高光谱影像小样本分类的图卷积网络方法

Graph convolutional network method for small sample classification of hyperspectral images

现有的基于卷积神经网络的高光谱影像分类方法通常对影像的规则正方形区域进行卷积,无法普遍适应具有不同地物分布和几何外观的影像局部区域,因此在小样本情况下的分类性能较差,而图卷积网络能对图拓扑信息所代表的不规则影像区域进行卷积。为此,本文提出基于图卷积网络的高光谱影像分类方法。该方法在构建拓扑图的过程中考虑了影像的空间光谱信息,并利用图卷积网络聚合邻居节点的特征信息。在Pavia大学、Indian Pines和Salinas 3个数据集上的试验结果表明,该方法能在训练样本较少的情况下取得较高的分类精度。

Existing based on convolutional neural network classification method of hyperspectral images usually rules of the square area of image convolution,not widely adapt to different terrain local area distribution and geometry appearance of the image,therefore,under the condition of small sample classification performance is poorer,and figure convolution can network topology information on the map represent irregular image area of the convolution.Therefore,a hyperspectral image classification method based on graph convolution network is proposed.In this method,the spatial spectral information of the image is considered in the process of constructing the graph,and the feature information of the neighbor node is aggregated by the graph convolution network.Experimental results on three data sets,Pavia university,Indian Pines and Salinas,show that this method can achieve a high classification accuracy with a small number of training samples.