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引用本文:

DOI:

10.11834/jrs.20210522

收稿日期:

2020-11-16

修改日期:

2021-07-25

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基于多图卷积网络的遥感图像小样本分类
陈杰虎, 汪西莉
陕西师范大学 计算机科学学院
摘要:

小样本学习旨在利用非常少的监督信息识别出新的类别,由于忽视了样本之间的关联信息,现有的小样本分类方法用于遥感图像小样本分类时往往不能获得令人满意的精度。为此,本文利用图来建模图像在特征空间的相似关系,使用图卷积运算平滑同类别图像的特征,增强不同类别图像特征的区分度,提升分类精度。所提方法在现有图卷积运算的基础上,使用多阶次的邻接矩阵线性加权的方法代替传统的一阶邻接矩阵,通过图谱分析得出这种改进方法能够让不同阶次邻接矩阵的频率响应函数在高频部分正负相抵,有效抑制图信号的高频分量,更显著的提升同类别节点特征的聚集程度。同时,在训练过程引入了微调的方法,使用新类别中的标记数据对最后一层图卷积网络进行少量次数的训练,能够进一步提高精度,增强模型的迁移能力。实验使用三个常用的遥感数据集对方法的有效性进行了测试,结果表明提出的方法在测试数据集每一类标记样本数分别为1、3、5、10的情况下,在分类精度方面均优于原型网络等比较方法。

Multi-graph Convolution Network for Remote Sensing Image Few Shot Classification
Abstract:

Abstract: Few shot learning aims at identifying novel concepts with very little supervisory information. For remote sensing (RS) image few shot classification, the existing methods are often unable to achieve satisfactory accuracy because of ignoring the correlation information between samples. In order to improve the RS few shot classification accuracy, Multi Graph Convolutional Network (Multi-GCN) is proposed in this paper. The proposed method uses a graph to model the similarity of images in the feature space, and uses multi-graph convolutional operation to smooth the image features from the same class to enhance the discrimination of different class samples to improve classification accuracy. First, an adjacency matrix of the graph is computed by "KNN + radial basis function" method after the initial feature of each image being extracted by a pre-trained convolutional neural network. Then the proposed multi-graph convolutional operation is used to update image features, which can make images with the same class get more similar feature representation. Finally, the updated features are used to predict class labels of nodes by metric distance between the labeled and the unlabeled in feature space. Through spectral domain analysis, it is concluded that the proposed method can effectively suppress high-frequency components of the graph signals and significantly enhance the clustering coefficients of node features from the same class. In addition, the fine-tuning method is introduced in the training process, and the labeled data in the new class are used to train the last layer graph convolutional network for a small number of times, which can further improve the classification accuracy and enhance the transfer ability of the model. In order to validate the effectiveness of the proposed method, the Multi-GCN is compared with ProtoNet, GNN-FSL and two single graph based methods ProtoGCN and ProtoIGCN on three benchmark datasets AID, OPTIMAL31 and RSI-CB256. The experimental results demonstrate that the classification accuracy of the proposed method is significantly higher than that of ProtoNet and GNN-FSL when the number of labeled samples of each class is only 1,3,5,10 and also higher than the graph convolutional methods based on a single adjacency matrix such as ProtoGCN and ProtoIGCN. After use of fine-tuning training methods, the classification accuracy can be further improved by about 1%. A few shot classification method of RS scene images is proposed in this paper. In this method, the multi-graph convolutional network is introduced into the metric network to smooth the data features, which can make images from same class get more similar feature representation and improve the classification accuracy. Experimental results demonstrate that the proposed method can increase the clustering degree of similar data in feature space and can achieve higher classification accuracy compared with ProtoNet, GNN-FSL and methods based on single graph convolutional network. The conclusion of multi-graph convolutional operation and spectrum analysis in this paper can also be extended to other graph-based semi-supervised learning.

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