智能系统学报2018，Vol.13Issue(4)：550-556,7.DOI:10.11992/tis.201706078
基于卷积神经网络的遥感图像分类研究
Classification of remote-sensing image based on convolutional neural network
摘要
Abstract
The classification of remote-sensing images is a specific application of pattern recognition technology in the remote-sensing domain.In this paper,we propose a method for the classification of remote-sensing images based on convolutional neural networks(CNN).In addition,to address the difficulty of providing effective information regarding a single-source feature in convolutional neural networks,we propose a multi-source and multi-feature fusion method.We combine the spectral,texture,and spatial-structure features of remote-sensing images in the form of vectors or matrices according to their spatial dimensions,and train the CNN model using these combined features.The experiment-al results show that multi-source and multi-feature fusion can effectively improve the model convergence speed and classification accuracy,in comparison with traditional classification methods,and that the CNN method achieves higher classification accuracy and classification effect.关键词
遥感图像/地物分类/卷积神经网络/特征融合Key words
remote-sensing image/classification of land cover/convolutional neural networks/feature fusion分类
信息技术与安全科学引用本文复制引用
李亚飞,董红斌..基于卷积神经网络的遥感图像分类研究[J].智能系统学报,2018,13(4):550-556,7.基金项目
国家自然科学基金项目(61472095). （61472095）
