Terrain Mapping Camera-2 onboard Chandrayaan-2 Orbiter

Chowdhury, A. R.; Patel, Vishnukumar D.; Joshi, Sanjay S.; Arya, A. S.; Kumar, Ankush; Paul, Sukamal Kr.; Shah, Dhrupesh; Soni, Pradeep; Karelia, J. C.; Sampat, Minal; Sharma, Shashwat; Somani, Sandip; Bhagat, H. V.; Sharma, J. M.; Amitabh,; Kalyanasundaram, Suresh; Rajasekhar, R. P.; Bokarwadia, B. B.; Kumar, M. Vijaya; Ghonia, D. N.

doi:10.18520/cs/v118/i4/566-572

Cited by 12 publications

(2 citation statements)

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“…2023, 15, 5691 5 of 23 TMC-2 of Chandrayaan-2 can provide high-resolution images of the Moon's polar regions. TMC-2 is configured to provide panchromatic images, and stereo triplets at 5 m/pixel spatial resolution from a 100 km circular orbit around the Moon for preparing detailed DEM of the complete lunar surface [37,38]. To supplement the LROC NAC images, 5 TMC-2 images were selected and downloaded from the Indian Space Science Data Center website (h ps://pradan.issdc.gov.in/ch2/protected/payload.xhtml, accessed on 11 September 2023).…”

Section: Methodsmentioning

confidence: 99%

Analysis of Illumination Conditions in the Lunar South Polar Region Using Multi-Temporal High-Resolution Orbital Images

Zhang,

Liu,

et al. 2023

Remote Sensing

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The illumination conditions of the lunar south pole region are complex due to the rugged terrain and very low solar elevation angles, posing significant challenges to the safety of lunar landing and rover explorations. High-spatial and temporal-resolution analyses of the illumination conditions in the south pole region are essential to support mission planning and surface operations. This paper proposes a method for illumination condition analysis in the lunar pole region using multi-temporal high-resolution orbital images with a pre-selected landing area of Chang’E-7 as the study area. Firstly, a database of historical multi-temporal high-resolution (0.69–1.97 m/pixel) orbital images, with associated image acquisition time, solar elevation angle, and azimuth angle, is established after preprocessing and registration. Secondly, images with the nearest solar elevation and azimuth at the planned time for mission operations are retrieved from the database for subsequent illumination condition analysis and exploration support. The differences in the actual solar positions at the mission moments from that of the nearest sun position image are calculated and their impact on illumination conditions is evaluated. Experimental results of the study area demonstrate that the constructed image database and the proposed illumination analysis method using multi-temporal images, with the assistance of DEM in a small number of cases, can effectively support the mission planning and operations for the Chang’E-7 mission in the near future.

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Section: Methodsmentioning

confidence: 99%

Analysis of Illumination Conditions in the Lunar South Polar Region Using Multi-Temporal High-Resolution Orbital Images

Zhang,

Liu,

et al. 2023

Remote Sensing

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“…Having access to high-quality labeled datasets is essential for the effectiveness of deep learning-based crater detection. Here in this article we have used the DEM images of Terrain Mapping Camera-2 Chowdhury et al (2020). Terrain Mapping Camera or TMC 2 is to map the lunar surface in three dimensions.…”

Section: Data Collectionmentioning

confidence: 99%

Lunar Crater Detection Using YOLOv8 Deep Learning

Bandyopadhyay

2024

Preprint

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In lunar exploration missions, the detection of lunar craters is essential for scientific inquiry, navigation, and terrain analysis. Conventional approaches for identifying craters depend on labor- and time-intensive manual inspection or semi-automated procedures. An effective and precise way to automate this procedure is through the use of deep learning algorithms. In this brief message, we introduce our implementation of the cutting-edge object detection method, YOLOv8, for the purpose of detecting lunar craters. The YOLOv8 architecture, which is well-known for its quickness and precision in object identification tasks, was employed. YOLO (You Only Look Once) predicts bounding boxes and class probabilities for several items in an image at once using a single neural network. We used a dataset of high-resolution lunar surface photos with crater annotations to train the YOLOv8 model.

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Chandrayaan-2 Mission

Chauhan

Chauhan²

2023

Encyclopedia of Lunar Science

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Terrain Mapping Camera-2 onboard Chandrayaan-2 Orbiter

Cited by 12 publications

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Analysis of Illumination Conditions in the Lunar South Polar Region Using Multi-Temporal High-Resolution Orbital Images

Analysis of Illumination Conditions in the Lunar South Polar Region Using Multi-Temporal High-Resolution Orbital Images

Lunar Crater Detection Using YOLOv8 Deep Learning

Chandrayaan-2 Mission

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