DOI number:10.1109/jstars.2018.2810320

Journal:IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING

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

Abstract: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.

Co-author:Qing Wang,Fan Dai,Yuanfu Gong,Kun Zhu

Indexed by:Journal paper

Correspondence Author:Xiaodong Zhang

Document Type:J

Volume:11

Issue:5

Page Number:1633-1644

ISSN No.:1939-1404

Translation or Not:no

Date of Publication:2018-03-27

Included Journals:SCI