Symmetrical Dense-Shortcut Deep Fully Convolutional Networks for Semantic Segmentation of Very-High-Resolution Remote Sensing Images

DOI码：10.1109/jstars.2018.2810320

发表刊物：IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING

关键字：Convolutional neural networks (CNN),deep learning (DL),fully convolutional networks (FCN),remote sensing,SDFCN,semantic segmentation

摘要：Semantic segmentation has emerged as a mainstream method in very-high-resolution remote sensing land-use/land-cover applications. In this paper, we first review the state-of-the-art semantic segmentation models in both computer vision and remote sensing fields. Subsequently, we introduce two semantic segmentation frameworks: SNFCN and SDFCN, both of which contain deep fully convolutional networks with shortcut blocks. We adopt an overlay strategy as the postprocessing method. Based on our frameworks, we conducted experiments on two online ISPRS datasets: Vaihingen and Potsdam. The results indicate that our frameworks achieve higher overall accuracy than the classic FCN-8s and Seg-Net models. In addition, our postprocessing method can increase the overall accuracy by about 1%-2% and help to eliminate "salt and pepper" phenomena and block effects.

合写作者：Qing Wang,Fan Dai,Yuanfu Gong,Kun Zhu

论文类型：期刊论文

通讯作者：Xiaodong Zhang

文献类型：J

卷号：11

期号：5

页面范围：1633-1644

ISSN号：1939-1404

是否译文：否

发表时间：2018-03-27

收录刊物：SCI