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Article

A New CNN-Bayesian Model for Extracting Improved Winter Wheat Spatial Distribution from GF-2 imagery

1
College of Information Science and Engineering, Shandong Agricultural University, 61 Daizong Road, Taian 271000, Shandong, China
2
Shandong Technology and Engineering Center for Digital Agriculture, 61 Daizong Road, Taian 271000, Shandong, China
3
Key Laboratory for Meteorological Disaster Monitoring and Early Warning and Risk Management of Characteristic Agriculture in Arid Regions, CMA, 71 Xinchangxi Road, Yinchuan 750002, Ningxia, China
4
Shandong Provincal Climate Center, NO.12 Wuying Mountain Road, Jinan 250001, Shandong, China
5
Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, 9 Dengzhuangnan Road, Beijing 100094, China
*
Author to whom correspondence should be addressed.
Received: 30 January 2019 / Revised: 8 March 2019 / Accepted: 12 March 2019 / Published: 14 March 2019
When the spatial distribution of winter wheat is extracted from high-resolution remote sensing imagery using convolutional neural networks (CNN), field edge results are usually rough, resulting in lowered overall accuracy. This study proposed a new per-pixel classification model using CNN and Bayesian models (CNN-Bayesian model) for improved extraction accuracy. In this model, a feature extractor generates a feature vector for each pixel, an encoder transforms the feature vector of each pixel into a category-code vector, and a two-level classifier uses the difference between elements of category-probability vectors as the confidence value to perform per-pixel classifications. The first level is used to determine the category of a pixel with high confidence, and the second level is an improved Bayesian model used to determine the category of low-confidence pixels. The CNN-Bayesian model was trained and tested on Gaofen 2 satellite images. Compared to existing models, our approach produced an improvement in overall accuracy, the overall accuracy of SegNet, DeepLab, VGG-Ex, and CNN-Bayesian was 0.791, 0.852, 0.892, and 0.946, respectively. Thus, this approach can produce superior results when winter wheat spatial distribution is extracted from satellite imagery. View Full-Text
Keywords: winter wheat; convolutional neural network; Visual Geometry Group Network; Bayesian model; per-pixel classification; high-resolution remote sensing imager; Gaofen 2 image winter wheat; convolutional neural network; Visual Geometry Group Network; Bayesian model; per-pixel classification; high-resolution remote sensing imager; Gaofen 2 image
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MDPI and ACS Style

Zhang, C.; Han, Y.; Li, F.; Gao, S.; Song, D.; Zhao, H.; Fan, K.; Zhang, Y. A New CNN-Bayesian Model for Extracting Improved Winter Wheat Spatial Distribution from GF-2 imagery. Remote Sens. 2019, 11, 619. https://0-doi-org.brum.beds.ac.uk/10.3390/rs11060619

AMA Style

Zhang C, Han Y, Li F, Gao S, Song D, Zhao H, Fan K, Zhang Y. A New CNN-Bayesian Model for Extracting Improved Winter Wheat Spatial Distribution from GF-2 imagery. Remote Sensing. 2019; 11(6):619. https://0-doi-org.brum.beds.ac.uk/10.3390/rs11060619

Chicago/Turabian Style

Zhang, Chengming, Yingjuan Han, Feng Li, Shuai Gao, Dejuan Song, Hui Zhao, Keqi Fan, and Ya’nan Zhang. 2019. "A New CNN-Bayesian Model for Extracting Improved Winter Wheat Spatial Distribution from GF-2 imagery" Remote Sensing 11, no. 6: 619. https://0-doi-org.brum.beds.ac.uk/10.3390/rs11060619

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