Impact craters are important for understanding the evolution of lunar geologic and surface erosion rates,among other functions.However,the morphological characteristics of these micro impact craters are not obvious an...Impact craters are important for understanding the evolution of lunar geologic and surface erosion rates,among other functions.However,the morphological characteristics of these micro impact craters are not obvious and they are numerous,resulting in low detection accuracy by deep learning models.Therefore,we proposed a new multi-scale fusion crater detection algorithm(MSF-CDA)based on the YOLO11 to improve the accuracy of lunar impact crater detection,especially for small craters with a diameter of<1 km.Using the images taken by the LROC(Lunar Reconnaissance Orbiter Camera)at the Chang’e-4(CE-4)landing area,we constructed three separate datasets for craters with diameters of 0-70 m,70-140 m,and>140 m.We then trained three submodels separately with these three datasets.Additionally,we designed a slicing-amplifying-slicing strategy to enhance the ability to extract features from small craters.To handle redundant predictions,we proposed a new Non-Maximum Suppression with Area Filtering method to fuse the results in overlapping targets within the multi-scale submodels.Finally,our new MSF-CDA method achieved high detection performance,with the Precision,Recall,and F1 score having values of 0.991,0.987,and 0.989,respectively,perfectly addressing the problems induced by the lesser features and sample imbalance of small craters.Our MSF-CDA can provide strong data support for more in-depth study of the geological evolution of the lunar surface and finer geological age estimations.This strategy can also be used to detect other small objects with lesser features and sample imbalance problems.We detected approximately 500,000 impact craters in an area of approximately 214 km2 around the CE-4 landing area.By statistically analyzing the new data,we updated the distribution function of the number and diameter of impact craters.Finally,we identified the most suitable lighting conditions for detecting impact crater targets by analyzing the effect of different lighting conditions on the detection accuracy.展开更多
国际月球科研站ILRS (International Lunar Research Station)的建立是中国顺应当前形势的重要太空战略部署。为研究基于多源数据的形貌分析在ILRS建设过程中的应用,本研究将“ILRS计划”分为选址和科考阶段,分析ILRS选址阶段需考虑的...国际月球科研站ILRS (International Lunar Research Station)的建立是中国顺应当前形势的重要太空战略部署。为研究基于多源数据的形貌分析在ILRS建设过程中的应用,本研究将“ILRS计划”分为选址和科考阶段,分析ILRS选址阶段需考虑的约束条件,简述正射影像、月表地形、微波辐射计、多/高光谱成像、测月雷达、伽马射线等多源数据在选址阶段的应用,利用嫦娥二号DOM、DEM、LRO Diviner辐射计、LOLA激光高度计以及地质制图等多源数据对月球南极地形特征、温度条件、光照条件、对地能见度和地质特征进行分析,提出在ILRS选址阶段应考虑的诸多因素和适合着陆选址的区域特点,并以Amundsen区域为例,选择了3个候选着陆点。最后,以Shackleton、Shoemaker、de Gerlache和Amundsen撞击坑为代表,基于多源数据分析结果为ILRS科考阶段巡视路线的规划、水冰探测的分析、观测基站的设立提供参考。综上,本研究基于多源数据的形貌分析结果对ILRS的选址和完成科学考察任务具有重要参考价值。展开更多
基金the National Key Research and Development Program of China (Grant No.2022YFF0711400)the National Space Science Data Center Youth Open Project (Grant No. NSSDC2302001)
文摘Impact craters are important for understanding the evolution of lunar geologic and surface erosion rates,among other functions.However,the morphological characteristics of these micro impact craters are not obvious and they are numerous,resulting in low detection accuracy by deep learning models.Therefore,we proposed a new multi-scale fusion crater detection algorithm(MSF-CDA)based on the YOLO11 to improve the accuracy of lunar impact crater detection,especially for small craters with a diameter of<1 km.Using the images taken by the LROC(Lunar Reconnaissance Orbiter Camera)at the Chang’e-4(CE-4)landing area,we constructed three separate datasets for craters with diameters of 0-70 m,70-140 m,and>140 m.We then trained three submodels separately with these three datasets.Additionally,we designed a slicing-amplifying-slicing strategy to enhance the ability to extract features from small craters.To handle redundant predictions,we proposed a new Non-Maximum Suppression with Area Filtering method to fuse the results in overlapping targets within the multi-scale submodels.Finally,our new MSF-CDA method achieved high detection performance,with the Precision,Recall,and F1 score having values of 0.991,0.987,and 0.989,respectively,perfectly addressing the problems induced by the lesser features and sample imbalance of small craters.Our MSF-CDA can provide strong data support for more in-depth study of the geological evolution of the lunar surface and finer geological age estimations.This strategy can also be used to detect other small objects with lesser features and sample imbalance problems.We detected approximately 500,000 impact craters in an area of approximately 214 km2 around the CE-4 landing area.By statistically analyzing the new data,we updated the distribution function of the number and diameter of impact craters.Finally,we identified the most suitable lighting conditions for detecting impact crater targets by analyzing the effect of different lighting conditions on the detection accuracy.
文摘国际月球科研站ILRS (International Lunar Research Station)的建立是中国顺应当前形势的重要太空战略部署。为研究基于多源数据的形貌分析在ILRS建设过程中的应用,本研究将“ILRS计划”分为选址和科考阶段,分析ILRS选址阶段需考虑的约束条件,简述正射影像、月表地形、微波辐射计、多/高光谱成像、测月雷达、伽马射线等多源数据在选址阶段的应用,利用嫦娥二号DOM、DEM、LRO Diviner辐射计、LOLA激光高度计以及地质制图等多源数据对月球南极地形特征、温度条件、光照条件、对地能见度和地质特征进行分析,提出在ILRS选址阶段应考虑的诸多因素和适合着陆选址的区域特点,并以Amundsen区域为例,选择了3个候选着陆点。最后,以Shackleton、Shoemaker、de Gerlache和Amundsen撞击坑为代表,基于多源数据分析结果为ILRS科考阶段巡视路线的规划、水冰探测的分析、观测基站的设立提供参考。综上,本研究基于多源数据的形貌分析结果对ILRS的选址和完成科学考察任务具有重要参考价值。
