Transfer learning for real-time crater detection on asteroids using a Fully Convolutional Neural Network

Latorre, Francesco; Spiller, Dario; Sasidharan, Sarathchandrakumar T.; Basheer, S.; Curti, Fabio

doi:10.1016/j.icarus.2023.115434

Cited by 14 publications

(8 citation statements)

References 28 publications

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“…Their trained CNN showed a high precision for crater detection as humangenerated. Inspired by Silburt et al, 2019, Latorre et al, 2023 implemented several transfer learning approaches including finetuning and presented its capability for the autonomous detection of impact craters across the Moon and Ceres, which have different geological features. Thus, semantic segmentation shows potential to extract specific geological features autonomously.…”

Section: Introductionmentioning

confidence: 99%

Extraction of stratigraphic exposures on visible images using a supervised machine learning technique

Noguchi,

Shoji

2023

Front. Earth Sci.

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As volcanic stratigraphy provides important information about volcanic activities, such as the eruption style, duration, magnitude, and their time sequences, its observation and description are fundamental tasks for volcanologists. Since outcrops are often obscured in nature, the first task would be identifying stratigraphic exposures in many cases. This identification/selection process has depended on humans and has difficulties in terms of time and effort consumption and in biases resulting from expertise levels. To address this issue, we present an approach that utilizes supervised machine learning with fine-tuning and forms the backbone to automatically extract the areas of stratigraphic exposures in visible images of volcanic outcrops. This study aimed to develop an automated method for identifying exposed stratigraphy. This method will aid in planning subsequent field investigations and quickly outputting results. We used U-Net and LinkNet, convolutional neural network architectures developed for image segmentation. Our dataset comprised 75 terrestrial outcrop images and their corresponding images with manually masked stratigraphic exposure areas. Aiming to recognize stratigraphic exposures in various situations, the original images include unnecessary objects such as sky and vegetation. Then, we compared 27 models with varying network architectures, hyperparameters, and training techniques. The highest validation accuracy was obtained by the model trained using the U-Net, fine-tuning, and ResNet50 backbone. Some of our trained U-Net and LinkNet models successfully excluded the sky and had difficulties in excluding vegetation, artifacts, and talus. Further surveys of reasonable training settings and network structures for obtaining higher prediction fidelities in lower time and effort costs are necessary. In this study, we demonstrated the usability of image segmentation algorithms in the observation and description of geological outcrops, which are often challenging for non-experts. Such approaches can contribute to passing accumulated knowledge on to future generations. The autonomous detection of stratigraphic exposures could enhance the output from the vast collection of remote sensing images obtained not only on Earth but also on other planetary bodies, such as Mars.

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Section: Introductionmentioning

confidence: 99%

Extraction of stratigraphic exposures on visible images using a supervised machine learning technique

Noguchi,

Shoji

2023

Front. Earth Sci.

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“…The image segmentation and classification capabilities of Convolutional Neural Networks (CNNs) have shown great promise (Wu et al., 2022) for automated mapping of surface features. Once trained, CNNs can yield a segmented image mask identifying the desired features from a minimally processed background image, making them ideal for recognition and classification tasks such as crater mapping (Latorre et al., 2023).…”

Section: Introductionmentioning

confidence: 99%

A Novel Approach to Impact Crater Mapping and Analysis on Enceladus, Using Machine Learning

Blanco‐Rojas,

Carroll,

Spradlin

et al. 2024

JGR Planets

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Impact cratering is one of the most important processes shaping planetary surfaces, offering valuable clues about the target body's geologic history and composition. However, crater mapping has historically been done manually, a process that has proven to be both arduous and time consuming. This paper outlines a machine learning crater mapping approach for bodies with limited elevation data available (Digital Elevation Models). We applied a Convolutional Neural Network for the detection and morphometry of impact craters on Saturn's moon Enceladus using light‐shadow labels trained on data from the Cassini Imaging Science Subsystem. Our algorithm identified a total of 5,240 features which were used to quantify crater distribution; this included the highest number of small craters (<1–2 km in diameter) recorded on Enceladus by any previous published study. The pool of features was later down‐selected to craters between 0 and 30°N (latitude) imaged at high incidence (>60°) and phase angles (>26°). The down selection was necessary to accurately perform diameter measurements and derive depths from shadow estimation techniques to calculate depth–diameter ratios (d/D); a well‐studied relationship used to constrain planetary surface properties. Results show that the d/D ratio of craters in the equatorial region of Enceladus range from ∼0.06 to 0.37, with a median of 0.19. Our results will inform efforts to constrain the surface properties of this region of Enceladus, potentially also supporting future mission concept design for the Saturnian moon. Future work will explore the simple‐to‐complex crater transition and differences between this area's d/D and Enceladus' northern and southern latitudes.

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“…The missions or instruments that return these images include High Resolution Imaging Science Experiment (HiRISE) [1], ConTeXt Camera (CTX) [2] on board the Mars Reconnaissance Orbiter, and Narrow Angle Camera (NAC) [3], [4] on board the Lunar Reconnaissance Orbiter (LRO) satellite, etc. Publicizing these data is helpful for a variety of computer vision tasks, such as image classification [5], object detection [6], [7], feature recognition [8]- [10], change detection [11] and image denoising [12], [13].…”

Section: Introductionmentioning

confidence: 99%

Automatic Change Detection of Planetary Striped Landform on Mars Surface

Zhang,

Hao,

et al. 2024

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

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Cameras carried by satellites take large-scale images of the planetary surface, which provides data support for exploration missions. With the progress of deep space exploration, the demands to identify surface changes are gradually increasing. There are few studies focus on planetary surface automatic change detection, since it faces the challenges of difficult registration, strong pseudo change, poor automatic detection capacity, etc. To handle these challenges, we propose a deep learning lightweight model SLCD-Net (i.e., striped landform change detection model) for patch-wise change detection of the planetary striped landform. SLCD-Net uses the dual-input-based siamese network as the overall architecture to learn the deep semantic information of pre-and post-temporal images. Importantly, SLCD-Net designs a dual-feature multi-level complementary fusion module between two branches of the siamese network, which can learn the cross-temporal complementary features and eliminate the noise impact. Besides, SLCD-Net exploits a spatial attention module after the fusion unit, which further helps to weaken noise that is produced by fusion. Furthermore, we construct a dataset about dark slope streak (DSS) (a striped landform type) (https://github.com/dl-zfq/Mars-CD-dataset). Training on our DSS dataset enables SLCD-Net to have an advantage in identifying the variation of DSS. The SLCD-Net's detection performance on multiple planetary surface datasets including our DSS test set proves that it has achieved a breakthrough in robustness and generalization. Not limited to the patch-wise task, the change map generated by SLCD-Net applied to different regions also proves that it can be effectively used for pixel-wise Mars striped landform change detection.

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Transfer learning for real-time crater detection on asteroids using a Fully Convolutional Neural Network

Cited by 14 publications

References 28 publications

Extraction of stratigraphic exposures on visible images using a supervised machine learning technique

Extraction of stratigraphic exposures on visible images using a supervised machine learning technique

A Novel Approach to Impact Crater Mapping and Analysis on Enceladus, Using Machine Learning

Automatic Change Detection of Planetary Striped Landform on Mars Surface

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