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Article

Extracting Crop Spatial Distribution from Gaofen 2 Imagery Using a Convolutional Neural Network

1
College of Information Science and Engineering, Shandong Agricultural University, 61 Daizong Road, Taian 271000, China
2
Key Open Laboratory of Arid Climate Change and Disaster Reduction of CMA, 2070 Donggangdong Road, Lanzhou 730020, China
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Shandong Technology and Engineering Center for Digital Agriculture, 61 Daizong Road, Taian 271000, China
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Key Laboratory for Meteorological Disaster Monitoring and Early Warning and Risk Management of Characteristic Agriculture in Arid Regions, CMA, 71 Xinchangxi Road, Yinchuan 750002, China
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Shandong Provincal Climate Center, NO.12 Wuying Mountain Road, Jinan 250001, China
*
Author to whom correspondence should be addressed.
Appl. Sci. 2019, 9(14), 2917; https://doi.org/10.3390/app9142917
Received: 25 May 2019 / Revised: 17 July 2019 / Accepted: 18 July 2019 / Published: 22 July 2019
(This article belongs to the Special Issue Machine Learning Techniques Applied to Geospatial Big Data)
Using satellite remote sensing has become a mainstream approach for extracting crop spatial distribution. Making edges finer is a challenge, while simultaneously extracting crop spatial distribution information from high-resolution remote sensing images using a convolutional neural network (CNN). Based on the characteristics of the crop area in the Gaofen 2 (GF-2) images, this paper proposes an improved CNN to extract fine crop areas. The CNN comprises a feature extractor and a classifier. The feature extractor employs a spectral feature extraction unit to generate spectral features, and five coding-decoding-pair units to generate five level features. A linear model is used to fuse features of different levels, and the fusion results are up-sampled to obtain a feature map consistent with the structure of the input image. This feature map is used by the classifier to perform pixel-by-pixel classification. In this study, the SegNet and RefineNet models and 21 GF-2 images of Feicheng County, Shandong Province, China, were chosen for comparison experiment. Our approach had an accuracy of 93.26%, which is higher than those of the existing SegNet (78.12%) and RefineNet (86.54%) models. This demonstrates the superiority of the proposed method in extracting crop spatial distribution information from GF-2 remote sensing images. View Full-Text
Keywords: convolutional neural network; high-resolution remote sensing imagery; Gaofen 2 imagery; crops; winter wheat; spatial distribution information; Feicheng county convolutional neural network; high-resolution remote sensing imagery; Gaofen 2 imagery; crops; winter wheat; spatial distribution information; Feicheng county
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MDPI and ACS Style

Chen, Y.; Zhang, C.; Wang, S.; Li, J.; Li, F.; Yang, X.; Wang, Y.; Yin, L. Extracting Crop Spatial Distribution from Gaofen 2 Imagery Using a Convolutional Neural Network. Appl. Sci. 2019, 9, 2917. https://doi.org/10.3390/app9142917

AMA Style

Chen Y, Zhang C, Wang S, Li J, Li F, Yang X, Wang Y, Yin L. Extracting Crop Spatial Distribution from Gaofen 2 Imagery Using a Convolutional Neural Network. Applied Sciences. 2019; 9(14):2917. https://doi.org/10.3390/app9142917

Chicago/Turabian Style

Chen, Yan, Chengming Zhang, Shouyi Wang, Jianping Li, Feng Li, Xiaoxia Yang, Yuanyuan Wang, and Leikun Yin. 2019. "Extracting Crop Spatial Distribution from Gaofen 2 Imagery Using a Convolutional Neural Network" Applied Sciences 9, no. 14: 2917. https://doi.org/10.3390/app9142917

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