Few-shot hyperspectral image classification aims to identify the classes of each pixel in the images by only marking few of these pixels. And in order to obtain the spatial-spectral joint features of each pixel, the fixed-size patches centering around each pixel are often used for classification. However, observing the classification results of existing methods, we found that boundary patches corresponding to the pixels which are located at the boundary of the objects in the hyperspectral images, are hard to classify. These boundary patchs are mixed with multi-class spectral information. Inspired by this, we propose to augment the prototype network with TransMix for few-shot hyperspectrial image classification(APNT). While taking the prototype network as the backbone, it adopts the transformer as feature extractor to learn the pixel-to-pixel relation and pay different attentions to different pixels. At the same time, instead of directly using the patches which are cut from the hyperspectral images for training, it randomly mixs up two patches to imitate the boundary patches and uses the synthetic patches to train the model, with the aim to enlarge the number of hard training samples and enhance their diversity. And by following the data agumentation technique TransMix, the attention returned by the transformer is also used to mix up the labels of two patches to generate better labels for synthetic patches. Compared with existing methods, the proposed method has demonstrated sate of the art performance and better robustness for few-shot hyperspectral image classification in our experiments.

翻译：小样本高光谱图像分类旨在通过仅标注少量像素即可识别图像中每个像素的类别。为获取各像素的空间-光谱联合特征，通常采用以每个像素为中心的固定大小图像块进行分类。然而，分析现有方法的分类结果发现，对应高光谱图像中物体边界的像素所构成的边界块难以分类，这些边界块混合了多类光谱信息。受此启发，我们提出增广原型网络与TransMix的小样本高光谱图像分类方法（APNT）。该方法以原型网络为骨干，采用Transformer作为特征提取器学习像素间关联，并对不同像素赋予差异化关注。同时，不同于直接使用从高光谱图像中裁剪的图像块进行训练，该方法随机混合两个图像块以模拟边界块，并利用合成图像块训练模型，旨在增加困难训练样本数量并提升其多样性。遵循数据增广技术TransMix，Transformer生成的注意力还用于混合两个图像块的标签，为合成块生成更优的标签。实验表明，与现有方法相比，所提方法在小样本高光谱图像分类中展现出最先进的性能和更强的鲁棒性。