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Extracting rice planting area based on deep learning and remote sensing data
QIU Ru-qiong, PENG Shao-kun, LI Meng-fan
HUBEI AGRICULTURAL SCIENCES
2023, 62 (11):
176-182.
DOI: 10.14088/j.cnki.issn0439-8114.2023.11.031
A pixel and multi-scale Deep Convolutional Neural Networks （DCNN） rice（Oryza sativa L.） remote sensing recognition model was constructed to address the issues of large sample collection workload, high sample labeling requirements, and difficulty in selecting the scale of rice receptive fields in existing rice （Oryza sativa L.） remote sensing recognition based on Deep Convolutional Neural Networks. Firstly, based on the distribution characteristics of rice planting, a pixel based DCNN extraction model was designed by integrating comprehensively the characteristics of Deep Convolutional Neural Networks methods;secondly, by combining multi-scale and DCNN, a multi-scale DCNN model was constructed to enhance the multi-scale characteristics of the receptive field; finally, in order to verify the effectiveness of the multi-scale DCNN model in extracting rice, the traditional machine learning SVM model, semantic segmentation D-Linknet model, and single-scale DCNN model were selected for classification accuracy comparison and analysis using Gaofen-1 and Gaofen-2 satellite images as data sources. The results showed that the accuracy, precision, recall, and equilibrium F-scores of the multi-scale DCNN model proposed in this study were 97.75%, 96.68%, 99.08%, and 97.85%, respectively;compared with other models, the multi-scale DCNN model had a simple structure, simple sample production, and high recognition accuracy, which had good application value.
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