Planetary surfaces,shaped by billions of years of geologic evolution,display numerous impact craters whose distribution of size,density,and spatial arrangement reveals the celestial body's history.Identifying thes...Planetary surfaces,shaped by billions of years of geologic evolution,display numerous impact craters whose distribution of size,density,and spatial arrangement reveals the celestial body's history.Identifying these craters is essential for planetary science and is currently mainly achieved with deep learning-driven detection algorithms.However,because impact crater characteristics are substantially affected by the geologic environment,surface materials,and atmospheric conditions,the performance of deep learning models can be inconsistent between celestial bodies.In this paper,we first examine how the surface characteristics of the Moon,Mars,and Earth,along with the differences in their impact crater features,affect model performance.Then,we compare crater detection across celestial bodies by analyzing enhanced convolutional neural networks and U-shaped Convolutional Neural Network-based models to highlight how geology,data,and model design affect accuracy and generalization.Finally,we address current deep learning challenges,suggest directions for model improvement,such as multimodal data fusion and cross-planet learning and list available impact crater databases.This review can provide necessary technical support for deep space exploration and planetary science,as well as new ideas and directions for future research on automatic detection of impact craters on celestial body surfaces and on planetary geology.展开更多
Impacted craters are commonly found on the surface of planets, satellites, asteroids and other solar system bodies. In order to speed up the rate of constructing the database of craters, it is important to develop cra...Impacted craters are commonly found on the surface of planets, satellites, asteroids and other solar system bodies. In order to speed up the rate of constructing the database of craters, it is important to develop crater detection algorithms. This paper presents a novel approach to automatically detect craters on planetary surfaces. The approach contains two parts： crater candidate region selection and crater detection. In the first part, crater candidate region selection is achieved by Kanade-Lucas-Tomasi （KLT） detector. Matrix-pattern-oriented least squares support vector machine （MatLSSVM）, as the matrixization version of least square support vector machine （SVM）, inherits the advantages of least squares support vector machine （LSSVM）, reduces storage space greatly and reserves spatial redundancies within each image matrix compared with general LSSVM. The second part of the approach employs MatLSSVM to design classifier for crater detection. Experimental results on the dataset which comprises 160 preprocessed image patches from Google Mars demonstrate that the accuracy rate of crater detection can be up to 88%. In addition, the outstanding feature of the approach introduced in this paper is that it takes resized crater candidate region as input pattern directly to finish crater detection. The results of the last experiment demonstrate that MatLSSVM-based classifier can detect crater regions effectively on the basis of KLT-based crater candidate region selection.展开更多
Craters, one of the most significant features of the lunar surface, have been widely researched because they offer us the relative age of the surface unit as well as crucial geological information. Research on crater ...Craters, one of the most significant features of the lunar surface, have been widely researched because they offer us the relative age of the surface unit as well as crucial geological information. Research on crater detec- tion algorithms （CDAs） of the Moon and other planetary bodies has concentrated on detecting them from imagery data, but the computational cost of detecting large craters using images makes these CDAs impractical. This paper presents a new approach to crater detection that utilizes a digital elevation model instead of images; this enables fully automatic global detection of large craters. Craters were delineated by terrain attributes, and then thresholding maps of terrain attributes were used to transform topographic data into a binary image, finally craters were detected by using the Hough Transform from the binary image. By using the proposed algorithm, we produced a catalog of all craters ≥ 10 km in diameter on the lunar surface and analyzed their distribution and population characteristics.展开更多
基金funded by the National Natural Science Foundation of China(12363009 and 12103020)Natural Science Foundation of Jiangxi Province(20224BAB211011)+1 种基金Youth Talent Project of Science and Technology Plan of Ganzhou(2022CXRC9191 and 2023CYZ26970)Jiangxi Province Graduate Innovation Special Funds Project(YC2024-S529 and YC2023-S672).
文摘Planetary surfaces,shaped by billions of years of geologic evolution,display numerous impact craters whose distribution of size,density,and spatial arrangement reveals the celestial body's history.Identifying these craters is essential for planetary science and is currently mainly achieved with deep learning-driven detection algorithms.However,because impact crater characteristics are substantially affected by the geologic environment,surface materials,and atmospheric conditions,the performance of deep learning models can be inconsistent between celestial bodies.In this paper,we first examine how the surface characteristics of the Moon,Mars,and Earth,along with the differences in their impact crater features,affect model performance.Then,we compare crater detection across celestial bodies by analyzing enhanced convolutional neural networks and U-shaped Convolutional Neural Network-based models to highlight how geology,data,and model design affect accuracy and generalization.Finally,we address current deep learning challenges,suggest directions for model improvement,such as multimodal data fusion and cross-planet learning and list available impact crater databases.This review can provide necessary technical support for deep space exploration and planetary science,as well as new ideas and directions for future research on automatic detection of impact craters on celestial body surfaces and on planetary geology.
基金co-supported by the National Natural Science Foundation of China (No. 61203170)the Fundamental Research Funds for the Central Universities (No. NS2012026)Startup Foundation for Introduced Talents of Nanjing University of Aeronautics and Astronautics (No. 1007-YAH10047)
文摘Impacted craters are commonly found on the surface of planets, satellites, asteroids and other solar system bodies. In order to speed up the rate of constructing the database of craters, it is important to develop crater detection algorithms. This paper presents a novel approach to automatically detect craters on planetary surfaces. The approach contains two parts： crater candidate region selection and crater detection. In the first part, crater candidate region selection is achieved by Kanade-Lucas-Tomasi （KLT） detector. Matrix-pattern-oriented least squares support vector machine （MatLSSVM）, as the matrixization version of least square support vector machine （SVM）, inherits the advantages of least squares support vector machine （LSSVM）, reduces storage space greatly and reserves spatial redundancies within each image matrix compared with general LSSVM. The second part of the approach employs MatLSSVM to design classifier for crater detection. Experimental results on the dataset which comprises 160 preprocessed image patches from Google Mars demonstrate that the accuracy rate of crater detection can be up to 88%. In addition, the outstanding feature of the approach introduced in this paper is that it takes resized crater candidate region as input pattern directly to finish crater detection. The results of the last experiment demonstrate that MatLSSVM-based classifier can detect crater regions effectively on the basis of KLT-based crater candidate region selection.
文摘Craters, one of the most significant features of the lunar surface, have been widely researched because they offer us the relative age of the surface unit as well as crucial geological information. Research on crater detec- tion algorithms （CDAs） of the Moon and other planetary bodies has concentrated on detecting them from imagery data, but the computational cost of detecting large craters using images makes these CDAs impractical. This paper presents a new approach to crater detection that utilizes a digital elevation model instead of images; this enables fully automatic global detection of large craters. Craters were delineated by terrain attributes, and then thresholding maps of terrain attributes were used to transform topographic data into a binary image, finally craters were detected by using the Hough Transform from the binary image. By using the proposed algorithm, we produced a catalog of all craters ≥ 10 km in diameter on the lunar surface and analyzed their distribution and population characteristics.
