{"title":"基于注意机制的遥感图像分割模型","authors":"Hanting Wang","doi":"10.1109/aemcse55572.2022.00086","DOIUrl":null,"url":null,"abstract":"Remote sensing images are often very large in size, which is difficult to put into GPU for training. Previous work proposed models of global and local branches. On the basis of this model, we add attention mechanism to make feature integration more complete. The results show that our method works well.","PeriodicalId":309096,"journal":{"name":"2022 5th International Conference on Advanced Electronic Materials, Computers and Software Engineering (AEMCSE)","volume":"32 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Remote sensing image segmentation model based on attention mechanism\",\"authors\":\"Hanting Wang\",\"doi\":\"10.1109/aemcse55572.2022.00086\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Remote sensing images are often very large in size, which is difficult to put into GPU for training. Previous work proposed models of global and local branches. On the basis of this model, we add attention mechanism to make feature integration more complete. The results show that our method works well.\",\"PeriodicalId\":309096,\"journal\":{\"name\":\"2022 5th International Conference on Advanced Electronic Materials, Computers and Software Engineering (AEMCSE)\",\"volume\":\"32 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-04-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2022 5th International Conference on Advanced Electronic Materials, Computers and Software Engineering (AEMCSE)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/aemcse55572.2022.00086\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2022 5th International Conference on Advanced Electronic Materials, Computers and Software Engineering (AEMCSE)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/aemcse55572.2022.00086","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Remote sensing image segmentation model based on attention mechanism
Remote sensing images are often very large in size, which is difficult to put into GPU for training. Previous work proposed models of global and local branches. On the basis of this model, we add attention mechanism to make feature integration more complete. The results show that our method works well.