Multi-modal, Sun angle and scale invariant image correspondence using Chandrayaan-2 optical images (OHRC, TMC and IIRS)
Automated co-registration of satellite images captured at different seasons, sun illumination angles, sensor modalities (Optical vs SAR Radar), and spatial resolutions (Cartosat vs Sentinel) suffers from high geometric distortion, parallax errors, and feature matching failures over rugged Indian terrain. Build an AI-Driven Multi-Modal, Sun-Angle, and Scale-Invariant Image Correspondence and Geo-Registration Engine for ISRO that uses deep local invariant feature descriptors (SuperPoint / LoFTR) to achieve sub-pixel spatial alignment across heterogeneous satellite acquisitions.
Smart India Hackathon 2026 Problem Statement PS-26166: "Multi-modal, Sun angle and scale invariant image correspondence using Chandrayaan-2 optical images (OHRC, TMC and IIRS)", Ministry: Indian Space Research Organisation(ISRO). Strategy & Architecture via SIH ONE (https://sihone.pages.dev/ps/26166)
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