Geocoding RISAT-1 MRS images using bias-compensated RPC models

Abstract

The purpose of this paper is two-fold. First, the use of the rational polynomial coefficients (RPCs) model is studied for geocoding of Medium Resolution Scan (MRS) ground range (GR) images from the RISAT-1 SAR mission. As the GR images are obtained after many preprocessing image corrections for topographic effect, range cell migration, etc., the number of ground control points (GCPs) required for orthorectification to meet desired geometric quality needs to be established. This assumes importance due to difficulty in visual identification of the GCPs in 18 m-resolution MRS SAR images. Second, three possible methods of bias-compensated RPC models are studied for geocoding. These cases are (A) modified RPC with shift bias, (B) regenerated RPC with shift bias, and (C) regenerated RPC with affine transform model. Experiments are carried out with a set of eight scenes acquired over planar regions especially to avoid the impact of SAR-specific geometric effects such as foreshortening and layover. Geometric accuracy of the orthoimages obtained from these cases is verified at GCPs used for processing as control points and at new GCPs used as check points. It is observed that the modified RPC with the shift bias case required more GCPs to meet the desired geopositioning accuracy. Even though both the regenerated RPC models have shown near similar performance, the regenerated RPC with shift bias compensation is found to reach the required geopositioning accuracy with least number of the GCPs, suggesting it as a strong candidate for realizing operational high precision RISAT-1 geocoded products for multi-temporal data analysis.