{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,24]],"date-time":"2026-04-24T01:01:55Z","timestamp":1776992515623,"version":"3.51.4"},"reference-count":37,"publisher":"MDPI AG","issue":"13","license":[{"start":{"date-parts":[[2021,6,28]],"date-time":"2021-06-28T00:00:00Z","timestamp":1624838400000},"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>Remote sensing technologies have been widely used in the contexts of land cover and land use. The image classification algorithms used in remote sensing are of paramount importance since the reliability of the result from remote sensing depends heavily on the classification accuracy. Parametric classifiers based on traditional statistics have successfully been used in remote sensing classification, but the accuracy is greatly impacted and rather constrained by the statistical distribution of the sensing data. To eliminate those constraints, new variants of support vector machine (SVM) are introduced. In this paper, we propose and implement land use classification based on improved SVM-enabled radial basis function (RBF) and SVM-Linear for image sensing. The proposed variants are applied for the cross-validation to determine how the optimization of parameters can affect the accuracy. The accuracy assessment includes both training and test sets, addressing the problems of overfitting and underfitting. Furthermore, it is not trivial to determine the generalization problem merely based on a training dataset. Thus, the improved SVM-RBF and SVM-Linear also demonstrate the outstanding generalization performance. The proposed SVM-RBF and SVM-Linear variants have been compared with the traditional algorithms (Maximum Likelihood Classifier (MLC) and Minimum Distance Classifier (MDC)), which are highly compatible with remote sensing images. Furthermore, the MLC and MDC are mathematically modeled and characterized with new features. Also, we compared the proposed improved SVM-RBF and SVM-Linear with the current state-of-the-art algorithms. Based on the results, it is confirmed that proposed variants have higher overall accuracy, reliability, and fault-tolerance than traditional as well as latest state-of-the-art algorithms.<\/jats:p>","DOI":"10.3390\/s21134431","type":"journal-article","created":{"date-parts":[[2021,6,28]],"date-time":"2021-06-28T13:39:22Z","timestamp":1624887562000},"page":"4431","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":81,"title":["Improved Support Vector Machine Enabled Radial Basis Function and Linear Variants for Remote Sensing Image Classification"],"prefix":"10.3390","volume":"21","author":[{"given":"Abdul","family":"Razaque","sequence":"first","affiliation":[{"name":"Department of Computer Engineering and Information Security, International Information Technology University, Almaty 050040, Kazakhstan"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5315-1898","authenticated-orcid":false,"given":"Mohamed","family":"Ben Haj Frej","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, University of Bridgeport, Bridgeport, CT 06604, USA"}]},{"given":"Muder","family":"Almi\u2019ani","sequence":"additional","affiliation":[{"name":"Gulf University for Science and Technology, Hawally 32093, Kuwait"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2181-7143","authenticated-orcid":false,"given":"Munif","family":"Alotaibi","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Shaqra University, Shaqra 15526, Saudi Arabia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9956-2027","authenticated-orcid":false,"given":"Bandar","family":"Alotaibi","sequence":"additional","affiliation":[{"name":"Department of Information Technology, University of Tabuk, Tabuk 47731, Saudi Arabia"},{"name":"Sensor Networks and Cellular Systems (SNCS) Research Center, University of Tabuk, Tabuk 47731, Saudi Arabia"}]}],"member":"1968","published-online":{"date-parts":[[2021,6,28]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Barmpoutis, P., Papaioannou, P., Dimitropoulos, K., and Grammalidis, N. (2020). A Review on Early Forest Fire Detection Systems Using Optical Remote Sensing. Sensors, 20.","DOI":"10.3390\/s20226442"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Van Natijne, A.L., Lindenbergh, R.C., and Bogaard, T.A. (2020). Machine learning: New potential for local and regional deep-seated landslide nowcasting. Sensors, 20.","DOI":"10.5194\/egusphere-egu2020-19515"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"528","DOI":"10.1016\/j.eng.2019.10.015","article-title":"Remote sensing and precision agriculture technologies for crop disease detection and management with a practical application example","volume":"6","author":"Yang","year":"2020","journal-title":"Engineering"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Li, W., Xiang, M., and Liang, X. (2021). MDCwFB: A Multilevel Dense Connection Network with Feedback Connections for Pansharpening. Remote Sens., 13.","DOI":"10.3390\/rs13112218"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Talukdar, S., Singha, P., Mahato, S., Pal, S., Liou, Y.-A., and Rahman, A. (2020). Land-Use Land-Cover Classification by Machine Learning Classifiers for Satellite Observations\u2014A Review. Remote Sens., 12.","DOI":"10.3390\/rs12071135"},{"key":"ref_6","first-page":"24","article-title":"Mapping of Krau Wildlife Reserve (KWR) protected area using Landsat 8 and supervised classification algorithms","volume":"10","author":"Shaharum","year":"2018","journal-title":"Remote Sens. Appl. Soc. Environ."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"107635","DOI":"10.1016\/j.patcog.2020.107635","article-title":"Hyperspectral remote sensing image classification based on tighter random projection with minimal intra-class variance algorithm","volume":"111","author":"Zhao","year":"2021","journal-title":"Pattern Recognit."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"71","DOI":"10.1016\/j.culher.2020.01.012","article-title":"Historical documents dating using multispectral imaging and ordinal classification","volume":"45","author":"Rahiche","year":"2020","journal-title":"J. Cult. Herit."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"506","DOI":"10.1016\/S2095-3119(18)62016-7","article-title":"Research advances of SAR remote sensing for agriculture applications: A review","volume":"18","author":"Liu","year":"2019","journal-title":"J. Integr. Agric."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"244","DOI":"10.1016\/j.rse.2019.01.002","article-title":"Geometric accuracy of remote sensing images over oceans: The use of global offshore platforms","volume":"222","author":"Liu","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_11","first-page":"973","article-title":"A novel hybrid machine learning approach for change detection in remote sensing images","volume":"23","author":"Pati","year":"2020","journal-title":"Eng. Sci. Technol. Int. J."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"202","DOI":"10.1016\/j.ress.2018.03.020","article-title":"Adaptive SVM-based real-time quality assessment for primer-sealer dispensing process of sunroof assembly line","volume":"184","author":"Oh","year":"2019","journal-title":"Reliab. Eng. Syst. Saf."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"32","DOI":"10.1016\/j.patrec.2020.08.028","article-title":"Object-oriented remote sensing image information extraction method based on multi-classifier combination and deep learning algorithm","volume":"141","author":"Tan","year":"2021","journal-title":"Pattern Recognit. Lett."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"92","DOI":"10.1016\/j.is.2019.03.005","article-title":"Binary classification in unstructured space with hypergraph case-based reasoning","volume":"85","author":"Quemy","year":"2019","journal-title":"Inf. Syst."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"185","DOI":"10.1016\/j.ecolind.2018.12.050","article-title":"Ecological risk assessment of cities on the Tibetan Plateau based on land use\/land cover changes\u2014Case study of Delingha City","volume":"101","author":"Jin","year":"2019","journal-title":"Ecol. Indic."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Glowacz, A. (2021). Ventilation Diagnosis of Angle Grinder Using Thermal Imaging. Sensors, 21.","DOI":"10.3390\/s21082853"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Wu, Q., Feng, D., Cao, C., Zeng, X., Feng, Z., Wu, J., and Huang, Z. (2021). Improved Mask R-CNN for Aircraft Detection in Remote Sensing Images. Sensors, 21.","DOI":"10.3390\/s21082618"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"75","DOI":"10.1016\/j.asoc.2017.11.045","article-title":"Computational intelligence in optical remote sensing image processing","volume":"64","author":"Zhong","year":"2018","journal-title":"Appl. Soft Comput."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"233","DOI":"10.1016\/j.jvcir.2018.11.004","article-title":"Hyperspectral remote sensing image change detection based on tensor and deep learning","volume":"58","author":"Huang","year":"2019","journal-title":"J. Vis. Commun. Image Represent."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"132","DOI":"10.1016\/j.cageo.2019.06.005","article-title":"Remote sensing image classification based on semi-supervised adaptive interval type-2 fuzzy c-means algorithm","volume":"131","author":"Xu","year":"2019","journal-title":"Comput. Geosci."},{"key":"ref_21","first-page":"e00971","article-title":"Land use\/cover classification in an arid desert-oasis mosaic landscape of China using remote sensed imagery: Performance assessment of four machine learning algorithms","volume":"22","author":"Ge","year":"2020","journal-title":"Glob. Ecol. Conserv."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"90","DOI":"10.1016\/j.isprsjprs.2019.07.002","article-title":"Addressing overfitting on point cloud classification using Atrous XCRF","volume":"155","author":"Arief","year":"2019","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_23","first-page":"8012568","article-title":"Advanced support vector machine-(ASVM-) based detection for distributed denial of service (DDoS) attack on software defined networking (SDN)","volume":"2019","author":"Kamolphiwong","year":"2019","journal-title":"J. Comput. Networks Commun."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Chen, Y., Wei, Y., Wang, Q., Chen, F., Lu, C., and Lei, S. (2020). Mapping Post-Earthquake Landslide Susceptibility: A U-Net Like Approach. Remote Sens., 12.","DOI":"10.3390\/rs12172767"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"114864","DOI":"10.1016\/j.geoderma.2020.114864","article-title":"Mean spectral reflectance from bare soil pixels along a Landsat-TM time series to increase both the prediction accuracy of soil clay content and mapping coverage","volume":"388","author":"Gasmi","year":"2021","journal-title":"Geoderma"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"112049","DOI":"10.1016\/j.rse.2020.112049","article-title":"Seasonal evolution of L-band SAR backscatter over landfast Arctic sea ice","volume":"251","author":"Mahmud","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"105175","DOI":"10.1016\/j.ocecoaman.2020.105175","article-title":"Spatio-temporal dynamics of the fish community associated with artisanal fisheries activities within a key marine protected area of the Southwest Atlantic (Uruguay)","volume":"190","author":"Segura","year":"2020","journal-title":"Ocean. Coast. Manag."},{"key":"ref_28","first-page":"535","article-title":"Weed detection in canola fields using maximum likelihood classification and deep convolutional neural network","volume":"7","author":"Asad","year":"2019","journal-title":"Inf. Process. Agric."},{"key":"ref_29","first-page":"1","article-title":"Computer vision technology in agricultural automation\u2014A review","volume":"7","author":"Tian","year":"2020","journal-title":"Inf. Process. Agric."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"66","DOI":"10.1002\/ep.12934","article-title":"A Hybrid clustering and classification technique for forecasting short-term energy consumption","volume":"38","author":"Torabi","year":"2019","journal-title":"Environ. Prog. Sustain. Energy"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"173","DOI":"10.1016\/j.rse.2018.11.014","article-title":"Joint Deep Learning for land cover and land use classification","volume":"221","author":"Zhang","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"233","DOI":"10.1016\/j.cherd.2019.09.026","article-title":"Fault diagnosis of nonlinear systems using recurrent neural networks","volume":"153","author":"Shahnazari","year":"2020","journal-title":"Chem. Eng. Res. Des."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"580","DOI":"10.1016\/j.patrec.2020.09.006","article-title":"Nonparametric maximum likelihood estimation using neural networks","volume":"138","author":"Huynh","year":"2020","journal-title":"Pattern Recognit. Lett."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"115293","DOI":"10.1016\/j.eswa.2021.115293","article-title":"A novel extreme learning machine based kNN classification method for dealing with big data","volume":"2021","author":"Shokrzade","year":"2021","journal-title":"Expert Syst. Appl."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Zhang, L., Wu, J., Fan, Y., Gao, H., and Shao, Y. (2020). An efficient building extraction method from high spatial resolution remote sensing images based on improved mask R-CNN. Sensors, 20.","DOI":"10.3390\/s20051465"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"63","DOI":"10.1016\/j.rse.2019.01.008","article-title":"Geostatistical characterization of local accuracies in remotely sensed land cover change categorization with complexly configured reference samples","volume":"223","author":"Zhang","year":"2019","journal-title":"Remote. Sens. Environ."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"447","DOI":"10.1016\/j.compeleceng.2017.05.035","article-title":"An adaptive decision based kriging interpolation algorithm for the removal of high density salt and pepper noise in images","volume":"70","author":"Varatharajan","year":"2018","journal-title":"Comput. Electr. Eng."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/21\/13\/4431\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T06:26:05Z","timestamp":1760163965000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/21\/13\/4431"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,6,28]]},"references-count":37,"journal-issue":{"issue":"13","published-online":{"date-parts":[[2021,7]]}},"alternative-id":["s21134431"],"URL":"https:\/\/doi.org\/10.3390\/s21134431","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,6,28]]}}}