Journal
APPLIED SCIENCES-BASEL
Volume 9, Issue 14, Pages -Publisher
MDPI
DOI: 10.3390/app9142917
Keywords
convolutional neural network; high-resolution remote sensing imagery; Gaofen 2 imagery; crops; winter wheat; spatial distribution information; Feicheng county
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Funding
- Science Foundation of Shandong [ZR2017MD018]
- Key Research and Development Program of Ningxia [2019BEH03008]
- Open Research Project of the Key Laboratory for Meteorological Disaster Monitoring, Early Warning and Risk Management of Characteristic Agriculture in Arid Regions [CAMF-201701, CAMF-201803]
- Key Open Laboratory of Arid Climate Change and Disaster Reduction of CMA [IAM201801]
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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.
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