基于局部分类器和深度神经网络的遥感图像分类

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WorldView2 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。

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

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.