结合注意力和改进样本选取方法的少样本高光谱分类孪生网络

针对高光谱图像(hyperspectral image)样本人工标记困难导致的样本数量不足的问题, 本文提出了一个结合注意力和空间邻域的少样本孪生网络算法. 它首先对高光谱图像进行PCA预处理, 实现数据降维; 其次, 对模型训练样本采用间隔采样和边缘采样的方式进行选取, 以有效减少冗余信息; 之后, Siamese network以大小不同的patch形式进行两两结合, 构建出样本对作为训练集进行训练, 不仅实现了数据增强的效果, 还能在提取光谱信息特征的同时, 充分提取目标像素光谱信息以及其周围邻域空间信息; 最后, 添加光谱维度的注意力模块以及空间维度的相似度度量模块, 分别对光谱信息和空间邻域信息进行权重分布, 以达到提升分类性能的目的. 实验结果表明, 本文提出的方法在部分公开数据集上对比常用方法取得了较好的实验效果.

Few-shot Hyperspectral Classification Siamese Network Combining Attention and Improved Sample Selection Method

In order to solve the problem of the insufficient number of hyperspectral image samples due to the difficulty of artificial labeling, a small sample twin network algorithm combining attention and spatial neighborhood is proposed in this study. Firstly, the hyperspectral image is preprocessed by PCA to achieve data dimensionality reduction. Secondly, the training samples of the model are selected by means of interval sampling and edge sampling to effectively reduce redundant information. After that, the Siamese network combines the samples in the form of patches of different sizes and constructs the sample pairs for training as a training set, which not only realizes the effect of data enhancement but also fully extracts the spectral information of target pixels and the spatial information of its neighborhoods while extracting spectral information features. Finally, the attention module of spectral dimension and the similarity measurement module of spatial dimension are added to distribute the weight of spectral information and spatial neighborhood information respectively, so as to improve classification performance. The experimental results show that the proposed method achieves better experimental results compared with common methods on some public datasets.