【摘要】 针对目前利用深度学习技术进行高分光学遥感图像分类方法研究中尚存在的不足,本文提出了一种以多时相遥感数据为数据源,面向农作物种植信息提取的分类算法。该算法首先获取农作物在若干典型生长时期的光学遥感图像并进行配准等预处理,然后建立了一种以像素为单位的数据组织结构,该结构包含不同生长时期的作物信息、纹理信息,能较好地解决现有分类研究中信息不足的问题;接着以前馈神经网络为基础,建立了一种以像素为单位的分类算法,最后以得到的逐像素分类结果为基础进行成图。与同类方法相比,本文提出的算法综合考虑了农作物在不同生长时期的特征,更能发挥深度学习技术的优势,且多时相数据在提高农作物提取信息精度方面具有明显优势。更多还原

【Abstract】 In view of the shortages in researching high resolution optical remote sensing image classification method by the deep learning technology,a classification algorithm for crop information extraction based on the multi-temporal remote sensing data was proposed in this paper. The algorithm firstly obtained the optical remote sensing images of crop at several typical growth stages,and preprocessed these images such as registration. Then it established data organization structure based on pixels to solve the problem of insufficient information in the existing classification researches,which contained crop information and texture information at different growth stages. And it proposed a pixel classification algorithm based on the feedforward neural network. Finally,it mapped images based on pixel by pixel classification results. Comparing to the previous methods,this method comprehensively considered the characteristics of crop at different growth stages,could give full play to the advantages of the deep learning technology and had obvious advantages in improving the precision of crop information extraction.更多还原