Journal
IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING
Volume 11, Issue 5, Pages 1633-1644Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/JSTARS.2018.2810320
Keywords
Convolutional neural networks (CNN); deep learning (DL); fully convolutional networks (FCN); remote sensing; SDFCN; semantic segmentation
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Funding
- LIESMARS Special Research Funding
- Fundamental Research Funds for the Central Universities
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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.
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