{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T01:37:44Z","timestamp":1760233064198,"version":"build-2065373602"},"reference-count":68,"publisher":"MDPI AG","issue":"24","license":[{"start":{"date-parts":[[2022,12,14]],"date-time":"2022-12-14T00:00:00Z","timestamp":1670976000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Youth Innovation Promotion Association under Grant","award":["E1213A02","22E0223301"],"award-info":[{"award-number":["E1213A02","22E0223301"]}]},{"name":"Key Research Program of Frontier Sciences, CAS","award":["E1213A02","22E0223301"],"award-info":[{"award-number":["E1213A02","22E0223301"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Small Celestial Body (SCB) image matching is essential for deep space exploration missions. In this paper, a large-scale invariant method is proposed to improve the matching accuracy of SCB images under large-scale variations. Specifically, we designed a novel network named DeepSpace-ScaleNet, which employs an attention mechanism for estimating the scale ratio to overcome the significant variation between two images. Firstly, the Global Attention-DenseASPP (GA-DenseASPP) module is proposed to refine feature extraction in deep space backgrounds. Secondly, the Correlation-Aware Distribution Predictor (CADP) module is built to capture the connections between correlation maps and improve the accuracy of the scale distribution estimation. To the best of our knowledge, this is the first work to explore large-scale SCB image matching using Transformer-based neural networks rather than traditional handcrafted feature descriptors. We also analysed the effects of different scale and illumination changes on SCB image matching in the experiment. To train the network and verify its effectiveness, we created a simulation dataset containing light variations and scale variations named Virtual SCB Dataset. Experimental results show that the DeepSpace-ScaleNet achieves a current state-of-the-art SCB image scale estimation performance. It also shows the best accuracy and robustness in image matching and relative pose estimation.<\/jats:p>","DOI":"10.3390\/rs14246339","type":"journal-article","created":{"date-parts":[[2022,12,15]],"date-time":"2022-12-15T03:01:51Z","timestamp":1671073311000},"page":"6339","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["A Large-Scale Invariant Matching Method Based on DeepSpace-ScaleNet for Small Celestial Body Exploration"],"prefix":"10.3390","volume":"14","author":[{"given":"Mingrui","family":"Fan","sequence":"first","affiliation":[{"name":"National Space Science Centre, Chinese Academy of Sciences, Beijing 100190, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3237-1135","authenticated-orcid":false,"given":"Wenlong","family":"Lu","sequence":"additional","affiliation":[{"name":"National Space Science Centre, Chinese Academy of Sciences, Beijing 100190, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"},{"name":"School of Fundamental Physics and Mathematical Sciences, Hangzhou Institute for Advanced Study, UCAS, Hangzhou 310024, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1643-1705","authenticated-orcid":false,"given":"Wenlong","family":"Niu","sequence":"additional","affiliation":[{"name":"National Space Science Centre, Chinese Academy of Sciences, Beijing 100190, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"}]},{"given":"Xiaodong","family":"Peng","sequence":"additional","affiliation":[{"name":"National Space Science Centre, Chinese Academy of Sciences, Beijing 100190, China"},{"name":"School of Fundamental Physics and Mathematical Sciences, Hangzhou Institute for Advanced Study, UCAS, Hangzhou 310024, China"}]},{"given":"Zhen","family":"Yang","sequence":"additional","affiliation":[{"name":"National Space Science Centre, Chinese Academy of Sciences, Beijing 100190, China"}]}],"member":"1968","published-online":{"date-parts":[[2022,12,14]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"100551","DOI":"10.1016\/j.paerosci.2019.06.002","article-title":"Recent development of autonomous GNC technologies for small celestial body descent and landing","volume":"110","author":"Ge","year":"2019","journal-title":"Prog. Aerosp. Sci."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"22","DOI":"10.1016\/j.actaastro.2021.10.025","article-title":"Deep learning-based spacecraft relative navigation methods: A survey","volume":"191","author":"Song","year":"2022","journal-title":"Acta Astronaut."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Ye, M., Li, F., Yan, J., H\u00e9rique, A., Kofman, W., Rogez, Y., Andert, T.P., Guo, X., and Barriot, J.P. (2021). Rosetta Consert Data as a Testbed for in Situ Navigation of Space Probes and Radiosciences in Orbit\/Escort Phases for Small Bodies of the Solar System. Remote Sens., 13.","DOI":"10.3390\/rs13183747"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Zhong, W., Jiang, J., and Ma, Y. (2022). L2AMF-Net: An L2-Normed Attention and Multi-Scale Fusion Network for Lunar Image Patch Matching. Remote Sens., 14.","DOI":"10.3390\/rs14205156"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"149","DOI":"10.1007\/s42064-020-0075-8","article-title":"Visual localization for asteroid touchdown operation based on local image features","volume":"4","author":"Anzai","year":"2020","journal-title":"Astrodynamics"},{"key":"ref_6","unstructured":"de Santayana, R.P., and Lauer, M. (2015, January 19\u201323). Optical measurements for rosetta navigation near the comet. Proceedings of the 25th International Symposium on Space Flight Dynamics (ISSFD), Munich, Germany."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"45","DOI":"10.2322\/tjsass.58.45","article-title":"Evaluation of Interest-Region Detectors and Descriptors for Automatic Landmark Tracking on Asteroids","volume":"58","author":"Takeishi","year":"2015","journal-title":"Trans. Jpn. Soc. Aeronaut. Space Sci."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"383","DOI":"10.1016\/j.actaastro.2020.04.055","article-title":"Visual navigation algorithm based on line geomorphic feature matching for Mars landing","volume":"173","author":"Shao","year":"2020","journal-title":"Acta Astronaut."},{"key":"ref_9","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_10","first-page":"1785","article-title":"Optical landmark detection for spacecraft navigation","volume":"114","author":"Cheng","year":"2003","journal-title":"Adv. Astronaut. Sci."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1205","DOI":"10.14358\/PERS.71.10.1205","article-title":"Automated crater detection, a new tool for Mars cartography and chronology","volume":"71","author":"Kim","year":"2005","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"DeTone, D., Malisiewicz, T., and Rabinovich, A. (2018, January 18\u201322). Superpoint: Self-supervised interest point detection and description. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPRW.2018.00060"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Sarlin, P.E., Detone, D., Malisiewicz, T., and Rabinovich, A. (2020, January 14\u201329). Superglue: Learning feature matching with graph neural networks. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.00499"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Barroso-Laguna, A., Tian, Y., and Mikolajczyk, K. (2022, January 20\u201325). ScaleNet: A Shallow Architecture for Scale Estimation. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Nashville, TN, USA.","DOI":"10.1109\/CVPR52688.2022.01247"},{"key":"ref_15","first-page":"583","article-title":"Learning to Reduce Scale Differences for Large-Scale Invariant Image Matching","volume":"61","author":"Fu","year":"2022","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"ref_16","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 IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.106"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Ghiasi, G., and Fowlkes, C.C. (2016, January 11\u201314). Laplacian Pyramid Reconstruction and Refinement for Semantic Segmentation. Proceedings of the European Conference on Computer Vision, Amsterdam, The Netherlands.","DOI":"10.1007\/978-3-319-46487-9_32"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Zhou, L., Zhu, S., Shen, T., Wang, J., Fang, T., and Quan, L. (2017, January 22\u201329). Progressive large-scale-invariant image matching in scale space. Proceedings of the IEEE International Conference on Computer Vision, Venice, Italy.","DOI":"10.1109\/ICCV.2017.259"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Rau, A., Garcia-Hernando, G., Stoyanov, D., Brostow, G.J., and Turmukhambetov, D. (2020, January 23\u201328). Predicting visual overlap of images through interpretable non-metric box embeddings. Proceedings of the European Conference on Computer Vision, Glasgow, UK.","DOI":"10.1007\/978-3-030-58558-7_37"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"22","DOI":"10.1016\/j.inffus.2021.02.012","article-title":"A review of multimodal image matching: Methods and applications","volume":"73","author":"Jiang","year":"2021","journal-title":"Inf. Fusion"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"23","DOI":"10.1007\/s11263-020-01359-2","article-title":"Image matching from handcrafted to deep features: A survey","volume":"129","author":"Ma","year":"2021","journal-title":"Int. J. Comput. Vis."},{"key":"ref_22","first-page":"10","article-title":"A combined corner and edge detector","volume":"15","author":"Harris","year":"1988","journal-title":"Alvey Vis. Conf."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Rosten, E., and Drummond, T. (2006, January 7\u201313). Machine learning for high-speed corner detection. Proceedings of the European Conference on Computer Vision, Graz, Austria.","DOI":"10.1007\/11744023_34"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"91","DOI":"10.1023\/B:VISI.0000029664.99615.94","article-title":"Distinctive image features from scale-invariant keypoints","volume":"60","author":"Lowe","year":"2004","journal-title":"Int. J. Comput. Vis."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"346","DOI":"10.1016\/j.cviu.2007.09.014","article-title":"Speeded-up robust features (SURF)","volume":"110","author":"Bay","year":"2008","journal-title":"Comput. Vis. Image Underst."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Rublee, E., Rabaud, V., Konolige, K., and Bradski, G. (2011, January 6\u201313). ORB: An efficient alternative to SIFT or SURF. Proceedings of the 2011 International Conference on Computer Vision, Barcelona, Spain.","DOI":"10.1109\/ICCV.2011.6126544"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"10576","DOI":"10.1007\/s10489-021-02990-3","article-title":"Dynamic-scale grid structure with weighted-scoring strategy for fast feature matching","volume":"52","author":"Yang","year":"2022","journal-title":"Appl. Intell."},{"key":"ref_28","first-page":"5835","article-title":"Key.Net: Keypoint Detection by Handcrafted and Learned CNN Filters","volume":"2019","author":"Laguna","year":"2019","journal-title":"Proc. IEEE Int. Conf. Comput. Vis."},{"key":"ref_29","first-page":"6234","article-title":"LF-Net: Learning Local Features from Images","volume":"2018","author":"Ono","year":"2018","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Sun, J., Shen, Z., Wang, Y., Bao, H., and Zhou, X. (2021, January 21\u201324). LoFTR: Detector-free local feature matching with transformers. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Nashville, TN, USA.","DOI":"10.1109\/CVPR46437.2021.00881"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"1413","DOI":"10.1017\/S0373463318000358","article-title":"A Novel Approach to Visual Navigation Based on Feature Line Correspondences for Precision Landing","volume":"71","author":"Shao","year":"2018","journal-title":"J. Navig."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Matthies, L., Huertas, A., Cheng, Y., and Johnson, A. (2008, January 19\u201323). Stereo Vision and Shadow Analysis for Landing Hazard Detection. Proceedings of the 2008 IEEE International Conference on Robotics and Automation, Pasadena, CA, USA.","DOI":"10.1109\/ROBOT.2008.4543625"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"83","DOI":"10.1007\/s42064-022-0146-0","article-title":"Robust Template Feature Matching Method Using Motion-Constrained DCF Designed for Visual Navigation in Asteroid Landing","volume":"7","author":"Wang","year":"2023","journal-title":"Astrodynamics"},{"key":"ref_34","unstructured":"Johnson, A.E., Cheng, Y., and Matthies, L.H. (2000, January 25). Machine vision for autonomous small body navigation. Proceedings of the 2000 IEEE Aerospace Conference. Proceedings (Cat. No. 00TH8484), Big Sky, MT, USA."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"1747","DOI":"10.1080\/01691864.2012.685227","article-title":"SLAM-based navigation scheme for pinpoint landing on small celestial body","volume":"26","author":"Cocaud","year":"2012","journal-title":"Adv. Robot."},{"key":"ref_36","unstructured":"Cheng, Y., and Miller, J.K. (2003, January 13\u201317). Autonomous landmark based spacecraft navigation system. Proceedings of the 2003 AAS\/AIAA Astrodynamics Specialist Conference, Big Sky, MT, USA."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"1810","DOI":"10.1016\/j.asr.2013.04.011","article-title":"A new approach based on crater detection and matching for visual navigation in planetary landing","volume":"53","author":"Yu","year":"2014","journal-title":"Adv. Space Res."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"171","DOI":"10.1016\/j.actaastro.2018.02.033","article-title":"Visual Navigation Using Edge Curve Matching for Pinpoint Planetary Landing","volume":"146","author":"Cui","year":"2018","journal-title":"Acta Astronaut."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.ast.2017.07.014","article-title":"A novel crater recognition based visual navigation approach for asteroid precise pin-point landing","volume":"70","author":"Tian","year":"2017","journal-title":"Aerosp. Sci. Technol."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Xiao, T., Liu, Y., Zhou, B., Jiang, Y., and Sun, J. (2018, January 8\u201314). Unified Perceptual Parsing for Scene Understanding. Proceedings of the Proceedings of the European conference on computer vision (ECCV), Munich, Germany.","DOI":"10.1007\/978-3-030-01228-1_26"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Eigen, D., and Fergus, R. (2015, January 7\u201313). Predicting depth, surface normals and semantic labels with a common multi-scale convolutional architecture. Proceedings of the IEEE International Conference on Computer Vision, Santiago, Chile.","DOI":"10.1109\/ICCV.2015.304"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"1915","DOI":"10.1109\/TPAMI.2012.231","article-title":"Learning Hierarchical Features for Scene Labeling","volume":"35","author":"Farabet","year":"2012","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Lin, G., Shen, C., Van Den Hengel, A., and Reid, I. (2016, January 27\u201330). Efficient piecewise training of deep structured models for semantic segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.348"},{"key":"ref_44","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 International Conference on Medical Image Computing and Computer-Assisted Intervention, Munich, Germany.","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Pohlen, T., Hermans, A., Mathias, M., and Leibe, B. (2017, January 21\u201326). Full-resolution residual networks for semantic segmentation in street scenes. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.353"},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Amirul Islam, M., Rochan, M., Bruce, N.D.B., and Wang, Y. (2017, January 21\u201326). Gated feedback refinement network for dense image labeling. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.518"},{"key":"ref_47","unstructured":"Oktay, O., Schlemper, J., Le Folgoc, L., Lee, M., Heinrich, M., Misawa, K., Mori, K., McDonagh, S., Hammerla, N.Y., and Kainz, B. (2018). Attention U-Net: Learning Where to Look for the Pancreas. arXiv."},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Zhao, H., Shi, J., Qi, X., Wang, X., and Jia, J. (2017, January 21\u201326). Pyramid scene parsing network. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.660"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"834","DOI":"10.1109\/TPAMI.2017.2699184","article-title":"Deeplab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected Crfs","volume":"40","author":"Chen","year":"2017","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_50","unstructured":"Chen, L.-C., Papandreou, G., Schroff, F., and Adam, H. (2017). Rethinking atrous convolution for semantic image segmentation. arXiv."},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Chen, L.-C., Zhu, Y., Papandreou, G., Schroff, F., and Adam, H. (2018, January 8\u201314). Encoder-decoder with atrous separable convolution for semantic image segmentation. Proceedings of the European Conference on Computer Vision (ECCV), Munich, Germany.","DOI":"10.1007\/978-3-030-01234-2_49"},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Zhao, X., Pang, Y., Zhang, L., Lu, H., and Zhang, L. (2020, January 23\u201328). Suppress and balance: A simple gated network for salient object detection. Proceedings of the European Conference on Computer Vision, Glasgow, UK.","DOI":"10.1007\/978-3-030-58536-5_3"},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Dai, J., Qi, H., Xiong, Y., Li, Y., Zhang, G., Hu, H., and Wei, Y. (2017, January 22\u201329). Deformable Convolutional Networks. Proceedings of the IEEE International Conference on Computer Vision, Venice, Italy.","DOI":"10.1109\/ICCV.2017.89"},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Zhu, X., Hu, H., Lin, S., and Dai, J. (2019, January 16\u201320). Deformable Convnets V2: More Deformable, Better Results. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00953"},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Liu, S., and Huang, D. (2018, January 8\u201314). Receptive field block net for accurate and fast object detection. Proceedings of the European conference on computer vision (ECCV), Munich, Germany.","DOI":"10.1007\/978-3-030-01252-6_24"},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"Zhao, X., Zhang, L., Pang, Y., Lu, H., and Zhang, L. (2020, January 23\u201328). A single stream network for robust and real-time RGB-D salient object detection. Proceedings of the European Conference on Computer Vision, Glasgow, UK.","DOI":"10.1007\/978-3-030-58542-6_39"},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"Rocco, I., Arandjelovic, R., and Sivic, J. (2017, January 21\u201326). Convolutional neural network architecture for geometric matching. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.12"},{"key":"ref_58","unstructured":"Liu, Y., Shao, Z., and Hoffmann, N. (2021). Global Attention Mechanism: Retain Information to Enhance Channel-Spatial Interactions. arXiv."},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"Woo, S., Park, J., Lee, J.-Y., and Kweon, I.S. (2018, January 8\u201314). Cbam: Convolutional Block Attention Module. Proceedings of the Proceedings of the European conference on computer vision (ECCV), Munich, Germany.","DOI":"10.1007\/978-3-030-01234-2_1"},{"key":"ref_60","unstructured":"Park, J., Woo, S., Lee, J.-Y., and Kweon, I.S. (2018). Bam: Bottleneck Attention Module. arXiv."},{"key":"ref_61","doi-asserted-by":"crossref","unstructured":"Misra, D., Nalamada, T., Arasanipalai, A.U., and Hou, Q. (2021, January 5\u20139). Rotate to Attend: Convolutional Triplet Attention Module. Proceedings of the Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision, Virtual.","DOI":"10.1109\/WACV48630.2021.00318"},{"key":"ref_62","doi-asserted-by":"crossref","unstructured":"Fukui, H., Hirakawa, T., Yamashita, T., and Fujiyoshi, H. (2019, January 16\u201320). Attention branch network: Learning of attention mechanism for visual explanation. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.01096"},{"key":"ref_63","doi-asserted-by":"crossref","unstructured":"Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., and Batra, D. (2017, January 22\u201329). Grad-cam: Visual explanations from deep networks via gradient-based localization. Proceedings of the IEEE International Conference on Computer Vision, Venice, Italy.","DOI":"10.1109\/ICCV.2017.74"},{"key":"ref_64","doi-asserted-by":"crossref","unstructured":"Srinivas, A., Lin, T.-Y., Parmar, N., Shlens, J., Abbeel, P., and Vaswani, A. (2021, January 20\u201325). Bottleneck Transformers for Visual Recognition. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Nashville, TN, USA.","DOI":"10.1109\/CVPR46437.2021.01625"},{"key":"ref_65","first-page":"448","article-title":"Batch normalization: Accelerating deep network training by reducing internal covariate shift. International conference on machine learning","volume":"37","author":"Ioffe","year":"2015","journal-title":"PMLR"},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s11214-006-9140-8","article-title":"The Rosetta Mission: Flying towards the Origin of the Solar System","volume":"128","author":"Glassmeier","year":"2007","journal-title":"Space Sci. Rev."},{"key":"ref_67","doi-asserted-by":"crossref","unstructured":"Saiki, T., Takei, Y., Fujii, A., Kikuchi, S., Terui, F., Mimasu, Y., Ogawa, N., Ono, G., Yoshikawa, K., and Tanaka, S. (2022). Overview of the Hayabusa2 Asteroid Proximity Operations. Hayabusa2 Asteroid Sample Return Mission, Elsevier.","DOI":"10.1016\/B978-0-323-99731-7.00007-6"},{"key":"ref_68","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."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/24\/6339\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T01:41:30Z","timestamp":1760146890000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/24\/6339"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,12,14]]},"references-count":68,"journal-issue":{"issue":"24","published-online":{"date-parts":[[2022,12]]}},"alternative-id":["rs14246339"],"URL":"https:\/\/doi.org\/10.3390\/rs14246339","relation":{},"ISSN":["2072-4292"],"issn-type":[{"type":"electronic","value":"2072-4292"}],"subject":[],"published":{"date-parts":[[2022,12,14]]}}}