An Efficient Approach for Geo-location Accuracy Improvement of Legacy IRS-1C/1D Data Products

IRS-1C/1D remote sensing datasets from Indian Space Research organization (ISRO) is of significant importance till now for change detection analysis in the field of agriculture growth, wetland mapping, urban sprawl monitoring, snow/glacier studies and ingestion of historical digital sensor data in data cube framework for doing data analytics and generation of value added information. Currently, IRS-1C/1D georeferenced data products have system level location accuracy go up to 1 Km. In addition, the location error of IRS-1C/1D datasets are increased to many kilometers especially in hilly terrain scenes. Such higher location accuracy error makes legacy IRS-1C/1D data not directly usable for any kind of space borne applications. In this paper, an efficient approach is presented to improve the geo-location accuracy of IRS-1C/1D data by incorporating inflight geometric calibration exercises. The rigorous sensor model is fed with primary image, ancillary orbit and attitude data, digital elevation model (DEM) and a reference image to produce ortho-rectified product. The major challenge is to do automatic feature matching of IRS-1C/1D data with current RS-2A reference image database to achieve sub-pixel level geometric error. As datasets are more than two decades older, the number of outliers in matched feature points are more. Also geo-location error of 1C/1D is varying a lot which makes it difficult to set the search window radius for feature matching. To handle this scenario, mode seeking based pruning mechanism is added with robust SIFT feature matching technique which is independent of search window size. Additionally, SIFT parameters are also fine-tuned to get more stable feature points. The approach developed is tested in many multi temporal IRS-1C/1D LISS-3 datasets. The geo-location error of the products generated is at sub-pixel level accuracy with respect to the reference. The IRS-1C/1D data products generated with this new updated chain can be used directly as analysis ready datasets.