基于局部分类器和深度神经网络的遥感图像分类（英文）  

Remote sensing image classification based on local partial classifier and deep neural network

作　　者：贾厚林[1] 戴红霞[2] 

机构地区：[1]无锡机电高等职业技术学校电子信息工程系,江苏无锡214028 [2]江苏信息职业技术学院电子信息工程学院,江苏无锡214153

出　　处：《机床与液压》2017年第24期64-68,89,共6页Machine Tool & Hydraulics

摘　　要：深度学习方法作为大数据自动分类工具时表现出较高的性能,但是在处理遥感图像任务时(比如图像分类问题)表现出效率较低。为此,提出一种新的基于局部分类器和深度神经网络的遥感图像分类算法。首先从原始图像中提取多个局部特征,并将这些特征输入给用于判决的深度神经网络,然后按照分配给图像标签对每个局部特征进行分类。最后根据简易的投票方法判决整体图像的结果。利用World View2高分辨率卫星遥感影像数据进行了分类实验,结果显示:提出的方法优于其他分类方法具有较好的分类准确性和分类效率。The deep learning method exhibits high performance as a large data auto-sorting tool. However, when dealing with remote sensing image tasks, such as image classification, the problem of low efficiency is shown. Therefore, a new classification algorithm for remote sensing image based on local classifier and deep neural network is proposed in this paper. First, the method extracts a plurality of local features from the original image and inputs them into the deep neural network for the decision, and then classifies each local feature according to the assignment to the image tag. Finally, the result of the overall image is judged according to the simple voting method. Based on the WorldView2 high-resolution satellite remote sensing image data, the classification experiment was carried out. Experimental results show that the proposed method is superior to other classification methods and it has better classification accuracy and classification efficiency.

关 键 词：遥感图像分类 局部分类器 深度学习 深度神经网络 分类性能 

分 类 号：TP79[自动化与计算机技术—检测技术与自动化装置]