Extraction of paddy rice based on convolutional neural network using multi-source remote sensing data
CAI Yaotong1,2,3,4(), LIU Shutong4, LIN Hui1,2,3,4, ZHANG Meng1,2,3,4()
1. Research Center of Forestry Remote Sensing and Information Engineering, Central South University of Forestry and Technology, Changsha 410004, China 2. Key Laboratory of Forestry Remote Sensing Based Big Data and Ecological Security for Hunan Province, Changsha 410004, China 3. Key Laboratory of State Forestry Administration on Forest Resources Management and Monitoring in Southern Area, Changsha 410004, China 4. College of Forestry, Central South University of Forestry and Technology, Changsha 410004, China
Rice is one of the most widely planted food crops in China. Therefore, timely and accurate rice identification and monitoring is of great significance to the national food security and the evolution of agricultural land spatial pattern. In this study, multi-temporal Sentinel-2A multispectral images, vegetation indices, vegetation abundance and Landsat 8 derived LST on the critical period of rice phenology were used. The CNN, SVM and RF classifiers were applied to extracting the paddy rice and finally the paddy rice map was obtained. The result shows that using multi-temporal and multi-source remote sensing data with the CNN algorithm can effectively extract rice information in high heterogeneity region. The overall accuracy of rice classification and Kappa coefficient are over 92% and 0.90 respectively. This study has demonstrated the potential of using moderate spatial resolution images combined with CNN to map the paddy rice in highly heterogeneous area.
蔡耀通, 刘书彤, 林辉, 张猛. 基于多源遥感数据的CNN水稻提取研究[J]. 国土资源遥感, 2020, 32(4): 97-104.
CAI Yaotong, LIU Shutong, LIN Hui, ZHANG Meng. Extraction of paddy rice based on convolutional neural network using multi-source remote sensing data. Remote Sensing for Land & Resources, 2020, 32(4): 97-104.
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2020, Vol. 32 
), 刘书彤4, 林辉1,2,3,4, 张猛1,2,3,4(
