Coarse-to-Fine Extraction of Small-Scale Lunar Impact Craters From the CCD Images of the Chang’E Lunar Orbiters

Zhang, Kang; Wang, Xingkun; Hu, Teng; Yang, Juntao

doi:10.1109/tgrs.2018.2852717

Cited by 27 publications

(11 citation statements)

References 40 publications

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“…Average precision (AP) is a comprehensive metric for the performance evaluation of neural network models. The formula for calculating recall rate, precision value, and F 1 score is as follows ( 18)- (20). "Recall" in Formula ( 18) is expressed as recall rate.…”

Section: Model Evaluationmentioning

confidence: 99%

“…Research related to impact craters relies on their identification and characterization. Early identification of impact craters primarily relied on manual labeling [12][13][14], morphological feature extraction algorithms [15][16][17][18], and machine learning-based identification [2,19,20]. With advancements in computer vision and artificial intelligence, identifying impact craters has gradually shifted toward deep learning.…”

Section: Introductionmentioning

confidence: 99%

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Identification of Lunar Craters in the Chang’e-5 Landing Region Based on Kaguya TC Morning Map

Liu,

Lai,

Xie

et al. 2024

Remote Sensing

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Impact craters are extensively researched geological features that contribute to various aspects of lunar science, such as evaluating the model age, regolith thickness, etc. The method for identifying impact craters has gradually transitioned from manual counting to automated identification. Automatic crater detection based on the digital elevation model (DEM) is commonly used to detect larger craters. However, using only DEM has limitations in discerning smaller craters (diameter < ~1 km). This study utilizes an improved Faster R-CNN algorithm and the Kaguya Terrain Camera (TC) morning map to detect small impact craters in the Chang’e-5 (CE-5) landing site. It uses model fusion to improve the precision of small crater identification. The results show a recall rate of 96.33% and a precision value of 90.19% for craters with diameters exceeding 200 m. The model found a total of 187,101 impact craters in the CE-5 region. The spatial distribution density of impact craters with diameters ranging from 100 m to 200 m is approximately 2.5706/km2. For craters with diameters ranging from 200 m to 1 km, the average spatial distribution density is about 0.9016/km2. By the unbiased impact crater density of chronological analysis, the model age of the Im2 and Em4 geological units in the CE-5 region is 3.78 Ga and 2.07 Ga, respectively.

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Section: Model Evaluationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Identification of Lunar Craters in the Chang’e-5 Landing Region Based on Kaguya TC Morning Map

Liu,

Lai,

Xie

et al. 2024

Remote Sensing

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“…Craters are one of the most noticeable geomorphological features on the lunar surface. Most recent studies focus on their morphological characteristics and spatial distributions [1][2][3][4]. The use of multi-source data allowed to scientists reconstruct the geological history of the lunar craters, among the most noticeable geomorphological features on the Moon.…”

Section: Introductionmentioning

confidence: 99%

Study of the Buried Basin C-H, Based on the Multi-Source Remote Sensing Data

Zhang

et al. 2022

Remote Sensing

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We use multi-source remote sensing data to identify the details of a mascon south-east of the lunar Copernicus crater. Studies of the topography, gravity, geochronology and mineral are combined to prove that the mascon is a buried peak-ring basin with diameters of about 130 km and 260 km. The underground structure is covered by 890 m thick mare basalts, as determined by analyzing the spectral features of the impact crater, Copernicus H. The determination of the crater size–frequency distribution (CSFD) suggests that the impact that created the C-H basin occurred earlier than 3.9 Ga. Then, a Hawaiian-style eruption in the late Imbrian period formed the Sinus Aestuum-I dark mantling deposit (DMD). Soon, mare basalts covered the basin several times from 3.8 Ga. Finally, the ejecta from the Copernicus impact event at about 800 Ma, and the weathering processes caused the disappearance of the C-H basin rim from the lunar surface.

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“…Craters are among the most noticeable geomorphological unit on the planetary surface. Most recent studies focus on their morphological characteristics and spatial distributions [1]. However, researches based on multi source data can revise geologic framework more accurately.…”

Section: Introductionmentioning

confidence: 99%

Study of Buried Basin in Copernicus Area Based on Multi Source Remote Sensing Data

Xu¹,

Zhang²,

Teng³

et al. 2022

Preprint

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Masons are often overlooked part of impact basins, but play an important role in revealing the lunar history. Previous study in masons were usually limited to gravity data. Few researches were reported on morphology features and chronology, which hampers the construction of a complete geological interpretation for the evolution of each mascons. We use multi source remote sensing data to identify the details characteristic of mascons. Result of topography, gravity and characteristic are combined to prove that a mason beside Copernicus crater is a buried peak-ring basin which is about 130km and 260km in diameter. The underground structure is of confirmed as 890m thick mare basalts by analyzing the spectral feature of the material in a geological outcrop called Copernicus H. Geology evolution analysis joint crater size-frequency distribution (CSDF) dating demonstrate that the buried basin impact event occurred in 3.6Ga. Then a hawaiian-style eruption in late Imbrian formed Sinus Aestuum Ⅰ Dark Mantling Deposit (DMD). Mare basalts filling in 3.4Ga. After that, ejecta from Copernicus impact event in about 820Ma and weathering processes cause the disappearing from lunar surface of the buried basin.

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Coarse-to-Fine Extraction of Small-Scale Lunar Impact Craters From the CCD Images of the Chang’E Lunar Orbiters

Cited by 27 publications

References 40 publications

Identification of Lunar Craters in the Chang’e-5 Landing Region Based on Kaguya TC Morning Map

Identification of Lunar Craters in the Chang’e-5 Landing Region Based on Kaguya TC Morning Map

Study of the Buried Basin C-H, Based on the Multi-Source Remote Sensing Data

Study of Buried Basin in Copernicus Area Based on Multi Source Remote Sensing Data

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