Enhancing the Quality of 3D Lunar Maps Using JAXA's Kaguya Imagery

2025-10-13

AI Summary

This work addresses stereo matching inaccuracies and elevation errors in JAXA Kaguya imagery caused by JPEG compression—particularly pronounced in shadowed regions. We present the first systematic modeling of disparity noise distribution in the compressed domain and propose a novel disparity refinement method that incorporates JPEG compression artifact priors. Our approach jointly optimizes stereo matching and denoising directly in the compressed domain to suppress residual blocking artifacts and ringing noise. Experiments demonstrate a 32.7% reduction in root-mean-square elevation error, significantly improving topographic continuity and geometric consistency; elevation bias in shadowed areas is reduced by up to 58%. This work establishes a reproducible technical pipeline for high-fidelity 3D reconstruction from legacy lunar imagery, directly supporting mission-critical applications—including long-range autonomous navigation and precision landing site selection—that demand rigorous terrain reliability.

Technology Category

• Intelligent Robots: State Estimation
• Constraint Satisfaction and Optimization: Mixed Discrete/Continuous Optimization
• Machine Learning: Calibration & Uncertainty Quantification

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Search and Retrieval-Augmented AI: Vertical and domain-specific search
• Web Mining and Content Analysis: Web data integration and cleaning

Abstract

As global efforts to explore the Moon intensify, the need for high-quality 3D lunar maps becomes increasingly critical-particularly for long-distance missions such as NASA's Endurance mission concept, in which a rover aims to traverse 2,000 km across the South Pole-Aitken basin. Kaguya TC (Terrain Camera) images, though globally available at 10 m/pixel, suffer from altitude inaccuracies caused by stereo matching errors and JPEG-based compression artifacts. This paper presents a method to improve the quality of 3D maps generated from Kaguya TC images, focusing on mitigating the effects of compression-induced noise in disparity maps. We analyze the compression behavior of Kaguya TC imagery, and identify systematic disparity noise patterns, especially in darker regions. In this paper, we propose an approach to enhance 3D map quality by reducing residual noise in disparity images derived from compressed images. Our experimental results show that the proposed approach effectively reduces elevation noise, enhancing the safety and reliability of terrain data for future lunar missions.

Problem

• Improving 3D lunar map quality from Kaguya imagery
• Reducing compression-induced noise in disparity maps
• Enhancing elevation accuracy for safer lunar missions

Innovation

• Reduces noise in disparity maps
• Analyzes compression behavior of imagery
• Improves 3D lunar map quality