边缘保护滤波和深度残差网络结合的高光谱影像分类  

Hyperspectral Image Classification Based on Edge-Preserving Filter and Deep Residual Network

作　　者：吕欢欢[1,2] 王琢璐 张辉 LüHuanhuan;Wang Zhuolu;Zhang Hui(School of Software,Liaoning Technical University,Huludao 125105,Liaoning,China;School of Information Engineering,Huzhou University,Huzhou 313000,Zhejiang,China)

机构地区：[1]辽宁工程技术大学软件学院,辽宁葫芦岛125105 [2]湖州师范学院信息工程学院,浙江湖州313000

出　　处：《激光与光电子学进展》2022年第16期252-263,共12页Laser & Optoelectronics Progress

基　　金：国家自然科学基金(61540056);辽宁省自然科学基金(20180550450)。

摘　　要：针对高光谱影像波段间相关度强、光谱和空间结构复杂性高和训练样本数量有限等问题,提出一种边缘保护滤波和深度残差网络结合的分类方法。首先采用联合双边滤波增强地物的边缘结构以提取出高质量的空间特征,将空间特征与光谱特征融合得到原始空谱特征;然后构建二维卷积神经网络,在卷积层中加入跳层连接将模型改进为一种深度残差网络模型;最后采用该模型提取影像的深层空谱特征并将其输入到Softmax分类器完成影像分类。实验在两个数据集上与相关先进方法比较,结果表明,本文方法考虑到了地物边缘结构的重要作用,缓解了卷积神经网络分类中的过拟合现象,显著提高了高光谱影像的分类精度。Herein,we proposed a hyperspectral image classification method using an edge-preserving filter and deep residual network due to the characteristics of the strong correlation between hyperspectral image bands,high complexity of spectral and spatial structures,and a limited number of training samples.First,we applied joint bilateral filtering to enhance the edge structure of ground objects and extract high-quality spatial features.The extracted spatial features were fused with spectral features to obtain the spatial-spectral features.Furthermore,we constructed a two-dimensional convolutional neural network and improved the model to a deep residual network model by adding a hop layer connection in the convolutional layer.Then,the model was used to extract the deep spatial-spectral features and input them to the Softmax classifier.We compare the experiment with related state-of-the-art methods on two datasets,and the results show that the proposed method alleviates the overfitting phenomenon in the convolutional neural network classification and considers the important role of the edge structure of ground objects,which significantly improves the classification accuracy of the hyperspectral images.

关 键 词：成像系统 高光谱遥感影像 空谱特征 联合双边滤波 卷积神经网络 残差网络 

分 类 号：TP751[自动化与计算机技术—检测技术与自动化装置]