{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,6]],"date-time":"2025-12-06T05:00:16Z","timestamp":1764997216675,"version":"build-2065373602"},"reference-count":65,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2019,3,6]],"date-time":"2019-03-06T00:00:00Z","timestamp":1551830400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Image classification is one of the most common methods of information extraction from satellite images. In this paper, a novel algorithm for image classification based on gravity theory was developed, which was called \u201chomogeneity distance classification algorithm (HDCA)\u201d. The proposed HDCA used texture and spectral information for classifying images in two iterative supplementary computing stages: (1) merging, (2) traveling and escaping operators. The HDCA was equipped by a new concept of distance, the weighted Manhattan distance (WMD). Moreover, an improved gravitational search algorithm (IGSA) was applied for selecting features and determining optimal feature space scale in HDCA. In the case of multispectral satellite image classification, the proposed method was compared with two well-known classification methods, Maximum Likelihood classifier (MLC) and Support Vector Machine (SVM). The results of the comparison indicated that overall accuracy values for HDCA, MLC, and SVM are 95.99, 93.15, and 95.00, respectively. Furthermore, the proposed HDCA method was also used for classifying hyperspectral reference datasets (Indian Pines, Salinas and Salinas-A scene). The classification results indicated substantial improvement over previous algorithms and studies by 2% in Indian Pines dataset, 0.7% in the Salinas dataset and 1.2% in the Salinas-A scene. These experimental results demonstrate that the proposed algorithm can classify both multispectral and hyperspectral remote sensing images with reliable accuracy because this algorithm uses the WMD in the classification process and the IGSA to select automatically optimal features for image classification based on spectral and texture information.<\/jats:p>","DOI":"10.3390\/rs11050546","type":"journal-article","created":{"date-parts":[[2019,3,7]],"date-time":"2019-03-07T10:52:22Z","timestamp":1551955942000},"page":"546","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["Homogeneity Distance Classification Algorithm (HDCA): A Novel Algorithm for Satellite Image Classification"],"prefix":"10.3390","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3060-9162","authenticated-orcid":false,"given":"Mohammad Karimi","family":"Firozjaei","sequence":"first","affiliation":[{"name":"Department of Remote Sensing and GIS, University of Tehran, Tehran 1417853933, Iran"}]},{"given":"Iman","family":"Daryaei","sequence":"additional","affiliation":[{"name":"Department of Civil and Surveying Engineering, Graduate University of Advanced Technology, Kerman 7616914111, Iran"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4272-2859","authenticated-orcid":false,"given":"Amir","family":"Sedighi","sequence":"additional","affiliation":[{"name":"Department of Remote Sensing and GIS, University of Tehran, Tehran 1417853933, Iran"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2498-0934","authenticated-orcid":false,"given":"Qihao","family":"Weng","sequence":"additional","affiliation":[{"name":"College of the Environment &amp; Ecology, Xiamen University, South Xiangan Road, Xiangan District, Xiamen 361102, Fujian, China"},{"name":"Center for Urban and Environmental Chang, Department of Earth and Environmental Systems, Indiana State University, Terre Haute, IN 47809, USA"}]},{"given":"Seyed Kazem","family":"Alavipanah","sequence":"additional","affiliation":[{"name":"Department of Remote Sensing and GIS, University of Tehran, Tehran 1417853933, Iran"}]}],"member":"1968","published-online":{"date-parts":[[2019,3,6]]},"reference":[{"unstructured":"Schowengerdt, R.A. (2012). Techniques for Image Processing and Classifications in Remote Sensing, Academic Press.","key":"ref_1"},{"key":"ref_2","first-page":"67","article-title":"Rapid maximum likelihood classification","volume":"57","author":"Bolstad","year":"1991","journal-title":"Photogramm. Eng. Remote Sens."},{"doi-asserted-by":"crossref","unstructured":"Bishop, C., and Bishop, C.M. (1995). Neural Networks for Pattern Recognition, Oxford University Press.","key":"ref_3","DOI":"10.1093\/oso\/9780198538493.001.0001"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"273","DOI":"10.1007\/BF00994018","article-title":"Support-vector networks","volume":"20","author":"Cortes","year":"1995","journal-title":"Mach. Learn."},{"unstructured":"Freund, Y., and Mason, L. (1999). The alternating decision tree learning algorithm. icml, Morgan Kaufmann Publishers Inc.","key":"ref_5"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1291","DOI":"10.1016\/S0031-3203(02)00121-8","article-title":"Attribute bagging: Improving accuracy of classifier ensembles by using random feature subsets","volume":"36","author":"Bryll","year":"2003","journal-title":"Pattern Recognit."},{"unstructured":"Weinberger, K.Q., Blitzer, J., and Saul, L.K. (2006). Distance metric learning for large margin nearest neighbor classification. Advances in Neural Information Processing Systems, Neural Information Processing Systems Foundation, Inc.","key":"ref_7"},{"unstructured":"Manning, C.D., Raghavan, P., and Schutze, H. (2008). Introduction to Information Retrieval, Cambridge University Press. Chapter 20.","key":"ref_8"},{"doi-asserted-by":"crossref","unstructured":"Makantasis, K., Doulamis, A.D., Doulamis, N.D., and Nikitakis, A. (2018). Tensor-based classification models for hyperspectral data analysis. IEEE Trans. Geosci. Remote Sens., 1\u201315.","key":"ref_9","DOI":"10.1109\/TGRS.2018.2845450"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"4192","DOI":"10.1016\/j.patcog.2012.04.033","article-title":"Higher rank support tensor machines for visual recognition","volume":"45","author":"Kotsia","year":"2012","journal-title":"Pattern Recognit."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1322","DOI":"10.1016\/j.engappai.2012.10.002","article-title":"A stochastic gravitational approach to feature based color image segmentation","volume":"26","author":"Rashedi","year":"2013","journal-title":"Eng. Appl. Artif. Intell."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"129","DOI":"10.1007\/s10994-005-1505-9","article-title":"Combined svm-based feature selection and classification","volume":"61","author":"Neumann","year":"2005","journal-title":"Mach. Learn."},{"unstructured":"Xia, J. (2014). Multiple Classifier Systems for the Classification of Hyperspectral Data. [Ph.D. Thesis, Universit\u00e9 de Grenoble].","key":"ref_13"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"19","DOI":"10.1016\/j.aeolia.2018.09.001","article-title":"Effect of environmental policies in combating aeolian desertification over sejzy plain of iran","volume":"35","author":"Moghaddam","year":"2018","journal-title":"Aeolian Res."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"47","DOI":"10.1016\/j.swevo.2012.02.003","article-title":"A combined approach for clustering based on k-means and gravitational search algorithms","volume":"6","author":"Hatamlou","year":"2012","journal-title":"Swarm Evol. Comput."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"256","DOI":"10.1016\/j.neucom.2015.01.008","article-title":"Using gravitational search algorithm in prototype generation for nearest neighbor classification","volume":"157","author":"Rezaei","year":"2015","journal-title":"Neurocomputing"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"40","DOI":"10.1016\/j.knosys.2010.07.003","article-title":"An effective feature selection method for hyperspectral image classification based on genetic algorithm and support vector machine","volume":"24","author":"Li","year":"2011","journal-title":"Knowl.-Based Syst."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"187","DOI":"10.1016\/j.aca.2003.12.032","article-title":"An ant colony approach for clustering","volume":"509","author":"Shelokar","year":"2004","journal-title":"Anal. Chim. Acta"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"433","DOI":"10.1109\/3477.764879","article-title":"Genetic k-means algorithm","volume":"29","author":"Krishna","year":"1999","journal-title":"IEEE Trans. Syst. Man Cybern. Part B (Cybern.)"},{"doi-asserted-by":"crossref","unstructured":"Qinand, A., and Suganthan, P.N. (2004, January 23\u201326). Kernel neural gas algorithms with application to cluster analysis. Proceedings of the 17th International Conference on Pattern Recognition, Cambridge, UK.","key":"ref_20","DOI":"10.1109\/ICPR.2004.1333848"},{"doi-asserted-by":"crossref","unstructured":"Omran, M.G., Engelbrecht, A.P., and Salman, A. (2004). Image classification using particle swarm optimization. Recent Advances in Simulated Evolution and Learning, World Scientific.","key":"ref_21","DOI":"10.1142\/9789812561794_0019"},{"doi-asserted-by":"crossref","unstructured":"Luo, Y., Zou, J., Yao, C., Zhao, X., Li, T., and Bai, G. (2018, January 9\u201310). Hsi-cnn: A novel convolution neural network for hyperspectral image. Proceedings of the 2018 International Conference on Audio, Language and Image Processing (ICALIP), Prague, Czech Republic.","key":"ref_22","DOI":"10.1109\/ICALIP.2018.8455251"},{"doi-asserted-by":"crossref","unstructured":"Gao, Q., Lim, S., and Jia, X. (2018). Hyperspectral image classification using convolutional neural networks and multiple feature learning. Remote Sens., 10.","key":"ref_23","DOI":"10.3390\/rs10020299"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"6232","DOI":"10.1109\/TGRS.2016.2584107","article-title":"Deep feature extraction and classification of hyperspectral images based on convolutional neural networks","volume":"54","author":"Chen","year":"2016","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"4544","DOI":"10.1109\/TGRS.2016.2543748","article-title":"Spectral\u2013spatial feature extraction for hyperspectral image classification: A dimension reduction and deep learning approach","volume":"54","author":"Zhao","year":"2016","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"468","DOI":"10.1080\/2150704X.2015.1047045","article-title":"Spectral\u2013spatial classification of hyperspectral images using deep convolutional neural networks","volume":"6","author":"Yue","year":"2015","journal-title":"Remote Sens. Lett."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"1970","DOI":"10.1109\/LGRS.2016.2619354","article-title":"Deep learning with attribute profiles for hyperspectral image classification","volume":"13","author":"Aptoula","year":"2016","journal-title":"IEEE Geosci. Remote Sens."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"3368","DOI":"10.1080\/2150704X.2015.1062157","article-title":"On combining multiscale deep learning features for the classification of hyperspectral remote sensing imagery","volume":"36","author":"Zhao","year":"2015","journal-title":"Int. J. Remote Sens"},{"doi-asserted-by":"crossref","unstructured":"Schutz, B. (2003). Gravity from the Ground Up: An Introductory Guide to Gravity and General Relativity, Cambridge University Press.","key":"ref_29","DOI":"10.1017\/CBO9780511807800"},{"unstructured":"Halliday, D., Resnick, R., and Walker, J. (1993). Fundamentals of Physics, Wiley and Sons.","key":"ref_30"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"151","DOI":"10.1016\/0031-3203(77)90013-9","article-title":"Gravitational clustering","volume":"9","author":"Wright","year":"1977","journal-title":"Pattern Recognit."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"989","DOI":"10.1117\/1.601932","article-title":"Segmentation of color images based on the gravitational clustering concept","volume":"37","author":"Lai","year":"1998","journal-title":"Opt. Eng."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"1149","DOI":"10.1016\/S0031-3203(98)00143-5","article-title":"Gravitational clustering: A new approach based on the spatial distribution of the points","volume":"32","author":"Kundu","year":"1999","journal-title":"Pattern Recognit."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"2256","DOI":"10.1016\/j.patcog.2005.03.005","article-title":"A statistics-based approach to control the quality of subclusters in incremental gravitational clustering","volume":"38","author":"Chen","year":"2005","journal-title":"Pattern Recognit."},{"doi-asserted-by":"crossref","unstructured":"Long, T., and Jin, L.-W. (2006). A new simplified gravitational clustering method for multi-prototype learning based on minimum classification error training. Advances in Machine Vision, Image Processing, and Pattern Analysis, Springer.","key":"ref_35","DOI":"10.1007\/11821045_18"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.engappai.2016.11.003","article-title":"A novel data clustering algorithm based on modified gravitational search algorithm","volume":"61","author":"Han","year":"2017","journal-title":"Eng. Appl. Artif. Intell."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"2232","DOI":"10.1016\/j.ins.2009.03.004","article-title":"Gsa: A gravitational search algorithm","volume":"179","author":"Rashedi","year":"2009","journal-title":"Inf. Sci."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"114","DOI":"10.1016\/j.engappai.2014.07.016","article-title":"Ggsa: A grouping gravitational search algorithm for data clustering","volume":"36","author":"Dowlatshahi","year":"2014","journal-title":"Eng. Appl. Artif. Intell."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"2880","DOI":"10.1109\/TGRS.2010.2041784","article-title":"Sensitivity of support vector machines to random feature selection in classification of hyperspectral data","volume":"48","author":"Waske","year":"2010","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"doi-asserted-by":"crossref","unstructured":"Hu, W., Huang, Y., Wei, L., Zhang, F., and Li, H. (2015). Deep convolutional neural networks for hyperspectral image classification. J. Sens., 2015.","key":"ref_40","DOI":"10.1155\/2015\/258619"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"844","DOI":"10.1109\/TGRS.2016.2616355","article-title":"Hyperspectral image classification using deep pixel-pair features","volume":"55","author":"Li","year":"2017","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"5122","DOI":"10.1109\/TGRS.2013.2286953","article-title":"Remotely sensed image classification using sparse representations of morphological attribute profiles","volume":"52","author":"Song","year":"2014","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"111","DOI":"10.1080\/22797254.2017.1279821","article-title":"Spectral\u2013spatial feature extraction using orthogonal linear discriminant analysis for classification of hyperspectral data","volume":"50","author":"Shahdoosti","year":"2017","journal-title":"Eur. J. Remote Sens."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"307","DOI":"10.1016\/j.procs.2015.08.025","article-title":"Comparative analysis of scattering and random features in hyperspectral image classification","volume":"58","author":"Haridas","year":"2015","journal-title":"Procedia Comput. Sci."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"2370","DOI":"10.1109\/JSTARS.2015.2434997","article-title":"Spectral\u2013spatial classification of hyperspectral image based on low-rank decomposition","volume":"8","author":"Xu","year":"2015","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"085095","DOI":"10.1117\/1.JRS.8.085095","article-title":"Dynamic classifier selection using spectral-spatial information for hyperspectral image classification","volume":"8","author":"Su","year":"2014","journal-title":"J. Appl. Remote Sens."},{"doi-asserted-by":"crossref","unstructured":"Ran, L., Zhang, Y., Wei, W., and Zhang, Q. (2017). A hyperspectral image classification framework with spatial pixel pair features. Sensors, 17.","key":"ref_47","DOI":"10.3390\/s17102421"},{"unstructured":"Iliopoulos, A.-S., Liu, T., and Sun, X. (arXiv, 2015). Hyperspectral image classification and clutter detection via multiple structural embeddings and dimension reductions, arXiv.","key":"ref_48"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"760","DOI":"10.1109\/LGRS.2011.2109367","article-title":"Unmixing prior to supervised classification of remotely sensed hyperspectral images","volume":"8","author":"Zortea","year":"2011","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"288","DOI":"10.1109\/LGRS.2013.2256336","article-title":"Spectral\u2013spatial classification of multispectral images using kernel feature space representation","volume":"11","author":"Marpu","year":"2014","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"93","DOI":"10.1109\/LGRS.2005.857031","article-title":"Composite kernels for hyperspectral image classification","volume":"3","year":"2006","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"2066","DOI":"10.1109\/JSTARS.2013.2292901","article-title":"Classification of hyperspectral data using an adaboostsvm technique applied on band clusters","volume":"7","author":"Ramzi","year":"2014","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"doi-asserted-by":"crossref","unstructured":"Thanh Noi, P., and Kappas, M. (2018). Comparison of random forest, k-nearest neighbor, and support vector machine classifiers for land cover classification using sentinel-2 imagery. Sensors, 18.","key":"ref_53","DOI":"10.3390\/s18010018"},{"doi-asserted-by":"crossref","unstructured":"Firozjaei, M.K., Kiavarz, M., Nematollahi, O., Karimpour Reihan, M., and Alavipanah, S.K. (2019). An evaluation of energy balance parameters, and the relations between topographical and biophysical characteristics using the mountainous surface energy balance algorithm for land (sebal). Int. J. Remote Sens., 1\u201331.","key":"ref_54","DOI":"10.1080\/01431161.2019.1579385"},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"155","DOI":"10.1016\/j.ecolind.2018.03.052","article-title":"Monitoring and forecasting heat island intensity through multi-temporal image analysis and cellular automata-markov chain modelling: A case of babol city, iran","volume":"91","author":"Firozjaei","year":"2018","journal-title":"Ecol. Indic."},{"unstructured":"Panah, S., Mogaddam, M.K., and Firozjaei, M.K. (2017). Monitoring spatiotemporal changes of heat island in babol city due to land use changes. Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci., 42.","key":"ref_56"},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"576","DOI":"10.1080\/15481603.2018.1548080","article-title":"Statistical analysis of surface urban heat island intensity variations: A case study of Babol city, Iran","volume":"56","author":"Weng","year":"2019","journal-title":"GISci. Remote Sens."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"2440","DOI":"10.3390\/rs3112440","article-title":"An object-based classification of mangroves using a hybrid decision tree\u2014Support vector machine approach","volume":"3","author":"Heumann","year":"2011","journal-title":"Remote Sens."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"153","DOI":"10.3390\/rs70100153","article-title":"Comparing machine learning classifiers for object-based land cover classification using very high resolution imagery","volume":"7","author":"Qian","year":"2015","journal-title":"Remote Sens."},{"key":"ref_60","first-page":"123","article-title":"Monitoring and predicting spatial-temporal changes heat island in babol city due to urban sprawl and land use changes","volume":"5","year":"2017","journal-title":"J. Geospat. Inf. Technol."},{"key":"ref_61","first-page":"89","article-title":"Quantifying the degree-of-freedom, degree-of-sprawl and degree-of-goodness of urban growth tehran and factors affecting it using remote sensing and statistical analyzes","volume":"7","author":"Mijani","year":"2018","journal-title":"J. Geomat. Sci. Technol."},{"doi-asserted-by":"crossref","unstructured":"Haralick, R.M., and Shanmugam, K. (1973). Textural features for image classification. IEEE Trans. Syst. Man Cybern., 610\u2013621.","key":"ref_62","DOI":"10.1109\/TSMC.1973.4309314"},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"466","DOI":"10.1109\/TGRS.2004.841417","article-title":"Dimensionality reduction and classification of hyperspectral image data using sequences of extended morphological transformations","volume":"43","author":"Plaza","year":"2005","journal-title":"IEEE Trans. Geosci Remote Sens."},{"doi-asserted-by":"crossref","unstructured":"Haridas, N., Sowmya, V., and Soman, K. (2015, January 2\u20134). Hyperspectral image classification using random kitchen sink and regularized least squares. Proceedings of the 2015 International Conference on Communications and Signal Processing (ICCSP), Melmaruvathur, India.","key":"ref_64","DOI":"10.1109\/ICCSP.2015.7322801"},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"5293","DOI":"10.1109\/TGRS.2017.2705073","article-title":"Bass net: Band-adaptive spectral-spatial feature learning neural network for hyperspectral image classification","volume":"55","author":"Santara","year":"2017","journal-title":"IEEE Trans. Geosci Remote Sens."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/11\/5\/546\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T12:36:46Z","timestamp":1760186206000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/11\/5\/546"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,3,6]]},"references-count":65,"journal-issue":{"issue":"5","published-online":{"date-parts":[[2019,3]]}},"alternative-id":["rs11050546"],"URL":"https:\/\/doi.org\/10.3390\/rs11050546","relation":{},"ISSN":["2072-4292"],"issn-type":[{"type":"electronic","value":"2072-4292"}],"subject":[],"published":{"date-parts":[[2019,3,6]]}}}