Abstract
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.
Highlights
Wheat is the most important food crop in the world, comprising 38.76% of the total area cultivated for food crops and 29.38% of total food crop production in 2016 [1]
As extraction of crop spatial distribution mainly relies on pixel-based image classification, correctly determining pixel features for accurate classification is the basis for this approach [9,10,11,12]
We developed a new convolutional neural networks (CNN) consisting of a feature extractor, encoder, and a Bayesian classifier, which we refer to as a Bayesian Convolutional Neural Network (CNN-Bayesian model)
Summary
Wheat is the most important food crop in the world, comprising 38.76% of the total area cultivated for food crops and 29.38% of total food crop production in 2016 [1]. As conditional random field (CRF) have the ability to learn the dependencies between categories of pixels, CRF can be used to further refine segmentation results [44] These convolution-based per-pixel-label models have been applied in remote sensing image segmentation with remarkable results. The CNN structure used in the pre-pixel classification of remote sensing imagery generally includes two parts: feature extractor and classifier The former has been the focus of many researchers with good results. We developed a new CNN consisting of a feature extractor, encoder, and a Bayesian classifier, which we refer to as a Bayesian Convolutional Neural Network (CNN-Bayesian model) We used this model to extract winter wheat spatial distribution information from Gaofen 2 (GF-2) remote sensing imagery and compared the results with those achieved by other methods
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