{"title":"一种基于区域分割和小波变换的图像融合方法","authors":"P. Sun, L. Deng","doi":"10.1109/Geoinformatics.2012.6270260","DOIUrl":null,"url":null,"abstract":"An image fusion method based on region segmentation and wavelet transform for multi-scale remote sensing image fusion is proposed. Firstly, the multiscale decomposition of source images is carried out with the wavelet transform. Then region segmentation based on area standard deviation is done for the low-frequency coefficients, the low-frequency coefficients is decomposed two parts: target information and background information. The target information is fused with larger absolute value operator, and the background information is fused with gray error value operator. The high-frequency coefficients are fused with image definition. Finally the fused coefficients are reconstructed to obtain the fusion image. Using this method and comparison with several traditional methods, the results show that the fused image by the presented algorithm can not only hold spectrum information of the multispectral image, but also improve the high resolution of the fusion image.","PeriodicalId":259976,"journal":{"name":"2012 20th International Conference on Geoinformatics","volume":"18 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2012-06-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":"{\"title\":\"An image fusion method based on region segmentation and wavelet transform\",\"authors\":\"P. Sun, L. Deng\",\"doi\":\"10.1109/Geoinformatics.2012.6270260\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"An image fusion method based on region segmentation and wavelet transform for multi-scale remote sensing image fusion is proposed. Firstly, the multiscale decomposition of source images is carried out with the wavelet transform. Then region segmentation based on area standard deviation is done for the low-frequency coefficients, the low-frequency coefficients is decomposed two parts: target information and background information. The target information is fused with larger absolute value operator, and the background information is fused with gray error value operator. The high-frequency coefficients are fused with image definition. Finally the fused coefficients are reconstructed to obtain the fusion image. Using this method and comparison with several traditional methods, the results show that the fused image by the presented algorithm can not only hold spectrum information of the multispectral image, but also improve the high resolution of the fusion image.\",\"PeriodicalId\":259976,\"journal\":{\"name\":\"2012 20th International Conference on Geoinformatics\",\"volume\":\"18 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2012-06-15\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"4\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2012 20th International Conference on Geoinformatics\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/Geoinformatics.2012.6270260\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2012 20th International Conference on Geoinformatics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/Geoinformatics.2012.6270260","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
An image fusion method based on region segmentation and wavelet transform
An image fusion method based on region segmentation and wavelet transform for multi-scale remote sensing image fusion is proposed. Firstly, the multiscale decomposition of source images is carried out with the wavelet transform. Then region segmentation based on area standard deviation is done for the low-frequency coefficients, the low-frequency coefficients is decomposed two parts: target information and background information. The target information is fused with larger absolute value operator, and the background information is fused with gray error value operator. The high-frequency coefficients are fused with image definition. Finally the fused coefficients are reconstructed to obtain the fusion image. Using this method and comparison with several traditional methods, the results show that the fused image by the presented algorithm can not only hold spectrum information of the multispectral image, but also improve the high resolution of the fusion image.