Semantic Segmentation of High-Resolution Remote Sensing Images Using Multiscale Skip Connection Network

doi:10.1109/JSEN.2021.3139629

淡江大學機構典藏 > 工學院 > 人工智慧學系 > 期刊論文 > Item 987654321/124047

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题名:	Semantic Segmentation of High-Resolution Remote Sensing Images Using Multiscale Skip Connection Network
作者:	Ma, Bifang;Chang, Chih-Yung
关键词:	Deep convolutional neural network;high-resolution remote sensing image;multi-scale skip connection;semantic segmentation
日期:	2022-02-15
上传时间:	2023-05-11 12:05:24 (UTC+8)
出版者:	IEEE
摘要:	Semantic segmentation of remote sensing images plays a vital role in land resource management, yield estimation, and economic evaluation. Therefore, this paper proposes a multi-scale skip connection network with the Atrous convolution to deal with the segmentation problems of the multi-modal and multi-scale high-resolution remote sensing images. Firstly, we applied the Atrous convolution in the encoder to enlarge the convolution kernel’s receptive field. Secondly, based on the U-Net network, we merged the light and deep features of different scales by redesigning the skip connection and combining multi-scale features in each U-Net layer. Finally, we applied a pixel-by-pixel classification method and obtained the semantic segmentation results of remote sensing images. The effectiveness of the proposed algorithm is verified. The experimental results show that the mF1 scores are 89.4% and 90.3% on the open dataset of ISPRS Vaihingen and ISPRS Potsdam, respectively, which are better than the state-of-the-art algorithms.
關聯:	IEEE Sensors Journal 22(4), p. 3745-3755
DOI:	10.1109/JSEN.2021.3139629
显示于类别:	[人工智慧學系] 期刊論文

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