aspects of terrain dependent RFM model for Cartosat-1 data for the Chitrapur, Simla, Himachal Pradesh state, India and the accuracies achieved and the stability of the model are discussed. The direct least square solutions to the RFM are implemented using row reduction. The validation of RFM is done at check points and achieved planimetric accuracy 1.5m,3.38m with respect to CE90 in X and Y directions respectively.