基于超像素图卷积网络的高光谱图像分类

Hyperspectral image classification based on superpixel graph convolution network

高光谱图像分类是遥感领域中一个具有挑战性的问题。基于深度学习框架的高光谱图像分类方法,由于其良好的分类性能受到了越来越多的关注。然而,这些方法普遍存在的问题为:模型的训练不仅需要大量的时间,而且还需要大量的标签样本。针对此问题,本文提出了一种基于超像素图卷积网络的高光谱图像分类方法。该方法以超像素作为图的节点,极大地减小了图的规模,从而提高了分类效率;提出的超像素合并技术能有效地融合光谱-空间信息,增强了空间信息在分类中的作用;为了验证该方法的有效性,在Indian Pines、Pavia University两个实际数据集上进行试验,并与一些先进的基于深度学习框架的高光谱图像分类方法进行比较。结果表明,本文方法在分类精度和分类效率上均优于其他方法。

Hyperspectral image classification is an challenging problem in remote sensing.Hyperspectral image classification based on deep learning framework has attracted more and more attention due to its excellent classification performance.However,the common drawback of these methods is that the training of the model requires not only a lot of time,but also a large number of labeled samples.To address this issue,a superpixel graph convolution network-based hyperspectral image classification method is proposed.The method takes superpixels instead of pixels as nodes of graphs,which greatly reduces the graph size and improves the classification efficiency.The proposed superpixel merging and smoothing techniques effectively fuse spectral and spatial information and enhance the role of spatial information in classification.To validate the effectiveness of the proposed method,experiments are carried out on two real datasets,Indian Pines and Pavia University.Furthermore,the method is also compared with some advanced hyperspectral image classification methods based on deep learning framework.The results show that the proposed method is superior to other methods in classification accuracy and efficiency.