Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Dense feature pyramid fusion deep network for building segmentation in remote sensing image
It is difficult to achieve detailed segmentation since the building size varies in high-resolution remote sensing images, especially for small buildings. To address these problems, a dense feature pyramid fusion deep network is proposed in this study. First, we built an encoder-decoder structure, and combine attention mechanism and atrous convolution to improve the feature extraction results in the encoder. Second, the pyramid pooling module is selected to extract the multi-scale features from different levels. Finally, dense feature pyramid is adopted in the decoder to fuse multi-level and multi-scale features to obtain the final segmentation results. Experiments on Inria Aerial Image Labeling Dataset show that our method achieves competitive performance compared with other classical semantic segmentation networks.
Dense feature pyramid fusion deep network for building segmentation in remote sensing image
It is difficult to achieve detailed segmentation since the building size varies in high-resolution remote sensing images, especially for small buildings. To address these problems, a dense feature pyramid fusion deep network is proposed in this study. First, we built an encoder-decoder structure, and combine attention mechanism and atrous convolution to improve the feature extraction results in the encoder. Second, the pyramid pooling module is selected to extract the multi-scale features from different levels. Finally, dense feature pyramid is adopted in the decoder to fuse multi-level and multi-scale features to obtain the final segmentation results. Experiments on Inria Aerial Image Labeling Dataset show that our method achieves competitive performance compared with other classical semantic segmentation networks.
Dense feature pyramid fusion deep network for building segmentation in remote sensing image
Tian, Qinglin (Autor:in) / Zhao, Yingjun (Autor:in) / Qin, Kai (Autor:in) / Li, Yao (Autor:in) / Chen, Xuejiao (Autor:in)
Seventh Symposium on Novel Photoelectronic Detection Technology and Applications ; 2020 ; Kunming,China
Proc. SPIE ; 11763
12.03.2021
Aufsatz (Konferenz)
Elektronische Ressource
Englisch
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