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PS 26166SOFTWARESpace TechnologyHeavy R&D

Multi-modal, Sun angle and scale invariant image correspondence using Chandrayaan-2 optical images (OHRC, TMC and IIRS)

Indian Space Research Organisation(ISRO)Department of Space / Indian Space Research Organisation
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30-Second Plain English Summary

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.

5-Dimension Strategic ScorecardOverall Score: 3.9 / 5.0
Innovation
4 / 5
36h Feasibility
4.5 / 5
Uniqueness
3.5 / 5
Jury Appeal
3.6 / 5
Tech Depth
3.9 / 5
Recommended System Architecture Pipeline
Multi-Modal Satellite Images (Optical/SAR/Thermal) -> LoFTR Transformer Dense Matcher -> RANSAC Outlier Rejection & Homography Solver -> Sub-Pixel GeoWarp Engine -> ISRO Geo-Registration Workbench
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Official Government Problem Description
Background Image Registration is the process of aligning two or more images of the same scene taken at different times, from different viewpoints, or by different sensors into a common coordinate system. It has two main components: • Source Image (Moving): The image that is to be geometrically transformed to align with the reference image. • Reference Image (Fixed): The target image about which source image is to be geometrically transformed. Description The process of lunar images registration involves finding match points between source and reference image and then aligning the source image with the reference image. The key challenges involved in this process are as follows: • Illumination variation: Illumination variation refers to changes in sun azimuth and elevation effect on the surface lighting conditions that affect the appearance of the lunar surface features which is hard to correlate. • Viewpoint variation: It refers to geometric distortions caused by different camera positions/orientations capturing the same scene. Objects appear shifted, scaled, rotated, or perspective-distorted depending on observing angle. • Scale Variation: Lunar imaging missions operate at vastly different altitudes and at different spatial resolutions. This creates scale ratios. Expected Solution Generic software solution for finding correspondence between Chandrayaan-2 acquired optical images and Lunar reference images with a sub-pixel accuracy of source image maintaining uniform distribution across the images. • Software and registered product with corresponding match points. • Evaluation metric (eg. RMSE, inlier match count, inlier ratio, etc.)
AI & PPT Citation Format

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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