{"title":"EMAFF-Net：用于从 VHR 遥感图像中提取建筑物的增强型多尺度注意特征融合网络","authors":"Lakshmi Vijayan, Akshara Preethy Byju","doi":"10.1080/2150704x.2024.2305624","DOIUrl":null,"url":null,"abstract":"Automated building extraction is imperative for several geospatial applications such as monitoring disaster-affected buildings and urban planning. Existing deep learning (DL)-based building extract...","PeriodicalId":49132,"journal":{"name":"Remote Sensing Letters","volume":null,"pages":null},"PeriodicalIF":1.4000,"publicationDate":"2024-01-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"EMAFF-Net: an enhanced multi-scale attentive feature fusion network for building extraction from VHR remote sensing images\",\"authors\":\"Lakshmi Vijayan, Akshara Preethy Byju\",\"doi\":\"10.1080/2150704x.2024.2305624\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Automated building extraction is imperative for several geospatial applications such as monitoring disaster-affected buildings and urban planning. Existing deep learning (DL)-based building extract...\",\"PeriodicalId\":49132,\"journal\":{\"name\":\"Remote Sensing Letters\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":1.4000,\"publicationDate\":\"2024-01-25\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Remote Sensing Letters\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://doi.org/10.1080/2150704x.2024.2305624\",\"RegionNum\":4,\"RegionCategory\":\"地球科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"IMAGING SCIENCE & PHOTOGRAPHIC TECHNOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Remote Sensing Letters","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.1080/2150704x.2024.2305624","RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"IMAGING SCIENCE & PHOTOGRAPHIC TECHNOLOGY","Score":null,"Total":0}
EMAFF-Net: an enhanced multi-scale attentive feature fusion network for building extraction from VHR remote sensing images
Automated building extraction is imperative for several geospatial applications such as monitoring disaster-affected buildings and urban planning. Existing deep learning (DL)-based building extract...
期刊介绍：
Remote Sensing Letters is a peer-reviewed international journal committed to the rapid publication of articles advancing the science and technology of remote sensing as well as its applications. The journal originates from a successful section, of the same name, contained in the International Journal of Remote Sensing from 1983 –2009. Articles may address any aspect of remote sensing of relevance to the journal’s readership, including – but not limited to – developments in sensor technology, advances in image processing and Earth-orientated applications, whether terrestrial, oceanic or atmospheric. Articles should make a positive impact on the subject by either contributing new and original information or through provision of theoretical, methodological or commentary material that acts to strengthen the subject.
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