阅读说明：本技术 一种基于时序遥感数据和卷积神经网络的作物种植分布预测方法 (Crop planting distribution prediction method based on time sequence remote sensing data and convolutional neural network ) 是由 张炜 黄河 史杨 吴晓伟 于 2019-10-11 设计创作，主要内容包括：一种基于时序遥感数据和卷积神经网络的作物种植分布预测方法,包括下列步骤：步骤1：地面调查及训练样本建立；步骤2：构造基于时序遥感数据和卷积神经网络的作物种植分布预测模型,所述卷积神经网络通过在多时相图像中目标像素点及其周围像素点的数据对其进行预测,输入值为多时相的高分辨率多光谱图像,输出值为作物类型、轮作方式的分类信息；步骤3：将统计区域的时序遥感数据输入已构建的模型获取识别结果。只需要通过少量的代表性地块的地面调查,构建了融合遥感遥感数据时序特征和遥感图像局部特征的预测模型,引入了决策点的上下文信息,提高了预测结果的准确性。(A crop planting distribution prediction method based on time sequence remote sensing data and a convolutional neural network comprises the following steps: step 1: ground investigation and training sample establishment; step 2: constructing a crop planting distribution prediction model based on time sequence remote sensing data and a convolutional neural network, wherein the convolutional neural network predicts a target pixel point and peripheral pixel points in a multi-temporal image through data of the target pixel point and the peripheral pixel points, the input value is a multi-temporal high-resolution multi-spectral image, and the output value is classification information of crop types and crop rotation modes; and step 3: and inputting the time sequence remote sensing data of the statistical area into the constructed model to obtain a recognition result. A prediction model fusing the time sequence characteristics of the remote sensing data and the local characteristics of the remote sensing image is constructed only through ground investigation of a small number of representative plots, context information of decision points is introduced, and accuracy of prediction results is improved.)