  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.
