{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,7]],"date-time":"2026-10-07T17:52:55Z","timestamp":1791395575232,"version":"4.3.3"},"reference-count":73,"publisher":"MDPI AG","issue":"21","license":[{"start":{"date-parts":[[2021,10,21]],"date-time":"2021-10-21T00:00:00Z","timestamp":1634774400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Sky and ground are two essential semantic components in computer vision, robotics, and remote sensing. The sky and ground segmentation has become increasingly popular. This research proposes a sky and ground segmentation framework for the rover navigation visions by adopting weak supervision and transfer learning technologies. A new sky and ground segmentation neural network (network in U-shaped network (NI-U-Net)) and a conservative annotation method have been proposed. The pre-trained process achieves the best results on a popular open benchmark (the Skyfinder dataset) by evaluating seven metrics compared to the state-of-the-art. These seven metrics achieve 99.232%, 99.211%, 99.221%, 99.104%, 0.0077, 0.0427, and 98.223% on accuracy, precision, recall, dice score (F1), misclassification rate (MCR), root mean squared error (RMSE), and intersection over union (IoU), respectively. The conservative annotation method achieves superior performance with limited manual intervention. The NI-U-Net can operate with 40 frames per second (FPS) to maintain the real-time property. The proposed framework successfully fills the gap between the laboratory results (with rich idea data) and the practical application (in the wild). The achievement can provide essential semantic information (sky and ground) for the rover navigation vision.<\/jats:p>","DOI":"10.3390\/s21216996","type":"journal-article","created":{"date-parts":[[2021,10,21]],"date-time":"2021-10-21T23:27:39Z","timestamp":1634858859000},"page":"6996","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["Sky and Ground Segmentation in the Navigation Visions of the Planetary Rovers"],"prefix":"10.3390","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1828-7663","authenticated-orcid":false,"given":"Boyu","family":"Kuang","sequence":"first","affiliation":[{"name":"Centre for Computational Engineering Sciences (CES), School of Aerospace, Transport and Manufacturing (SATM), Cranfield University, Bedfordshire MK43 0AL, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7839-3949","authenticated-orcid":false,"given":"Zeeshan A.","family":"Rana","sequence":"additional","affiliation":[{"name":"Centre for Computational Engineering Sciences (CES), School of Aerospace, Transport and Manufacturing (SATM), Cranfield University, Bedfordshire MK43 0AL, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2383-5724","authenticated-orcid":false,"given":"Yifan","family":"Zhao","sequence":"additional","affiliation":[{"name":"Centre for Life-Cycle Engineering and Management, School of Aerospace, Transport and Manufacturing (SATM), Cranfield University, Bedfordshire MK43 0AL, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2021,10,21]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"590393","DOI":"10.1155\/2013\/590393","article-title":"Resveratrol Impedes the Stemness, Epithelial-Mesenchymal Transition, and Metabolic Reprogramming of Cancer Stem Cells in Nasopharyngeal Carcinoma through p53 Activation","volume":"2013","author":"Shen","year":"2013","journal-title":"Evid.-Based Complement. Altern. Med."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/2897824.2925942","article-title":"Sky is not the limit","volume":"35","author":"Tsai","year":"2016","journal-title":"ACM Trans. Graph."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"3954","DOI":"10.1109\/JSTARS.2018.2833382","article-title":"DeepUNet: A Deep Fully Convolutional Network for Pixel-Level Sea-Land Segmentation","volume":"11","author":"Li","year":"2018","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/2601097.2601101","article-title":"Transient attributes for high-level understanding and editing of outdoor scenes","volume":"33","author":"Laffont","year":"2014","journal-title":"ACM Trans. Graph."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"2510","DOI":"10.1109\/TPAMI.2016.2640295","article-title":"Two-Class Weather Classification","volume":"39","author":"Lu","year":"2017","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"4457","DOI":"10.1109\/JSTARS.2019.2947281","article-title":"A Novel Stitching Method for Dust and Rock Analysis Based on Yutu Rover Panoramic Imagery","volume":"12","author":"Ye","year":"2019","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"996","DOI":"10.1049\/iet-ipr.2016.0308","article-title":"Haze removal for a single inland waterway image using sky segmentation and dark channel prior","volume":"10","author":"Liu","year":"2016","journal-title":"IET Image Process."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1163","DOI":"10.1049\/iet-ipr.2017.0058","article-title":"Scene-aware image dehazing based on sky-segmented dark channel prior","volume":"11","author":"Xiao","year":"2017","journal-title":"IET Image Process."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Hoiem, D., Efros, A.A., and Hebert, M. (2005, January 17\u201321). Geometric context from a single image. Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV\u201905), Beijing, China.","DOI":"10.1109\/ICCV.2005.107"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"329","DOI":"10.1007\/s11263-012-0574-z","article-title":"Superparsing","volume":"101","author":"Tighe","year":"2013","journal-title":"Int. J. Comput. Vis."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"247","DOI":"10.1109\/LGRS.2016.2637439","article-title":"SeNet: Structured Edge Network for Sea\u2013Land Segmentation","volume":"14","author":"Cheng","year":"2017","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1814","DOI":"10.1109\/LGRS.2019.2912140","article-title":"CloudSegNet: A Deep Network for Nychthemeron Cloud Image Segmentation","volume":"16","author":"Dev","year":"2019","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Krauz, L., Janout, P., Bla\u017eek, M., and P\u00e1ta, P. (2020). Assessing Cloud Segmentation in the Chromacity Diagram of All-Sky Images. Remote Sens., 12.","DOI":"10.3390\/rs12111902"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Li, X., Zheng, H., Han, C., Zheng, W., Chen, H., Jing, Y., and Dong, K. (2021). SFRS-Net: A Cloud-Detection Method Based on Deep Convolutional Neural Networks for GF-1 Remote-Sensing Images. Remote Sens., 13.","DOI":"10.3390\/rs13152910"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"112005","DOI":"10.1016\/j.rse.2020.112005","article-title":"Cloud detection for Landsat imagery by combining the random forest and superpixels extracted via energy-driven sampling segmentation approaches","volume":"248","author":"Wei","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Wr\u00f3\u017cy\u0144ski, R., Pyszny, K., and Sojka, M. (2020). Quantitative Landscape Assessment Using LiDAR and Rendered 360 degree Panoramic Images. Remote Sens., 12.","DOI":"10.3390\/rs12030386"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"M\u00fcller, M.M., Bertrand, O.J.N., Differt, D., and Egelhaaf, M. (2018). The problem of home choice in skyline-based homing. PLoS ONE, 13.","DOI":"10.1371\/journal.pone.0194070"},{"key":"ref_18","first-page":"2476","article-title":"Honeybees use the skyline in orientation","volume":"220","author":"Towne","year":"2017","journal-title":"J. Exp. Biol."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Stone, T., Differt, D., Milford, M., and Webb, B. (2016, January 16\u201321). Skyline-based localisation for aggressively manoeuvring robots using UV sensors and spherical harmonics. Proceedings of the 2016 IEEE International Conference on Robotics and Automation (ICRA), Stockholm, Sweden.","DOI":"10.1109\/ICRA.2016.7487780"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"353","DOI":"10.1007\/s00359-017-1174-8","article-title":"Skyline retention and retroactive interference in the navigating Australian desert ant, Melophorus bagoti","volume":"203","author":"Freas","year":"2017","journal-title":"J. Comp. Physiol. A"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"60","DOI":"10.1108\/AEAT-12-2016-0239","article-title":"Autonomous planetary rover navigation via active SLAM","volume":"91","author":"Li","year":"2018","journal-title":"Aircr. Eng. Aerosp. Technol."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Clark, B.C., Kolb, V.M., Steele, A., House, C.H., Lanza, N.L., Gasda, P.J., VanBommel, S.J., Newsom, H.E., and Mart\u00ednez-Fr\u00edas, J. (2021). Origin of Life on Mars: Suitability and Opportunities. Life, 11.","DOI":"10.3390\/life11060539"},{"key":"ref_23","unstructured":"McGee, T.G., Sengupta, R., and Hedrick, K. (2005, January 18\u201322). Obstacle Detection for Small Autonomous Aircraft Using Sky Segmentation. Proceedings of the 2005 IEEE International Conference on Robotics and Automation, Barcelona, Spain."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"8890","DOI":"10.1109\/ACCESS.2017.2710305","article-title":"Single Image Dehazing via Large Sky Region Segmentation and Multiscale Opening Dark Channel Model","volume":"5","author":"Liu","year":"2017","journal-title":"IEEE Access"},{"key":"ref_25","first-page":"248","article-title":"Supervised Approach to Sky and Ground Classification Using Whiteness-Based Features","volume":"Volume 10633","author":"Beuren","year":"2018","journal-title":"Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), LNAI"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Song, Y., Luo, H., Ma, J., Hui, B., and Chang, Z. (2018). Sky Detection in Hazy Image. Sensors, 18.","DOI":"10.3390\/s18041060"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"231","DOI":"10.1109\/JSTARS.2016.2558474","article-title":"Color-Based Segmentation of Sky\/Cloud Images from Ground-Based Cameras","volume":"10","author":"Dev","year":"2017","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Beuren, A.T., de Souza Britto, A., and Facon, J. (2020, January 19\u201324). Sky\/Ground Segmentation Using Different Approaches. Proceedings of the 2020 International Joint Conference on Neural Networks (IJCNN), Glasgow, UK.","DOI":"10.1109\/IJCNN48605.2020.9206876"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"111716","DOI":"10.1016\/j.rse.2020.111716","article-title":"Deep learning in environmental remote sensing: Achievements and challenges","volume":"241","author":"Yuan","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Gonzalez-Cid, Y., Burguera, A., Bonin-Font, F., and Matamoros, A. (2017, January 19\u201322). Machine learning and deep learning strategies to identify Posidonia meadows in underwater images. Proceedings of the OCEANS 2017-Aberdeen, Aberdeen, UK.","DOI":"10.1109\/OCEANSE.2017.8084991"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Mihail, R.P., Workman, S., Bessinger, Z., and Jacobs, N. (2016, January 7\u201310). Sky segmentation in the wild: An empirical study. Proceedings of the 2016 IEEE Winter Conference on Applications of Computer Vision (WACV), Lake Placid, NY, USA.","DOI":"10.1109\/WACV.2016.7477637"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"3477","DOI":"10.1109\/TIP.2019.2897941","article-title":"An Iterative Spanning Forest Framework for Superpixel Segmentation","volume":"28","author":"Chowdhury","year":"2019","journal-title":"IEEE Trans. Image Process."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"1829","DOI":"10.1109\/ACCESS.2018.2886563","article-title":"Scene-Awareness Based Single Image Dehazing Technique via Automatic Estimation of Sky Area","volume":"7","author":"Fu","year":"2019","journal-title":"IEEE Access"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"La Place, C., Urooj, A., and Borji, A. (2019, January 7\u201311). Segmenting Sky Pixels in Images: Analysis and Comparison. Proceedings of the 2019 IEEE Winter Conference on Applications of Computer Vision (WACV), Waikoloa, HI, USA.","DOI":"10.1109\/WACV.2019.00189"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"100572","DOI":"10.1016\/j.uclim.2019.100572","article-title":"Sky pixel detection in outdoor imagery using an adaptive algorithm and machine learning","volume":"31","author":"Nice","year":"2020","journal-title":"Urban Clim."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Ho\u017cy\u0144, S., and Zalewski, J. (2020). Shoreline Detection and Land Segmentation for Autonomous Surface Vehicle Navigation with the Use of an Optical System. Sensors, 20.","DOI":"10.3390\/s20102799"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"281","DOI":"10.1109\/TSMCB.2008.2002909","article-title":"SVMs modeling for highly imbalanced classification","volume":"39","author":"Tang","year":"2009","journal-title":"IEEE Trans. Syst. Man Cybern. Part B Cybern."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"277","DOI":"10.1145\/357401.357402","article-title":"End-to-end arguments in system design","volume":"2","author":"Saltzer","year":"1984","journal-title":"ACM Trans. Comput. Syst."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"7972","DOI":"10.1109\/TGRS.2019.2917612","article-title":"Supervised Fine-Grained Cloud Detection and Recognition in Whole-Sky Images","volume":"57","author":"Ye","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"362","DOI":"10.5772\/56884","article-title":"Sky Region Detection in a Single Image for Autonomous Ground Robot Navigation","volume":"10","author":"Shen","year":"2013","journal-title":"Int. J. Adv. Robot. Syst."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Ahmad, T., Bebis, G., Nicolescu, M., Nefian, A., and Fong, T. (2015, January 6\u20138). Fusion of edge-less and edge-based approaches for horizon line detection. Proceedings of the 2015 6th International Conference on Information, Intelligence, Systems and Applications (IISA), Corfu, Greece.","DOI":"10.1109\/IISA.2015.7387988"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"238","DOI":"10.1016\/j.neucom.2015.12.126","article-title":"Sky detection by effective context inference","volume":"208","author":"Shang","year":"2016","journal-title":"Neurocomputing"},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Carrio, A., Sampedro, C., Fu, C., Collumeau, J.F., and Campoy, P. (2016, January 7\u201310). A real-time supervised learning approach for sky segmentation onboard unmanned aerial vehicles. Proceedings of the 2016 International Conference on Unmanned Aircraft Systems (ICUAS), Arlington, VA, USA.","DOI":"10.1109\/ICUAS.2016.7502586"},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Chiodini, S., Pertile, M., Debei, S., Bramante, L., Ferrentino, E., Villa, A.G., Musso, I., and Barrera, M. (2017, January 21\u201323). Mars rovers localization by matching local horizon to surface digital elevation models. Proceedings of the 2017 IEEE International Workshop on Metrology for AeroSpace (MetroAeroSpace), Padua, Italy.","DOI":"10.1109\/MetroAeroSpace.2017.7999600"},{"key":"ref_45","unstructured":"Verbickas, R., and Whitehead, A. (2014, January 14\u201315). Sky and Ground Detection Using Convolutional Neural Networks. Proceedings of the International Conference on Machine Vision and Machine Learning, Prague, Czech Republic."},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Cordts, M., Omran, M., Ramos, S., Rehfeld, T., Enzweiler, M., Benenson, R., Franke, U., Roth, S., and Schiele, B. (2016, January 27\u201330). The Cityscapes Dataset for Semantic Urban Scene Understanding. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.350"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1177\/0278364917737153","article-title":"The Katwijk beach planetary rover dataset","volume":"37","author":"Hewitt","year":"2018","journal-title":"Int. J. Robot. Res."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"21981","DOI":"10.1002\/rob.21981","article-title":"Efficient autonomous navigation for planetary rovers with limited resources","volume":"37","author":"Gerdes","year":"2020","journal-title":"J. Field Robot."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"830","DOI":"10.1002\/rob.21943","article-title":"SLAM for autonomous planetary rovers with global localization","volume":"37","author":"Geromichalos","year":"2020","journal-title":"J. Field Robot."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"641","DOI":"10.1177\/0278364920908922","article-title":"The Canadian Planetary Emulation Terrain Energy-Aware Rover Navigation Dataset","volume":"39","author":"Lamarre","year":"2020","journal-title":"Int. J. Robot. Res."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"32","DOI":"10.1016\/j.engappai.2019.08.011","article-title":"Dynamic path planning for reconfigurable rovers using a multi-layered grid","volume":"86","author":"Azkarate","year":"2019","journal-title":"Eng. Appl. Artif. Intell."},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Chiodini, S., Torresin, L., Pertile, M., and Debei, S. (2020, January 22\u201324). Evaluation of 3D CNN Semantic Mapping for Rover Navigation. Proceedings of the 2020 IEEE 7th International Workshop on Metrology for AeroSpace (MetroAeroSpace), Pisa, Italy.","DOI":"10.1109\/MetroAeroSpace48742.2020.9160157"},{"key":"ref_53","first-page":"1","article-title":"CNN Based Detectors on Planetary Environments: A Performance Evaluation","volume":"14","author":"Rubio","year":"2020","journal-title":"Front. Neurorobot."},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Ono, M., Fuchs, T.J., Steffy, A., Maimone, M., and Yen, J. (2015, January 7\u201314). Risk-aware planetary rover operation: Autonomous terrain classification and path planning. Proceedings of the 2015 IEEE Aerospace Conference, Big Sky, MT, USA.","DOI":"10.1109\/AERO.2015.7119022"},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"707","DOI":"10.1177\/0278364911433135","article-title":"The Devon Island rover navigation dataset","volume":"31","author":"Furgale","year":"2012","journal-title":"Int. J. Robot. Res."},{"key":"ref_56","unstructured":"NASA (2021, May 29). NASA Science Mars Exploration Program, Available online: https:\/\/mars.nasa.gov\/mars2020\/multimedia\/raw-images\/."},{"key":"ref_57","first-page":"234","article-title":"U-Net: Convolutional Networks for Biomedical Image Segmentation","volume":"Volume 9351","author":"Ronneberger","year":"2015","journal-title":"Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)"},{"key":"ref_58","first-page":"3","article-title":"Unet++: A nested u-net architecture for medical image segmentation","volume":"Volume 11045","author":"Zhou","year":"2018","journal-title":"Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support. Lecture Notes in Computer Science"},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"Huang, H., Lin, L., Tong, R., Hu, H., Zhang, Q., Iwamoto, Y., Han, X., Chen, Y.W., and Wu, J. (2020, January 4\u20138). Unet 3+: A full-scale connected unet for medical image segmentation. Proceedings of the ICASSP 2020\u20142020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Barcelona, Spain.","DOI":"10.1109\/ICASSP40776.2020.9053405"},{"key":"ref_60","unstructured":"Lin, M., Chen, Q., and Yan, S. (2013). Network in Network. arXiv."},{"key":"ref_61","doi-asserted-by":"crossref","unstructured":"Xiao, J., Li, J., Yuan, Q., and Zhang, L. (2021). A Dual-UNet with Multistage Details Injection for Hyperspectral Image Fusion. IEEE Trans. Geosci. Remote Sens., 1\u201313.","DOI":"10.1109\/TGRS.2021.3101848"},{"key":"ref_62","doi-asserted-by":"crossref","unstructured":"Ciecholewski, M., and Kassja\u0144ski, M. (2021). Computational Methods for Liver Vessel Segmentation in Medical Imaging: A Review. Sensors, 21.","DOI":"10.3390\/s21062027"},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"504","DOI":"10.1126\/science.1127647","article-title":"Reducing the Dimensionality of Data with Neural Networks","volume":"313","author":"Hinton","year":"2006","journal-title":"Science"},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"60956","DOI":"10.1109\/ACCESS.2018.2875412","article-title":"Deep Semantic Segmentation in an AUV for Online Posidonia Oceanica Meadows Identification","volume":"6","year":"2018","journal-title":"IEEE Access"},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"e3","DOI":"10.23915\/distill.00003","article-title":"Deconvolution and Checkerboard Artifacts","volume":"1","author":"Odena","year":"2016","journal-title":"Distill"},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"157","DOI":"10.1007\/s11263-007-0090-8","article-title":"LabelMe: A database and web-based tool for image annotation","volume":"77","author":"Russell","year":"2008","journal-title":"Int. J. Comput. Vis."},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"781","DOI":"10.1007\/978-981-15-5971-6_83","article-title":"A Survey on Transfer Learning","volume":"Volume 194","author":"Panigrahi","year":"2021","journal-title":"Smart Innovation, Systems and Technologies"},{"key":"ref_68","unstructured":"Frosst, N., and Hinton, G. (2017). Distilling a Neural Network into a Soft Decision Tree. arXiv."},{"key":"ref_69","unstructured":"Keskar, N.S., Nocedal, J., Tang, P.T.P., Mudigere, D., and Smelyanskiy, M. (2017). On large-batch training for deep learning: Generalization gap and sharp minima. arXiv."},{"key":"ref_70","unstructured":"(2021, July 23). Zhixuhao Unet. Available online: https:\/\/github.com\/zhixuhao\/unet."},{"key":"ref_71","unstructured":"(2021, July 23). Mulesial Pytorch-UNet. Available online: https:\/\/github.com\/milesial\/Pytorch-UNet."},{"key":"ref_72","doi-asserted-by":"crossref","unstructured":"Qin, X., He, S., Zhang, Z., Dehghan, M., and Jagersand, M. (2018, January 12\u201315). ByLabel: A boundary based semi-automatic image annotation tool. Proceedings of the 2018 IEEE Winter Conference on Applications of Computer Vision (WACV), Lake Tahoe, NV, USA.","DOI":"10.1109\/WACV.2018.00200"},{"key":"ref_73","unstructured":"NASA (2021, October 19). MARS Exploration Rovers, Available online: https:\/\/mars.nasa.gov\/mer\/multimedia\/raw\/."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/21\/21\/6996\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T07:20:32Z","timestamp":1760167232000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/21\/21\/6996"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,10,21]]},"references-count":73,"journal-issue":{"issue":"21","published-online":{"date-parts":[[2021,11]]}},"alternative-id":["s21216996"],"URL":"https:\/\/doi.org\/10.3390\/s21216996","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,10,21]]}}}