A method for information extraction of buildings from remote sensing images based on hybrid attention mechanism and Deeplabv3+
LIU Chenchen1(), GE Xiaosan1(), WU Yongbin1,2, YU Haikun3, ZHANG Beibei3
1. School of Surveying and Land Information Engineering, Henan Polytechnic University, Jiaozuo 454003, China 2. Henan Surveying and Mapping Geographic Information Technology Center, Zhengzhou 450003, China 3. Henan Remote Sensing Institute, Zhengzhou 450003, China
Extracting information about buildings from a large and complex set of remote sensing images has always been a hot research topic in the intelligent applications of remote sensing. To address issues such as inaccurate information extraction of buildings and the tendency to ignore small buildings within a complex environment in remote sensing images, this study proposed the SC-deep network-a semantic segmentation algorithm for remote sensing images based on a hybrid attention mechanism and Deeplabv3+. Utilizing an encoder-decoder structure, this network employs a backbone residual attention network to extract deep- and shallow-layer features. Meanwhile, this network aggregates the spatial and channel information weights in remote sensing images using a dilated space pyramid pool module and a channel-space attention module. These allow for effectively utilizing the multi-scale information of building structures in remote sensing images, thereby reducing the loss of image details during training. The experimental results indicate that the proposed method outperforms other mainstream segmentation networks on the Aerial imagery dataset. Overall, this method can effectively identify and extract the edges of complex buildings and small structures, exhibiting superior building extraction performance.
刘晨晨, 葛小三, 武永斌, 余海坤, 张蓓蓓. 基于混合注意力机制和Deeplabv3+的遥感影像建筑物提取方法[J]. 自然资源遥感, 2025, 37(1): 31-37.
LIU Chenchen, GE Xiaosan, WU Yongbin, YU Haikun, ZHANG Beibei. A method for information extraction of buildings from remote sensing images based on hybrid attention mechanism and Deeplabv3+. Remote Sensing for Natural Resources, 2025, 37(1): 31-37.
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