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In the hyperspectral image classification (HSIC) task, the most commonly usedmodel validation paradigm is partitioning the training-test dataset throughpixel-wise random sampling. By training on a small amount of data, the deeplearning model can achieve almost perfect accuracy. However, in ourexperiments, we found that the high accuracy was reached because the trainingand test datasets share a lot of information. On non-overlapping datasetpartitions, well-performing models suffer significant performance degradation.To this end, we propose a spectral-spatial axial aggregation transformer model,namely SaaFormer, that preserves generalization across dataset partitions.SaaFormer applies a multi-level spectral extraction structure to segment thespectrum into multiple spectrum clips, such that the wavelength continuity ofthe spectrum across the channel are preserved. For each spectrum clip, theaxial aggregation attention mechanism, which integrates spatial features alongmultiple spectral axes is applied to mine the spectral characteristic. Themulti-level spectral extraction and the axial aggregation attention emphasizespectral characteristic to improve the model generalization. The experimentalresults on five publicly available datasets demonstrate that our model exhibitscomparable performance on the random partition, while significantlyoutperforming other methods on non-overlapping partitions. Moreover, SaaFormershows excellent performance on background classification.

Boosting the Generalization Ability for Hyperspectral Image Classification using Spectral-spatial Axial Aggregation Transformer