Land Use Land Cover map segmentation using Remote Sensing: A Case study of Ajoy river watershed, India

Image segmentation in land cover regions which are overlapping in satellite imagery, is one crucial challenge. To detect true belonging of one pixel becomes a challenging problem while classifying mixed pixels in overlapping regions. In current work, we propose one new approach for image segmentatio...

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Autores principales:	Mahata Kalyan, Das Rajib, Das Subhasish, Sarkar Anasua
Formato:	article
Lenguaje:	EN
Publicado:	De Gruyter 2020
Materias:	remote sensing pixel classification land use land cover map segmentation k-means clustering cellular automata catchment analysis 68-xx 92-xx 62-xx Science Q Electronic computers. Computer science QA75.5-76.95
Acceso en línea:	https://doaj.org/article/9b60486e4a134863b20574cee0044437
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id	oai:doaj.org-article:9b60486e4a134863b20574cee0044437
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spelling	oai:doaj.org-article:9b60486e4a134863b20574cee00444372021-12-05T14:10:51ZLand Use Land Cover map segmentation using Remote Sensing: A Case study of Ajoy river watershed, India2191-026X10.1515/jisys-2019-0155https://doaj.org/article/9b60486e4a134863b20574cee00444372020-09-01T00:00:00Zhttps://doi.org/10.1515/jisys-2019-0155https://doaj.org/toc/2191-026XImage segmentation in land cover regions which are overlapping in satellite imagery, is one crucial challenge. To detect true belonging of one pixel becomes a challenging problem while classifying mixed pixels in overlapping regions. In current work, we propose one new approach for image segmentation using a hybrid algorithm of K-Means and Cellular Automata algorithms. This newly implemented unsupervised model can detect cluster groups using hybrid 2-Dimensional Cellular-Automata model based on K-Means segmentation approach. This approach detects different land use land cover areas in satellite imagery by existing K-Means algorithm. Since it is a discrete dynamical system, cellular automaton realizes uniform interconnecting cells containing states. In the second stage of current model, we experiment with a 2-dimensional cellular automata to rank allocations of pixels among different land-cover regions. The method is experimented on the watershed area of Ajoy river (India) and Salinas (California) data set with true class labels using two internal and four external validity indices. The segmented areas are then compared with existing FCM, DBSCAN and K-Means methods and verified with the ground truth. The statistical analysis results also show the superiority of the new method.Mahata KalyanDas RajibDas SubhasishSarkar AnasuaDe Gruyterarticleremote sensingpixel classificationland use land cover map segmentationk-means clusteringcellular automatacatchment analysis68-xx92-xx62-xxScienceQElectronic computers. Computer scienceQA75.5-76.95ENJournal of Intelligent Systems, Vol 30, Iss 1, Pp 273-286 (2020)
institution	DOAJ
collection	DOAJ
language	EN
topic	remote sensing pixel classification land use land cover map segmentation k-means clustering cellular automata catchment analysis 68-xx 92-xx 62-xx Science Q Electronic computers. Computer science QA75.5-76.95
spellingShingle	remote sensing pixel classification land use land cover map segmentation k-means clustering cellular automata catchment analysis 68-xx 92-xx 62-xx Science Q Electronic computers. Computer science QA75.5-76.95 Mahata Kalyan Das Rajib Das Subhasish Sarkar Anasua Land Use Land Cover map segmentation using Remote Sensing: A Case study of Ajoy river watershed, India
description	Image segmentation in land cover regions which are overlapping in satellite imagery, is one crucial challenge. To detect true belonging of one pixel becomes a challenging problem while classifying mixed pixels in overlapping regions. In current work, we propose one new approach for image segmentation using a hybrid algorithm of K-Means and Cellular Automata algorithms. This newly implemented unsupervised model can detect cluster groups using hybrid 2-Dimensional Cellular-Automata model based on K-Means segmentation approach. This approach detects different land use land cover areas in satellite imagery by existing K-Means algorithm. Since it is a discrete dynamical system, cellular automaton realizes uniform interconnecting cells containing states. In the second stage of current model, we experiment with a 2-dimensional cellular automata to rank allocations of pixels among different land-cover regions. The method is experimented on the watershed area of Ajoy river (India) and Salinas (California) data set with true class labels using two internal and four external validity indices. The segmented areas are then compared with existing FCM, DBSCAN and K-Means methods and verified with the ground truth. The statistical analysis results also show the superiority of the new method.
format	article
author	Mahata Kalyan Das Rajib Das Subhasish Sarkar Anasua
author_facet	Mahata Kalyan Das Rajib Das Subhasish Sarkar Anasua
author_sort	Mahata Kalyan
title	Land Use Land Cover map segmentation using Remote Sensing: A Case study of Ajoy river watershed, India
title_short	Land Use Land Cover map segmentation using Remote Sensing: A Case study of Ajoy river watershed, India
title_full	Land Use Land Cover map segmentation using Remote Sensing: A Case study of Ajoy river watershed, India
title_fullStr	Land Use Land Cover map segmentation using Remote Sensing: A Case study of Ajoy river watershed, India
title_full_unstemmed	Land Use Land Cover map segmentation using Remote Sensing: A Case study of Ajoy river watershed, India
title_sort	land use land cover map segmentation using remote sensing: a case study of ajoy river watershed, india
publisher	De Gruyter
publishDate	2020
url	https://doaj.org/article/9b60486e4a134863b20574cee0044437
work_keys_str_mv	AT mahatakalyan landuselandcovermapsegmentationusingremotesensingacasestudyofajoyriverwatershedindia AT dasrajib landuselandcovermapsegmentationusingremotesensingacasestudyofajoyriverwatershedindia AT dassubhasish landuselandcovermapsegmentationusingremotesensingacasestudyofajoyriverwatershedindia AT sarkaranasua landuselandcovermapsegmentationusingremotesensingacasestudyofajoyriverwatershedindia
_version_	1718371678791139328

Land Use Land Cover map segmentation using Remote Sensing: A Case study of Ajoy river watershed, India

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