Semantic Segmentation of High-Resolution Remote Sensing Images Using Multiscale Skip Connection Network
學年 110
學期 2
出版(發表)日期 2022-02-15
作品名稱 Semantic Segmentation of High-Resolution Remote Sensing Images Using Multiscale Skip Connection Network
作品名稱(其他語言)
著者 Bifang Ma; Chih-Yung Chang
單位
出版者
著錄名稱、卷期、頁數 IEEE Sensors Journal 22(4), p. 3745-3755
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.
關鍵字 Deep convolutional neural network;high-resolution remote sensing image;multi-scale skip connection;semantic segmentation
語言 en
ISSN 1558-1748; 1530-437X
期刊性質 國外
收錄於 SCI
產學合作
通訊作者
審稿制度
國別 USA
公開徵稿
出版型式 ,電子版,紙本
相關連結

機構典藏連結 ( http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/124047 )