基于深度残差网络的遥感数据分类

Remote Sensing Data Classification Based on Deep Residual Network

论文提出了一种基于深度残差卷积神经网络的高光谱遥感数据的分类方法。我们将深度残差卷积神经网络作为一种分类器,将待分类的像元及其领域像元一同作为神经网络的输入,通过残差网络的算法模型实现高光谱遥感数据的分类。我们使用深度学习技术将数据特征提取出来再进行分类,以达到提高分类准确度和效率的目的。本文主要通过改善分类方法来增强遥感数据分类的效率和处理能力。

This paper proposes a hyperspectral remote sensing data classification method based on deep residual convolutional neural network. We use the deep residual convolutional neural network as a classifier to classify the pixels to be classified and their domain pixels into the input of the neural network. The algorithm model of the residual network is used to classify the hyperspectral remote sensing data. We use deep learning technology to extract data features and then classify them to achieve the purpose of improving classification accuracy and efficiency. This paper mainly enhances the efficiency and processing ability of remote sensing data classification by improving the classification method.