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Article

Comparing Fully Deep Convolutional Neural Networks for Land Cover Classification with High-Spatial-Resolution Gaofen-2 Images

1
College of Horticulture and Forestry Sciences, Huazhong Agricultural University, Wuhan 430070, China
2
Hubei Engineering Technology Research Centre for Forestry Information, Huazhong Agricultural University, Wuhan 430070, China
3
Key Laboratory of Urban Agriculture in Central China, Ministry of Agriculture, Wuhan 430070, China
4
Aerospace Information Research Institute, Chinese Academy of Science, Beijing 100101, China
*
Author to whom correspondence should be addressed.
ISPRS Int. J. Geo-Inf. 2020, 9(8), 478; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi9080478
Received: 7 June 2020 / Revised: 22 July 2020 / Accepted: 28 July 2020 / Published: 30 July 2020
(This article belongs to the Special Issue Geospatial Artificial Intelligence)
Land cover is an important variable of the terrestrial ecosystem that provides information for natural resources management, urban sprawl detection, and environment research. To classify land cover with high-spatial-resolution multispectral remote sensing imagery is a difficult problem due to heterogeneous spectral values of the same object on the ground. Fully convolutional networks (FCNs) are a state-of-the-art method that has been increasingly used in image segmentation and classification. However, a systematic quantitative comparison of FCNs on high-spatial-multispectral remote imagery was not yet performed. In this paper, we adopted the three FCNs (FCN-8s, Segnet, and Unet) for Gaofen-2 (GF2) satellite imagery classification. Two scenes of GF2 with a total of 3329 polygon samples were used in the study area and a systematic quantitative comparison of FCNs was conducted with red, green, blue (RGB) and RGB+near infrared (NIR) inputs for GF2 satellite imagery. The results showed that: (1) The FCN methods perform well in land cover classification with GF2 imagery, and yet, different FCNs architectures exhibited different results in mapping accuracy. The FCN-8s model performed best among the Segnet and Unet architectures due to the multiscale feature channels in the upsampling stage. Averaged across the models, the overall accuracy (OA) and Kappa coefficient (Kappa) were 5% and 0.06 higher, respectively, in FCN-8s when compared with the other two models. (2) High-spatial-resolution remote sensing imagery with RGB+NIR bands performed better than RGB input at mapping land cover, and yet the advantage was limited; the OA and Kappa only increased an average of 0.4% and 0.01 in the RGB+NIR bands. (3) The GF2 imagery provided an encouraging result in estimating land cover based on the FCN-8s method, which can be exploited for large-scale land cover mapping in the future. View Full-Text
Keywords: deep learning; classification; land cover; full convolutional network; Unet; Segnet; FCN deep learning; classification; land cover; full convolutional network; Unet; Segnet; FCN
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MDPI and ACS Style

Han, Z.; Dian, Y.; Xia, H.; Zhou, J.; Jian, Y.; Yao, C.; Wang, X.; Li, Y. Comparing Fully Deep Convolutional Neural Networks for Land Cover Classification with High-Spatial-Resolution Gaofen-2 Images. ISPRS Int. J. Geo-Inf. 2020, 9, 478. https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi9080478

AMA Style

Han Z, Dian Y, Xia H, Zhou J, Jian Y, Yao C, Wang X, Li Y. Comparing Fully Deep Convolutional Neural Networks for Land Cover Classification with High-Spatial-Resolution Gaofen-2 Images. ISPRS International Journal of Geo-Information. 2020; 9(8):478. https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi9080478

Chicago/Turabian Style

Han, Zemin, Yuanyong Dian, Hao Xia, Jingjing Zhou, Yongfeng Jian, Chonghuai Yao, Xiong Wang, and Yuan Li. 2020. "Comparing Fully Deep Convolutional Neural Networks for Land Cover Classification with High-Spatial-Resolution Gaofen-2 Images" ISPRS International Journal of Geo-Information 9, no. 8: 478. https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi9080478

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