Enhancing the Quality of 3D Lunar Maps Using JAXA's Kaguya Imagery
Fuente:
arXiv
Enregistré dans:
| Auteurs principaux: | , , , , , , , , |
|---|---|
| Format: | Preprint |
| Publié: |
2025
|
| Sujets: | |
| Accès en ligne: | |
| Tags: |
Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
|
| _version_ | 1866909843541983232 |
|---|---|
| author | Iwashita, Yumi Moe, Haakon Cheng, Yang Ansar, Adnan Georgakis, Georgios Stoica, Adrian Nakashima, Kazuto Kurazume, Ryo Torresen, Jim |
| author_facet | Iwashita, Yumi Moe, Haakon Cheng, Yang Ansar, Adnan Georgakis, Georgios Stoica, Adrian Nakashima, Kazuto Kurazume, Ryo Torresen, Jim |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_11817 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Enhancing the Quality of 3D Lunar Maps Using JAXA's Kaguya Imagery Iwashita, Yumi Moe, Haakon Cheng, Yang Ansar, Adnan Georgakis, Georgios Stoica, Adrian Nakashima, Kazuto Kurazume, Ryo Torresen, Jim Computer Vision and Pattern Recognition Machine Learning 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. |
| title | Enhancing the Quality of 3D Lunar Maps Using JAXA's Kaguya Imagery |
| topic | Computer Vision and Pattern Recognition Machine Learning |
| url | https://arxiv.org/abs/2510.11817 |