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CROP PLANTING DISTRIBUTION PREDICTION METHOD BASED ON TIME SERIES REMOTE SENSING DATA AND CONVOLUTIONAL NEURAL NETWORK
CROP PLANTING DISTRIBUTION PREDICTION METHOD BASED ON TIME SERIES REMOTE SENSING DATA AND CONVOLUTIONAL NEURAL NETWORK
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机译：基于时间序列遥感数据和卷积神经网络的作物种植分布预测方法
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摘要
A crop planting distribution prediction method based on time series remote sensing data and a convolutional neural network. The method comprises the following steps: step 1: carrying out ground survey and training sample establishment; step 2: constructing a crop planting distribution prediction model based on time series remote sensing data and a convolutional neural network, wherein the convolutional neural network carries out prediction by means of data of a target pixel point and surrounding pixel points thereof in a multi-temporal image, and an input value is a multi-temporal high-resolution multi-spectral image, and an output value is classification information of crop types and crop rotation modes; and step 3: inputting the time series remote sensing data of a statistical area into the constructed model to acquire a recognition result. A ground survey of only a small number of representative parcels of land is required, a prediction model fusing time series features of remote sensing data and the local features of remote sensing images is constructed, and the context information of decision points is introduced, thereby improving the accuracy of a prediction result.
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