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

Autor:	Fan Keqi, Chengming Zhang, Hui Zhao, Shuai Gao, Yingjuan Han, Dejuan Song, Ya’nan Zhang, Feng Li
Rok vydání:	2019
Předmět:	winter wheat convolutional neural network Visual Geometry Group Network Bayesian model per-pixel classification high-resolution remote sensing imager Gaofen 2 image Computer science Feature vector Bayesian probability 0211 other engineering and technologies 02 engineering and technology Bayesian inference Convolutional neural network Classifier (linguistics) 0202 electrical engineering electronic engineering information engineering lcsh:Science 021101 geological & geomatics engineering Pixel business.industry Pattern recognition Feature (computer vision) Computer Science::Computer Vision and Pattern Recognition General Earth and Planetary Sciences lcsh:Q 020201 artificial intelligence & image processing Artificial intelligence business Encoder
Zdroj:	Remote Sensing; Volume 11; Issue 6; Pages: 619 Remote Sensing, Vol 11, Iss 6, p 619 (2019)
ISSN:	2072-4292
Popis:	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.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_dedup___::a55e320908134dbb8ba323d0ccb2b83e https://doi.org/10.3390/rs11060619 Zobrazit plný text záznamu Plný text ve formátu PDF Plný text ve formátu HTML
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