{"title":"高分辨率遥感影像中水分提取的耦合共生矩阵/多尺度分割方法","authors":"Xiao Liang, Wenying Hu","doi":"10.1109/GEOINFORMATICS.2018.8557177","DOIUrl":null,"url":null,"abstract":"This study developed a coupled co-occurrence matrix/multi-scale segmentation method to improve extraction precision of water from high-resolution remote sensing images. Two images of Kunming city (subject A & B) were obtained from Quick Bird image gallery, pre-processed by co-occurrence matrix, and then multi-scale segmented based on inherent geometrical and geographical attributes. Water encompassed by the ring roads of the city was extracted via object-oriented information analysis with successfully removal of all shadows. Results showed that water extraction precisions had significantly increased for both subject A (68.6% → 95.2%) and B (63.0% → 92.3%), indicating superior performance of the proposed method in extracting water from complex urban environment.","PeriodicalId":142380,"journal":{"name":"2018 26th International Conference on Geoinformatics","volume":"4 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2018-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"A Coupled Co-Occurrence Matrix/Multi-Scale Segmentation Method to Extract Water from High Resolution Remote Sensing Image\",\"authors\":\"Xiao Liang, Wenying Hu\",\"doi\":\"10.1109/GEOINFORMATICS.2018.8557177\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This study developed a coupled co-occurrence matrix/multi-scale segmentation method to improve extraction precision of water from high-resolution remote sensing images. Two images of Kunming city (subject A & B) were obtained from Quick Bird image gallery, pre-processed by co-occurrence matrix, and then multi-scale segmented based on inherent geometrical and geographical attributes. Water encompassed by the ring roads of the city was extracted via object-oriented information analysis with successfully removal of all shadows. Results showed that water extraction precisions had significantly increased for both subject A (68.6% → 95.2%) and B (63.0% → 92.3%), indicating superior performance of the proposed method in extracting water from complex urban environment.\",\"PeriodicalId\":142380,\"journal\":{\"name\":\"2018 26th International Conference on Geoinformatics\",\"volume\":\"4 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2018-06-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2018 26th International Conference on Geoinformatics\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/GEOINFORMATICS.2018.8557177\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2018 26th International Conference on Geoinformatics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/GEOINFORMATICS.2018.8557177","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
A Coupled Co-Occurrence Matrix/Multi-Scale Segmentation Method to Extract Water from High Resolution Remote Sensing Image
This study developed a coupled co-occurrence matrix/multi-scale segmentation method to improve extraction precision of water from high-resolution remote sensing images. Two images of Kunming city (subject A & B) were obtained from Quick Bird image gallery, pre-processed by co-occurrence matrix, and then multi-scale segmented based on inherent geometrical and geographical attributes. Water encompassed by the ring roads of the city was extracted via object-oriented information analysis with successfully removal of all shadows. Results showed that water extraction precisions had significantly increased for both subject A (68.6% → 95.2%) and B (63.0% → 92.3%), indicating superior performance of the proposed method in extracting water from complex urban environment.