RSI-Net: Two-Stream Deep Neural Network for Remote Sensing Images-Based Semantic Segmentation

doi:10.1109/ACCESS.2022.3163535

	RSI-Net: Two-Stream Deep Neural Network for Remote Sensing Images-Based Semantic Segmentation
	Shuang He 1,2; Xia Lu 1,2; Jason Gu 3; Haitong Tang 1,2; Qin Yu 4; Kaiyue Liu 1,2; Haozhou Ding 5; Chunqi Chang 6,7; Nizhuan Wang1,2,4,8
	2022
发表期刊	IEEE ACCESS (IF:3.4[JCR-2023],3.7[5-Year])
ISSN	2169-3536
卷号	10
发表状态	已发表
DOI	10.1109/ACCESS.2022.3163535
摘要	For semantic segmentation of remote sensing images (RSI), trade-off between representation power and location accuracy is quite important. How to get the trade-off effectively is an open question, where current approaches of utilizing very deep models result in complex models with large memory consumption. In contrast to previous work that utilizes dilated convolutions or deep models, we propose a novel two-stream deep neural network for semantic segmentation of RSI (RSI-Net) to obtain improved performance through modeling and propagating spatial contextual structure effectively and a decoding scheme with image-level and graph-level combination. The first component explicitly models correlations between adjacent land covers and conduct flexible convolution on arbitrarily irregular image regions by using graph convolutional network, while densely connected atrous convolution network (DenseAtrousCNet) with multi-scale atrous convolution can expand the receptive fields and obtain image global information. Extensive experiments are implemented on the Vaihingen, Potsdam and Gaofen RSI datasets, where the comparison results demonstrate the superior performance of RSI-Net in terms of overall accuracy (91.83%, 93.31% and 93.67% on three datasets, respectively), F1 score (90.3%, 91.49% and 89.35% on three datasets, respectively) and kappa coefficient (89.46%, 90.46% and 90.37% on three datasets, respectively) when compared with six state-of-the-art RSI semantic segmentation methods.
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收录类别	SCI ; SCIE ; EI
来源库	IEEE
引用统计	正在获取...
文献类型	期刊论文
条目标识符	https://kms.shanghaitech.edu.cn/handle/2MSLDSTB/176031
专题	生物医学工程学院信息科学与技术学院
作者单位	1.Jiangsu Key Laboratory of Marine Bioresources and Environment/Jiangsu Key Laboratory of Marine Biotechnology/Co-Innovation Center of Jiangsu Marine Bio-Industry Technology, Jiangsu Ocean University, Lianyungang, China 2.School of Geomatics and Marine Information, Jiangsu Ocean University, Lianyungang, China 3.Department of Electrical and Computer Engineering, Dalhousie University, Halifax, NS, Canada 4.School of Computer Engineering, Jiangsu Ocean University, Lianyungang, China 5.School of Computer and Software, Nanjing University of Information Science and Technology, Nanjing, China 6.School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen, China 7.Peng Cheng Laboratory, Shenzhen, China 8.School of Biomedical Engineering, ShanghaiTech University, Shanghai, China
推荐引用方式 GB/T 7714	Shuang He,Xia Lu,Jason Gu,et al. RSI-Net: Two-Stream Deep Neural Network for Remote Sensing Images-Based Semantic Segmentation[J]. IEEE ACCESS,2022,10.
APA	Shuang He.,Xia Lu.,Jason Gu.,Haitong Tang.,Qin Yu.,...&Nizhuan Wang.(2022).RSI-Net: Two-Stream Deep Neural Network for Remote Sensing Images-Based Semantic Segmentation.IEEE ACCESS,10.
MLA	Shuang He,et al."RSI-Net: Two-Stream Deep Neural Network for Remote Sensing Images-Based Semantic Segmentation".IEEE ACCESS 10(2022).