P. Tchimev, Naoya Moritani, G. Georgiev, I. Valova
{"title":"地理图像分析的神经网络方法","authors":"P. Tchimev, Naoya Moritani, G. Georgiev, I. Valova","doi":"10.1109/IAI.2000.839571","DOIUrl":null,"url":null,"abstract":"We have developed a method based on the precise pixel-to-pixel matching between two images. This is done by automatic generation of displacement vectors, carrying the information of differences between the two images. For generating a layer of vectors defining the information of displacement we use a neural network with self-learning architecture. The proposed algorithm perform successful mapping, which can be quantitatively measured as 90% correct recognition as demonstrated by the results.","PeriodicalId":224112,"journal":{"name":"4th IEEE Southwest Symposium on Image Analysis and Interpretation","volume":"22 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2000-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"A neural network approach to geographic image analysis\",\"authors\":\"P. Tchimev, Naoya Moritani, G. Georgiev, I. Valova\",\"doi\":\"10.1109/IAI.2000.839571\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"We have developed a method based on the precise pixel-to-pixel matching between two images. This is done by automatic generation of displacement vectors, carrying the information of differences between the two images. For generating a layer of vectors defining the information of displacement we use a neural network with self-learning architecture. The proposed algorithm perform successful mapping, which can be quantitatively measured as 90% correct recognition as demonstrated by the results.\",\"PeriodicalId\":224112,\"journal\":{\"name\":\"4th IEEE Southwest Symposium on Image Analysis and Interpretation\",\"volume\":\"22 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2000-04-02\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"4th IEEE Southwest Symposium on Image Analysis and Interpretation\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/IAI.2000.839571\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"4th IEEE Southwest Symposium on Image Analysis and Interpretation","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IAI.2000.839571","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
A neural network approach to geographic image analysis
We have developed a method based on the precise pixel-to-pixel matching between two images. This is done by automatic generation of displacement vectors, carrying the information of differences between the two images. For generating a layer of vectors defining the information of displacement we use a neural network with self-learning architecture. The proposed algorithm perform successful mapping, which can be quantitatively measured as 90% correct recognition as demonstrated by the results.
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