{"title":"多光谱图像的分割算法","authors":"O. V. Nikolaeva","doi":"10.1134/s2070048224700029","DOIUrl":null,"url":null,"abstract":"<h3 data-test=\"abstract-sub-heading\">Abstract</h3><p>An algorithm for the segmentation of multispectral images is presented. First the algorithm performs a preliminary estimate of the number of segments, then the image is divided into segments using a noniterative version of the <i>k</i>-means method. Next, statistical analysis is performed; segment pairs that are realizations of the same random vector are found and merged. The results of testing the algorithm on model and real (HYPERION sensor) data are presented. Partitions of real images, whose segments correspond to various elements of the landscape, are shown.</p>","PeriodicalId":38050,"journal":{"name":"Mathematical Models and Computer Simulations","volume":"6 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Segmentation Algorithm of Multispectral Images\",\"authors\":\"O. V. Nikolaeva\",\"doi\":\"10.1134/s2070048224700029\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<h3 data-test=\\\"abstract-sub-heading\\\">Abstract</h3><p>An algorithm for the segmentation of multispectral images is presented. First the algorithm performs a preliminary estimate of the number of segments, then the image is divided into segments using a noniterative version of the <i>k</i>-means method. Next, statistical analysis is performed; segment pairs that are realizations of the same random vector are found and merged. The results of testing the algorithm on model and real (HYPERION sensor) data are presented. Partitions of real images, whose segments correspond to various elements of the landscape, are shown.</p>\",\"PeriodicalId\":38050,\"journal\":{\"name\":\"Mathematical Models and Computer Simulations\",\"volume\":\"6 1\",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-06-25\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Mathematical Models and Computer Simulations\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1134/s2070048224700029\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"Mathematics\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Mathematical Models and Computer Simulations","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1134/s2070048224700029","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"Mathematics","Score":null,"Total":0}
An algorithm for the segmentation of multispectral images is presented. First the algorithm performs a preliminary estimate of the number of segments, then the image is divided into segments using a noniterative version of the k-means method. Next, statistical analysis is performed; segment pairs that are realizations of the same random vector are found and merged. The results of testing the algorithm on model and real (HYPERION sensor) data are presented. Partitions of real images, whose segments correspond to various elements of the landscape, are shown.
期刊介绍:
Mathematical Models and Computer Simulations is a journal that publishes high-quality and original articles at the forefront of development of mathematical models, numerical methods, computer-assisted studies in science and engineering with the potential for impact across the sciences, and construction of massively parallel codes for supercomputers. The problem-oriented papers are devoted to various problems including industrial mathematics, numerical simulation in multiscale and multiphysics, materials science, chemistry, economics, social, and life sciences.