激光与光电子学进展, 2019, 56 (15): 151006, 网络出版: 2019-08-05   

利用残差密集网络的高光谱图像分类 下载: 1224次

Hyperspectral Image Classification Based on Residual Dense Network
作者单位
信息工程大学, 河南 郑州 450001
摘要
卷积神经网络模型能够提取图像不同层次的分层特征,提取图像包含有大量的细节信息,然而,现有方法没有充分利用网络模型提取的所有分层特征。为了充分利用所有分层特征,增强特征重利用和信息连续传递,设计了适用于高光谱图像分类的残差密集网络模型。残差密集网络结合了残差网络和密集网络,包括浅层特征提取、残差密集单元和密集特征融合三部分。利用卷积操作提取原始图像的浅层特征,将浅层特征作为残差密集单元的输入,残差密集单元的输出与下一个单元中每个卷积层的输出建立短连接,实现了信息连续传递;将两个单元提取的密集特征与浅层特征相加形成全局残差学习,实现了所有分层特征的融合,最终的融合特征用于高光谱图像分类。实验表明,本文方法用于Indian Pines数据、University of Pavia数据及Salinas数据能够分别取得98.71%、99.31%及97.91%的分类精度,有效提高了高光谱图像的分类精度,增强了分类方法的稳定性。
Abstract
A convolutional neural network (CNN) can extract hierarchical features in an image, and the extracted images include a large amount of detailed information contained in the image. However, CNN-based methods do not take full advantage of all hierarchical features extracted by the network. To make full use of all hierarchical features and enhance feature reuse and information flow, we design a residual dense network suitable for hyperspectral image classification. The residual dense network combines residual and dense networks, including shallow feature extraction, residual dense units, and dense feature fusion. Firstly, shallow features of the original image are extracted using a convolution operation, which is input to the residual dense unit. Secondly, the output of the residual dense unit establishes a shortcut connection with each convolution layer and output layer in the next unit, thereby realizing continuous information transmission. Subsequently, dense features extracted from the two units are added to the shallow features to form global residual learning, which realizes the fusion of all hierarchical features. The fused features are then used for hyperspectral image classification. Experimental results demonstrate that the proposed method can obtain 98.71%, 99.31%, and 97.91% classification accuracies on the Indian Pines, University of Pavia, and Salinas data, respectively, which effectively improves the classification accuracy of hyperspectral images and enhances the stability of classification methods.

魏祥坡, 余旭初, 谭熊, 刘冰. 利用残差密集网络的高光谱图像分类[J]. 激光与光电子学进展, 2019, 56(15): 151006. Xiangpo Wei, Xuchu Yu, Xiong Tan, Bing Liu. Hyperspectral Image Classification Based on Residual Dense Network[J]. Laser & Optoelectronics Progress, 2019, 56(15): 151006.

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