基于记忆关联学习的小样本高光谱图像分类方法

Hyperspectral Image (HSI) classification is one of the fundamental applications in remote sensing domain. Due to the expensive cost of manual labeling in HSIs, in real applications, only small labeled samples can be obtained. However, limited samples cannot accurately describe the data distribution and often cause the training of classifiers to be overfitting. To address this problem, we present a small sample hyperspectral image classification method based on memory association learning. First, considering that the unlabeled samples also contain a lot of information related to the data distribution, we construct a memory module based on the labeled samples. Then, according to the feature association among labeled and unlabeled samples, we learn the label distribution of the unlabeled sample with the continuously updated memory module. Finally, we build an unsupervised classifier model and a supervised classifier model, and jointly learn these two models. Extensive experimental results on multiple hyperspectral image classification datasets demonstrate that the proposed method can effectively improve the accuracy of small sample HSI classification.