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Terrain classification can help rovers to select a safe terrain to traverse and avoid sinking and\/or damaging the vehicle. Mars terrains are often classified using visual methods. However, the accuracy of terrain classification has been less than 90% in read operations. A high-accuracy vision-based method for Mars terrain classification is presented in this paper. By analyzing Mars terrain characteristics, novel image features, including multiscale gray gradient-grade features, multiscale edges strength-grade features, multiscale frequency-domain mean amplitude features, multiscale spectrum symmetry features, and multiscale spectrum amplitude-moment features, are proposed that are specifically targeted for terrain classification. Three classifiers, K-nearest neighbor (KNN), support vector machine (SVM), and random forests (RF), are adopted to classify the terrain using the proposed features. The Mars image dataset MSLNet that was collected by the Mars Science Laboratory (MSL, Curiosity) rover is used to conduct terrain classification experiments. The resolution of Mars images in the dataset is 256 \u00d7 256. Experimental results indicate that the RF classifies Mars terrain at the highest level of accuracy of 94.66%.<\/jats:p>","DOI":"10.3390\/e24091304","type":"journal-article","created":{"date-parts":[[2022,9,15]],"date-time":"2022-09-15T22:11:10Z","timestamp":1663279870000},"page":"1304","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":16,"title":["Highly Accurate Visual Method of Mars Terrain Classification for Rovers Based on Novel Image Features"],"prefix":"10.3390","volume":"24","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3556-1575","authenticated-orcid":false,"given":"Fengtian","family":"Lv","sequence":"first","affiliation":[{"name":"State Key Laboratory of Robotics and System, Harbin Institute of Technology, Harbin 150001, China"},{"name":"State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China"},{"name":"Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang 110169, China"},{"name":"Key Laboratory of Marine Robotics, Shenyang 110169, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nan","family":"Li","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Robotics and System, Harbin Institute of Technology, Harbin 150001, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chuankai","family":"Liu","sequence":"additional","affiliation":[{"name":"Beijing Aerospace Control Center Key Laboratory on the Technology of Space Flight Dynamics, Beijing 100190, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Haibo","family":"Gao","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Robotics and System, Harbin Institute of Technology, Harbin 150001, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Liang","family":"Ding","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Robotics and System, Harbin Institute of Technology, Harbin 150001, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zongquan","family":"Deng","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Robotics and System, Harbin Institute of Technology, Harbin 150001, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Guangjun","family":"Liu","sequence":"additional","affiliation":[{"name":"Department of Aerospace Engineering, Ryerson University, Toronto, ON M5B2K3, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,9,15]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"46","DOI":"10.1017\/S1473550421000380","article-title":"Mars: New insights and unresolved questions\u2014Corrigendum","volume":"21","author":"Changela","year":"2022","journal-title":"Int. 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Astron."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"767","DOI":"10.1017\/S0263574708004360","article-title":"A study of visual and tactile terrain classification and classifier fusion for planetary exploration rovers","volume":"26","author":"Halatci","year":"2008","journal-title":"Robotica"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"445","DOI":"10.1002\/rob.21408","article-title":"Self-supervised terrain classification for planetary surface exploration rovers","volume":"29","author":"Brooks","year":"2012","journal-title":"J. Field Robot."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Shirkhodaie, A., and Rababaah, H. (2007, January 11\u201312). Visual detection, recognition, and classification of surface-buried UXO based on soft-computing decision fusion. Proceedings of the SPIE 6553, Detection and Remediation Technologies for Mines and Minelike Targets XII, Orlando, FL, USA.","DOI":"10.1117\/12.719776"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"207","DOI":"10.5772\/7229","article-title":"A combination of terrain prediction and correction for search and rescue robot autonomous navigation","volume":"6","author":"Guo","year":"2009","journal-title":"Int. J. Adv. Robot. Syst."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Selvathai, T., Varadhan, J., and Ramesh, S. (2017, January 23\u201324). Road and off road terrain classification for autonomous ground vehicle. Proceedings of the 2017 International Conference on Information Communication and Embedded Systems, Chennai, India.","DOI":"10.1109\/ICICES.2017.8070724"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"20","DOI":"10.1002\/rob.21512","article-title":"A self-learning framework for statistical ground classification using radar and monocular vision","volume":"32","author":"Milella","year":"2015","journal-title":"J. Field Robot."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Zhang, H., Dai, X., Sun, F., and Yuan, J. (2016, January 27\u201329). Terrain classification in field environment based on Random Forest for the mobile robot. Proceedings of the 35th Chinese Control Conference, Chengdu, China.","DOI":"10.1109\/ChiCC.2016.7554310"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Shang, C., Barnes, D., and Shen, Q. (2009, January 15\u201318). Taking Fuzzy-Rough Application to Mars: Fuzzy-Rough Feature Selection for Mars Terrain Image Classification. Proceedings of the 12th International Conference on Rough Sets, Fuzzy Sets, Data Mining and Granular Computing, Delhi, India.","DOI":"10.1007\/978-3-642-10646-0_25"},{"key":"ref_14","unstructured":"Shang, C., Barnes, D., and Shen, Q. (December, January 30). Effective Feature Selection for Mars McMurdo Terrain Image Classification. Proceedings of the Ninth International Conference on Intelligent Systems Design and Applications, Pisa, Italy."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"202","DOI":"10.1016\/j.cviu.2012.12.002","article-title":"Fuzzy-rough feature selection aided support vector machines for mars image classification","volume":"117","author":"Shang","year":"2013","journal-title":"Comput. Vis. Image Underst."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"814","DOI":"10.1109\/LRA.2016.2525040","article-title":"Autonomous terrain classification with co-and self-training approach","volume":"1","author":"Otsu","year":"2016","journal-title":"IEEE Robot. Autom. Lett."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"026002","DOI":"10.1117\/1.JRS.11.026002","article-title":"Terrain classification of polarimetric synthetic aperture radar imagery based on polarimetric features and ensemble learning","volume":"11","author":"Huang","year":"2017","journal-title":"J. Appl. Remote Sens."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Winkens, C., Kobelt, V., and Paulus, D. (2017, January 22\u201324). Robust features for snapshot hyperspectral terrain-classification. Proceedings of the International Conference on Computer Analysis of Images and Patterns, Ystad, Sweden.","DOI":"10.1007\/978-3-319-64689-3_2"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Khan, Y.N., Komma, P., and Zell, A. (2011, January 6\u201313). High resolution visual terrain classification for outdoor robots. Proceedings of the 2011 IEEE International Conference on Computer Vision Workshops, Barcelona, Spain.","DOI":"10.1109\/ICCVW.2011.6130362"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Khan, Y.N., Komma, P., Bohlmann, K., and Zell, A. (2011, January 11\u201315). Grid-based visual terrain classification for outdoor robots using local features. Proceedings of the 2011 IEEE Symposium on Computational Intelligence in Vehicles and Transportation Systems Proceedings, Paris, France.","DOI":"10.1109\/CIVTS.2011.5949534"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Laible, S., Khan, Y.N., Bohlmann, K., and Zell, A. (2012, January 26\u201328). 3D lidar-and camera-based terrain classification under different lighting conditions. Proceedings of the Autonomous Mobile Systems 2012, Stuttgart, Germany.","DOI":"10.1007\/978-3-642-32217-4_3"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Witus, G., Karlsen, R., and Hunt, S. (2009, January 30). Sequential learning for robot vision terrain classification. Proceedings of the SPIE 7332, Unmanned Systems Technology XI, Orlando, FL, USA.","DOI":"10.1117\/12.819473"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"954","DOI":"10.1016\/j.robot.2011.06.015","article-title":"Terrain surface classification with a control mode update rule using a 2D laser stripe-based structured light sensor","volume":"59","author":"Lu","year":"2011","journal-title":"Robot. Auton. Syst."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Liu, F., Ma, X., Li, X., Song, R., Tian, G., and Li, Y. (2017, January 20\u201322). Terrain recognition for outdoor mobile robots. Proceedings of the 2017 Chinese Automation Congress, Jinan, China.","DOI":"10.1109\/CAC.2017.8243527"},{"key":"ref_25","unstructured":"(2022, September 07). Mars Surface Image (Curiosity Rover) Labeled Data Set. Available online: https:\/\/zenodo.org\/record\/1049137#.XXcCBfmsejg."},{"key":"ref_26","unstructured":"Zhou, Z.H. (2016). Machine Learing, Tsinghua University Press."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Wu, H., Zhang, W., Li, B., Sun, Y., Duan, D., and Chen, P. (2019, January 5\u20137). Visual Terrain Classification Methods for Mobile Robots Using Hybrid Coding Architecture. Proceedings of the 2019 IEEE 4th International Conference on Image, Vision and Computing, Xiamen, China.","DOI":"10.1109\/ICIVC47709.2019.8981092"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Wu, H., Liu, B., Sun, W., Zhang, W., and Sun, J. (2016). Hierarchical Coding Vectors for Scene Level Land-Use Classification. Remote Sens., 8.","DOI":"10.3390\/rs8050436"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"222","DOI":"10.1007\/s11263-013-0636-x","article-title":"Image classification with the fisher vector: Theory and practice","volume":"105","author":"Jorge","year":"2013","journal-title":"Int. J. Comput. 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