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Few-shot hyperspectral image classification aims to identify the classes ofeach pixel in the images by only marking few of these pixels. And in order toobtain the spatial-spectral joint features of each pixel, the fixed-sizepatches centering around each pixel are often used for classification. However,observing the classification results of existing methods, we found thatboundary patches corresponding to the pixels which are located at the boundaryof the objects in the hyperspectral images, are hard to classify. Theseboundary patchs are mixed with multi-class spectral information. Inspired bythis, we propose to augment the prototype network with TransMix for few-shothyperspectrial image classification(APNT). While taking the prototype networkas the backbone, it adopts the transformer as feature extractor to learn thepixel-to-pixel relation and pay different attentions to different pixels. Atthe same time, instead of directly using the patches which are cut from thehyperspectral images for training, it randomly mixs up two patches to imitatethe boundary patches and uses the synthetic patches to train the model, withthe aim to enlarge the number of hard training samples and enhance theirdiversity. And by following the data agumentation technique TransMix, theattention returned by the transformer is also used to mix up the labels of twopatches to generate better labels for synthetic patches. Compared with existingmethods, the proposed method has demonstrated sate of the art performance andbetter robustness for few-shot hyperspectral image classification in ourexperiments.

Augmenting Prototype Network with TransMix for Few-shot Hyperspectral Image Classification