{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,3]],"date-time":"2025-11-03T23:03:41Z","timestamp":1762211021318,"version":"build-2065373602"},"reference-count":44,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2022,1,29]],"date-time":"2022-01-29T00:00:00Z","timestamp":1643414400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Science &amp; Technology Department of Sichuan Provience","award":["No. 2021YFH0140"],"award-info":[{"award-number":["No. 2021YFH0140"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Lunar crater detection plays an important role in lunar exploration, while machine learning (ML) exhibits promising advantages in the field. However, previous ML works almost all used a single type of lunar map, such as an elevation map (DEM) or orthographic projection map (WAC), to extract crater features; the two types of images have individual limitations on reflecting the crater features, which lead to insufficient feature information, in turn influencing the detection performance. To address this limitation, we, in this work, propose feature complementary of the two types of images and accordingly explore an advanced dual-path convolutional neural network (Dual-Path) based on a U-NET structure to effectively conduct feature integration. Dual-Path consists of a contracting path, bridging path, and expanding path. The contracting path separately extracts features from DEM and WAC images by means of two independent input branches, while the bridging layer integrates the two types of features by 1 \u00d7 1 convolution. Finally, the expanding path, coupled with the attention mechanism, further learns and optimizes the feature information. In addition, a special deep convolution block with a residual module is introduced to avoid network degradation and gradient disappearance. The ablation experiment and the comparison of four competitive models only using DEM features confirm that the feature complementary can effectively improve the detection performance and speed. Our model is further verified by different regions of the whole moon, exhibiting high robustness and potential in practical applications.<\/jats:p>","DOI":"10.3390\/rs14030661","type":"journal-article","created":{"date-parts":[[2022,1,30]],"date-time":"2022-01-30T00:12:56Z","timestamp":1643501576000},"page":"661","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":17,"title":["Coupling Complementary Strategy to U-Net Based Convolution Neural Network for Detecting Lunar Impact Craters"],"prefix":"10.3390","volume":"14","author":[{"given":"Yuqing","family":"Mao","sequence":"first","affiliation":[{"name":"School of Computer, Sichuan University, Chengdu 610065, China"},{"name":"School of Cyber Science and Engineering, Sichuan University, Chengdu 610065, China"}]},{"given":"Rongao","family":"Yuan","sequence":"additional","affiliation":[{"name":"School of Computer, Sichuan University, Chengdu 610065, China"}]},{"given":"Wei","family":"Li","sequence":"additional","affiliation":[{"name":"School of Computer, Sichuan University, Chengdu 610065, China"}]},{"given":"Yijing","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Computer, Sichuan University, Chengdu 610065, China"}]}],"member":"1968","published-online":{"date-parts":[[2022,1,29]]},"reference":[{"key":"ref_1","unstructured":"(2021, May 10). NASA Catalogue of Lunar Nomenclature, Available online: https:\/\/ntrs.nasa.gov\/citations\/19830003761."},{"key":"ref_2","unstructured":"Losiak, A., Wilhelms, D.E., Byrne, C.J., Thaisen, K.G., Weider, S.Z., Kohout, T., and Kring, D.A. (2009, January 23\u201327). A new lunar impact crater database. Proceedings of the Lunar and Planetary Science Conference, Woodlands, TX, USA."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"880","DOI":"10.1016\/j.pss.2009.03.009","article-title":"Automatic detection of sub-km craters in high resolution planetary images","volume":"57","author":"Urbach","year":"2009","journal-title":"Planet. Space Sci."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"798","DOI":"10.1016\/j.icarus.2013.06.028","article-title":"Crater detection, classification and contextual information extraction in lunar images using a novel algorithm","volume":"226","author":"Vijayan","year":"2013","journal-title":"Icarus"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"2419","DOI":"10.1016\/j.asr.2014.08.018","article-title":"A machine learning approach to crater detection from topographic data","volume":"54","author":"Di","year":"2014","journal-title":"Adv. Space Res."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Mu, Y., Ding, W., Tao, D., and Stepinski, T.F. (August, January 31). Biologically inspired model for crater detection. Proceedings of the 2011 International Joint Conference on Neural Networks, San Jose, CA, USA.","DOI":"10.1109\/IJCNN.2011.6033542"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"21","DOI":"10.1016\/j.asr.2005.08.022","article-title":"Automated detection and classification of lunar craters using multiple approaches","volume":"37","author":"Sawabe","year":"2006","journal-title":"Adv. Space Res."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"885","DOI":"10.1109\/LGRS.2012.2226432","article-title":"Crater Detection Using the Morphological Characteristics of Chang\u2019E-1 Digital Elevation Models","volume":"10","author":"Xie","year":"2013","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"3681","DOI":"10.1109\/TGRS.2018.2806371","article-title":"Lunar Crater Detection Based on Terrain Analysis and Mathematical Morphology Methods Using Digital Elevation Models","volume":"56","author":"Chen","year":"2018","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"522","DOI":"10.1007\/978-3-319-19390-8_59","article-title":"Crater detection in multi-ring basins of mercury","volume":"9117","author":"Pedrosa","year":"2015","journal-title":"Lect. Notes Comput. Sci."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"5777","DOI":"10.1109\/TGRS.2019.2902198","article-title":"Active Machine Learning Approach for Crater Detection from Planetary Imagery and Digital Elevation Models","volume":"57","author":"Wang","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"181","DOI":"10.1109\/TGRS.2018.2852717","article-title":"Coarse-to-Fine Extraction of Small-Scale Lunar Impact Craters from the CCD Images of the Chang\u2019E Lunar Orbiters","volume":"57","author":"Kang","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Stepinski, T.F., Ding, W., and Vilalta, R. (2012). Detecting Impact Craters in Planetary Images Using Machine Learning. Intelligent Data Analysis for Real-Life Applications, IGI Global.","DOI":"10.4018\/978-1-4666-1806-0.ch008"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Jin, Y., He, F., Liu, S., and Tong, X. (2019, January 5\u20137). Small Scale Crater Detection based on Deep Learning with Multi-Temporal Samples of High-Resolution Images. Proceedings of the 2019 10th International Workshop on the Analysis of Multitemporal Remote Sensing Images (MultiTemp), Shanghai, China.","DOI":"10.1109\/Multi-Temp.2019.8866941"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1137","DOI":"10.1109\/TPAMI.2016.2577031","article-title":"Faster R-CNN: Towards real-time object detection with region proposal networks","volume":"39","author":"Ren","year":"2017","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"113749","DOI":"10.1016\/j.icarus.2020.113749","article-title":"Automated crater shape retrieval using weakly-supervised deep learning","volume":"345","author":"Menou","year":"2020","journal-title":"Icarus"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"27","DOI":"10.1016\/j.icarus.2018.06.022","article-title":"Lunar crater identification via deep learning","volume":"317","author":"Silburt","year":"2019","journal-title":"Icarus"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Ronneberger, O., Fischer, P., and Brox, T. (2015, January 5\u20139). U-net: Convolutional networks for biomedical image segmentation. Proceedings of the Medical Image Computing and Computer-Assisted Intervention, Cambridge, UK.","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Wang, S., Fan, Z., Li, Z., Zhang, H., and Wei, C. (2020). An Effective Lunar Crater Recognition Algorithm Based on Convolutional Neural Network. Remote Sens., 12.","DOI":"10.3390\/rs12172694"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"1615","DOI":"10.1016\/j.asr.2019.07.017","article-title":"Automated crater detection algorithms from a machine learning perspective in the convolutional neural network era","volume":"64","author":"DeLatte","year":"2019","journal-title":"Adv. Space Res."},{"key":"ref_21","unstructured":"(2021, May 10). LRO LOLA and Kaguya Terrain Camera DEM Merge 60N60S 512ppd (59m), Available online: https:\/\/astrogeology.usgs.gov\/search\/map\/Moon\/LRO\/LOLA\/Lunar_LRO_LOLAKaguya_DEMmerge_60N60S_512ppd."},{"key":"ref_22","unstructured":"(2021, May 10). Lunar Reconnaissance Orbiter Camera Global Morphological Map of the Moon. Available online: http:\/\/wms.lroc.asu.edu\/lroc\/view_rdr\/WAC_GLOBAL."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"1153","DOI":"10.1109\/TASSP.1981.1163711","article-title":"Cubic convolution interpolation for digital image processing","volume":"29","author":"Keys","year":"1981","journal-title":"IEEE Trans. Acoust. Speech Signal Process."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"41","DOI":"10.1016\/j.pss.2017.05.006","article-title":"Crater density differences: Exploring regional resurfacing, secondary crater populations, and crater saturation equilibrium on the moon","volume":"162","author":"Povilaitis","year":"2018","journal-title":"Planet. Space Sci."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"1504","DOI":"10.1126\/science.1195050","article-title":"Global Distribution of Large Lunar Craters: Implications for Resurfacing and Impactor Populations","volume":"329","author":"Head","year":"2010","journal-title":"Science"},{"key":"ref_26","unstructured":"(2021, May 10). Cartopy: A Cartographic Python Library with a Matplotlib Inter-Face. Available online: http:\/\/scitools.org.uk\/cartopy\/index.html."},{"key":"ref_27","unstructured":"Iglovikov, V., and Shvets, A. (2018). Ternausnet: U-net with vgg11 encoder pre-trained on imagenet for image segmentation. ArXiv."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Zhou, Z., Siddiquee, M.M.R., Tajbakhsh, N., and Liang, J. (2018). Unet++: A nested u-net architecture for medical image segmentation. Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support, Springer. [1st ed.].","DOI":"10.1007\/978-3-030-00889-5_1"},{"key":"ref_29","first-page":"109490F","article-title":"Deep residual dense U-Net for resolution enhancement in accelerated MRI acquisition","volume":"Volume 10949","author":"Ding","year":"2019","journal-title":"Proceedings of the Medical Imaging 2019: Image Processing"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"107404","DOI":"10.1016\/j.patcog.2020.107404","article-title":"U2-Net: Going deeper with nested U-structure for salient object detection","volume":"106","author":"Qin","year":"2020","journal-title":"Pattern Recognit."},{"key":"ref_31","unstructured":"Ioffe, S., and Szegedy, C. (2015, January 11). Batch normalization: Accelerating deep network training by reducing internal covariate shift. Proceedings of the International Conference on Machine Learning, Lille, France."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2017, January 21\u201326). Deep residual learning for image recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Lin, T.Y., Doll\u00e1r, P., Girshick, R., He, K., Hariharan, B., and Belongie, S. (2017, January 21\u201326). Feature pyramid networks for object detection. Proceedings of the 30th IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.106"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Cao, Y., Xu, J., Lin, S., Wei, F., and Hu, H. (2019, January 27\u201328). GCNet: Non-Local Networks Meet Squeeze-Excitation Networks and Beyond. Proceedings of the 2019 IEEE\/CVF International Conference on Computer Vision Workshop (ICCVW), Seoul, Korea.","DOI":"10.1109\/ICCVW.2019.00246"},{"key":"ref_35","first-page":"1929","article-title":"Dropout: A Simple Way to Prevent Neural Networks from Overfitting","volume":"15","author":"Srivastava","year":"2014","journal-title":"J. Mach. Learn. Res."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"453","DOI":"10.7717\/peerj.453","article-title":"Scikit-image: Image processing in Python","volume":"2","author":"Boulogne","year":"2014","journal-title":"PeerJ"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"2944","DOI":"10.1109\/JSTARS.2019.2918302","article-title":"Segmentation Convolutional Neural Networks for Automatic Crater Detection on Mars","volume":"12","author":"Delatte","year":"2019","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Zhou, L., Zhang, C., and Wu, M. (2018, January 18\u201322). D-LinkNet: LinkNet With Pretrained Encoder and Dilated Convolution for High Resolution Satellite Imagery Road Extraction. Proceedings of the 2018 IEEE\/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPR), Salt Lake City, UT, USA.","DOI":"10.1109\/CVPRW.2018.00034"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Peng, B., Li, Y., Fan, K., Yuan, L., Tong, L., and He, L. (August, January 28). New Network Based on D-Linknet and Densenet for High Resolution Satellite Imagery Road Extraction. Proceedings of the IGARSS 2019\u20132019 IEEE International Geoscience and Remote Sensing Symposium, Yokohama, Japan.","DOI":"10.1109\/IGARSS.2019.8898640"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Zhu, Q., Zheng, Y., Jiang, Y., and Yang, J. (August, January 28). Efficient Multi-Class Semantic Segmentation of High Resolution Aerial Imagery with Dilated LinkNet. Proceedings of the IGARSS 2019\u20132019 IEEE International Geoscience and Remote Sensing Symposium, Yokohama, Japan.","DOI":"10.1109\/IGARSS.2019.8900281"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Yuan, S., Yang, K., Li, X., and Cai, H. (2020). Automatic Seamline Determination for Urban Image Mosaicking Based on Road Probability Map from the D-LinkNet Neural Network. Sensors, 20.","DOI":"10.3390\/s20071832"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"014006","DOI":"10.1117\/1.JMI.6.1.014006","article-title":"Recurrent residual U-Net for medical image segmentation","volume":"6","author":"Alom","year":"2019","journal-title":"J. Med. Imaging"},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Zhang, W., Tang, P., Zhao, L., and Huang, Q. (2019, January 22\u201324). A Comparative Study of U-Nets with Various Convolution Components for Building Extraction. Proceedings of the 2019 Joint Urban Remote Sensing Event (JURSE), Vannes, France.","DOI":"10.1109\/JURSE.2019.8809055"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"703","DOI":"10.1007\/s13246-021-01019-w","article-title":"Improved U-Net architecture with VGG-16 for brain tumor segmentation","volume":"44","author":"Ghosh","year":"2021","journal-title":"Phys. Eng. Sci. Med."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/3\/661\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T22:11:10Z","timestamp":1760134270000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/3\/661"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,1,29]]},"references-count":44,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2022,2]]}},"alternative-id":["rs14030661"],"URL":"https:\/\/doi.org\/10.3390\/rs14030661","relation":{},"ISSN":["2072-4292"],"issn-type":[{"type":"electronic","value":"2072-4292"}],"subject":[],"published":{"date-parts":[[2022,1,29]]}}}