基于多尺度学习与深度卷积神经网络的遥感图像土地利用分类

土地利用信息是国土资源管理的基础和重要依据，随着高分辨率遥感图像数据的日益增多，迫切需要快速准确的土地利用分类方法。目前应用较广的面向对象的分类方法对空间特征的利用尚不够充分，在特征选择上存在一定的局限性。为此，提出一种基于多尺度学习与深度卷积神经网络（deep convolutional neural network，DCNN）的多尺度神经网络（multi-scale neural network，MSNet）模型，基于残差网络构建了100层编码网络，通过并行输入实现输入图像的多尺度学习，利用膨胀卷积实现特征图像的多尺度学习，设计了一种端到端的分类网络。以浙江省0.5 m分辨率的光学航空遥感图像为数据源进行了实验，总体分类精度达91.97%，并将其与传统全卷积网络（fully convolutional networks，FCN）方法和基于支持向量机（support vector machine，SVM）的面向对象方法进行了对比，结果表明，本文所提方法分类精度更高，分类结果整体性更强。

Land use classification of remote sensing images based on multi-scale learning and deep convolution neural network

Land use data is an important fundamental information for national land resources management. Following the availability of high resolution remote sensing image data, it is on urgent demand to have a fast and accurate land-use classification method. The object-oriented classification which has been widely applied at present has some problems such as low level utilization of spatial features and limited choice of features. In this paper, a multi-scale neural network (MSNet)model based on multi-scale learning and deep convolutional neural network (DCNN) is proposed. We built 100 layers encoding network based on residual neural network, and conducted several parallel input streams to accomplish multi-scale learning of input images, then utilized dilated convolution to accomplish multi-scale learning of feature images, finally designed an end-to-end classification network. Experiments were implemented on the optical aerial remote sensing images dataset of Zhejiang province with 0.5 m resolution, the overall accuracy of classification reached 91.97%. Compared with fully convolutional networks (FCN) network and the object-oriented method based on support vector machine (SVM), the MSNet method has a higher precision of classification and demonstrates more integrity of the scene.