  Hyperspectral images (HSIs) contain rich spectral and spatial information.
Motivated by the success of transformers in the field of natural language
processing and computer vision where they have shown the ability to learn long
range dependencies within input data, recent research has focused on using
transformers for HSIs. However, current state-of-the-art hyperspectral
transformers only tokenize the input HSI sample along the spectral dimension,
resulting in the under-utilization of spatial information. Moreover,
transformers are known to be data-hungry and their performance relies heavily
on large-scale pretraining, which is challenging due to limited annotated
hyperspectral data. Therefore, the full potential of HSI transformers has not
been fully realized. To overcome these limitations, we propose a novel
factorized spectral-spatial transformer that incorporates factorized
self-supervised pretraining procedures, leading to significant improvements in
performance. The factorization of the inputs allows the spectral and spatial
transformers to better capture the interactions within the hyperspectral data
cubes. Inspired by masked image modeling pretraining, we also devise efficient
masking strategies for pretraining each of the spectral and spatial
transformers. We conduct experiments on six publicly available datasets for HSI
classification task and demonstrate that our model achieves state-of-the-art
performance in all the datasets. The code for our model will be made available
at https://github.com/csiro-robotics/factoformer.
