CraterDANet: A Convolutional Neural Network for Small-Scale Crater Detection via Synthetic-to-Real Domain Adaptation

Yang, Huan; Xu, Xinchao; Ma, Youqing; Xu, You‐Lin; Liu, Shaochuang

doi:10.1109/tgrs.2021.3116348

Cited by 11 publications

(10 citation statements)

References 33 publications

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“…However, up to now, studies on CELMS data still mainly relied on manual interpretation. Recently, machine learning models have been gradually introduced into lunar and planetary research [52][53][54]. Further studies will explore the potential of more advanced machine learning models with regard to lunar brightness temperature analysis [55][56][57].…”

Section: Discussionmentioning

confidence: 99%

A Unified Brightness Temperature Features Analysis Framework for Mapping Mare Basalt Units Using Chang’e-2 Lunar Microwave Sounder (CELMS) Data

Yuan

Meng

et al. 2023

Remote Sensing

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The brightness temperature (TB) features extracted from Chang’e-2 Lunar Microwave Sounder (CELMS) data represent the passive microwave thermal emission (MTE) from the lunar regolith at different depths. However, there have been few studies assessing the importance and contribution of each TB feature for mapping mare basalt units. In this study, a unified framework of TB features analysis is proposed through a case study of Mare Fecunditatis, which is a large basalt basin on the eastern nearside of the Moon. Firstly, TB maps are generated from original CELMS data. Next, all TB features are evaluated systematically using a range of analytical approaches. The Pearson coefficient is used to compute the correlation of features and basalt classes. Two distance metrics, normalized distance and J-S divergence, are selected to measure the discrimination of basalt units by each TB feature. Their contributions to basalt classification are quantitatively evaluated by the ReliefF method and out-of-bag (OOB) importance index. Then, principal component analysis (PCA) is applied to reduce the dimension of TB features and analyze the feature space. Finally, a new geological map of Mare Fecunditatis is generated using CELMS data based on a random forest (RF) classifier. The results will be of great significance in utilizing CELMS data more widely as an additional tool to study the geological structure of the lunar basalt basin.

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Section: Discussionmentioning

confidence: 99%

A Unified Brightness Temperature Features Analysis Framework for Mapping Mare Basalt Units Using Chang’e-2 Lunar Microwave Sounder (CELMS) Data

Yuan

Meng

et al. 2023

Remote Sensing

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“…Over the past several years, object detection based on deep learning [16][17][18][19] has made great progress. Crater detection is also gradually gaining attention [5][6][7][8][9]. However, there are still several issues that have not yet been fully resolved, including the extreme imbalance of crater sizes, domain adaptation, and the absence of sufficiently labelled datasets.…”

Section: Related Workmentioning

confidence: 99%

“…Training a crater detection model that can adapt to the terrain of unknown exoplanets is challenging. Recent works [5][6][7][8][9] have attempted to address this issue. For example, the studies in [5,7,9] employ attention mechanisms or use dilated convolution to learn better feature representations.…”

Section: Introductionmentioning

confidence: 99%

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Two-Stage Adaptive Network for Semi-Supervised Cross-Domain Crater Detection under Varying Scenario Distributions

Liu,

Song,

Xian

et al. 2024

Remote Sensing

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Crater detection can provide valuable information for humans to explore the topography and understand the history of extraterrestrial planets. Due to the significantly varying scenario distributions, existing detection models trained on known labelled crater datasets are hardly effective when applied to new unlabelled planets. To address this issue, we propose a two-stage adaptive network (TAN) for semi-supervised cross-domain crater detection. Our network is built on the YOLOv5 detector, where a series of strategies are employed to enhance its cross-domain generalisation ability. In the first stage, we propose an attention-based scale-adaptive fusion (ASAF) strategy to handle objects with significant scale variances. Furthermore, we propose a smoothing hard example mining (SHEM) loss function to address the issue of overfitting on hard examples. In the second stage, we propose a sort-based pseudo-labelling fine-tuning (SPF) strategy for semi-supervised learning to mitigate the distributional differences between source and target domains. For both stages, we employ weak or strong image augmentation to suit different cross-domain tasks. Experimental results on benchmark datasets demonstrate that the proposed network can enhance domain adaptation ability for crater detection under varying scenario distributions.

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“…Ghifary et al [45] deployed the Maximum Mean Discrepancy (MMD) metric in the supervised domain adaptation to diminish the mismatch in the features subspace between the cross-domains. Since the Gradient Reversal Layer (GRL) [25] has been proposed, the adversarialbased methods [20][21][22][23][49][50][51][52] have become increasingly popular. Zhu et al [20] designed a semi-supervised centerbased discriminative adversarial learning (SCDAL) framework for cross-domain classification, which is based on filtering out easy triplets, proposed hard triplet loss, and the adversarial learning with center loss.…”

Section: B Domain Adaptationmentioning

confidence: 99%

RFA-Net: Reconstructed Feature Alignment Network for Domain Adaptation Object Detection in Remote Sensing Imagery

Zhu

Sun

Diao

et al. 2022

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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CraterDANet: A Convolutional Neural Network for Small-Scale Crater Detection via Synthetic-to-Real Domain Adaptation

Cited by 11 publications

References 33 publications

A Unified Brightness Temperature Features Analysis Framework for Mapping Mare Basalt Units Using Chang’e-2 Lunar Microwave Sounder (CELMS) Data

A Unified Brightness Temperature Features Analysis Framework for Mapping Mare Basalt Units Using Chang’e-2 Lunar Microwave Sounder (CELMS) Data

Two-Stage Adaptive Network for Semi-Supervised Cross-Domain Crater Detection under Varying Scenario Distributions

RFA-Net: Reconstructed Feature Alignment Network for Domain Adaptation Object Detection in Remote Sensing Imagery

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