基于改进GraphSAGE的高光谱图像分类

针对传统的图卷积网络节点嵌入过程中接受邻域范围小的问题, 本文提出了一种基于改进GraphSAGE算法的高光谱图像分类网络. 首先, 利用超像素分割算法对原始图像进行预处理, 减少图节点的个数, 既最大化保留了原始图像的局部拓扑结构信息, 又降低了算法的复杂度, 缩短运算时间; 其次, 采用改进的GraphSAGE算法, 对目标节点进行平均采样, 选用平均聚合函数对邻居节点进行聚合, 降低空间复杂度. 在公开的高光谱图像数据集Pavia University和Kenndy Space Center上与相关模型进行对比, 实验证明, 基于改进GraphSAGE算法的高光谱图像分类网络可以取得较好的分类结果.

Hyperspectral Image Classification Based on Improved GraphSAGE

To address the problem of a small accepting neighborhood range during the node embedding of traditional graph convolutional networks, this study proposes a hyperspectral image classification network based on an improved GraphSAGE algorithm. Firstly, the original image is preprocessed by using the super-pixel segmentation algorithm to reduce the number of image nodes. This not only conserves the local topology information of the original image to the largest extent but also reduces algorithm complexity and thus shortened operation time. Secondly, the average sampling of the target node is carried out by the improved GraphSAGE algorithm, and the neighbor nodes are aggregated by the average aggregation function to reduce spatial complexity. Finally, the proposes approach is compared with other models on the public hyperspectral image datasets Pavia University and Kenndy Space Center. The experiment proves that the hyperspectral image classification network based on the improved GraphSAGE algorithm can achieve good classification results.