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

IF 1.4 4区地球科学 Q3 IMAGING SCIENCE & PHOTOGRAPHIC TECHNOLOGY

Remote Sensing Letters Pub Date : 2024-01-25 DOI:10.1080/2150704x.2024.2305624

Lakshmi Vijayan, Akshara Preethy Byju

引用次数: 0

摘要

自动建筑物提取对于监测受灾害影响的建筑物和城市规划等若干地理空间应用来说势在必行。现有的基于深度学习（DL）的建筑物抽取技术可用于对建筑物进行...

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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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...

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来源期刊

Remote Sensing Letters REMOTE SENSING-IMAGING SCIENCE & PHOTOGRAPHIC TECHNOLOGY

CiteScore

4.10

自引率

4.30%

发文量

审稿时长

6-12 weeks

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.