{"title":"PolSAR图像分割-先进的统计建模与简单的特征提取","authors":"A. Doulgeris, T. Eltoft","doi":"10.1109/IGARSS.2014.6946601","DOIUrl":null,"url":null,"abstract":"In recent years, we have presented many algorithms for polarimetric SAR image segmentation that show the continually improving developments in the field. However, there are two distinct and divergent approaches - one using highly flexible textured models for the covariance matrix statistics (such as the Wishart, K-Wishart, and U-distribution), and the other using simple features extracted from such data (the Extended Polarimetric Feature Space method). In this study we will present a summary and comparison of both approaches and discuss the pros and cons for each with respect to image segmentation applications.","PeriodicalId":385645,"journal":{"name":"2014 IEEE Geoscience and Remote Sensing Symposium","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2014-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"9","resultStr":"{\"title\":\"PolSAR image segmentation — Advanced statistical modelling versus simple feature extraction\",\"authors\":\"A. Doulgeris, T. Eltoft\",\"doi\":\"10.1109/IGARSS.2014.6946601\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In recent years, we have presented many algorithms for polarimetric SAR image segmentation that show the continually improving developments in the field. However, there are two distinct and divergent approaches - one using highly flexible textured models for the covariance matrix statistics (such as the Wishart, K-Wishart, and U-distribution), and the other using simple features extracted from such data (the Extended Polarimetric Feature Space method). In this study we will present a summary and comparison of both approaches and discuss the pros and cons for each with respect to image segmentation applications.\",\"PeriodicalId\":385645,\"journal\":{\"name\":\"2014 IEEE Geoscience and Remote Sensing Symposium\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2014-11-06\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"9\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2014 IEEE Geoscience and Remote Sensing Symposium\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/IGARSS.2014.6946601\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2014 IEEE Geoscience and Remote Sensing Symposium","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IGARSS.2014.6946601","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
In recent years, we have presented many algorithms for polarimetric SAR image segmentation that show the continually improving developments in the field. However, there are two distinct and divergent approaches - one using highly flexible textured models for the covariance matrix statistics (such as the Wishart, K-Wishart, and U-distribution), and the other using simple features extracted from such data (the Extended Polarimetric Feature Space method). In this study we will present a summary and comparison of both approaches and discuss the pros and cons for each with respect to image segmentation applications.