Spatially augmented guided sequence-based bidirectional encoder representation from transformer networks for hyperspectral classification studies

摘要

In recent years, bidirectional encoder representation from transformers (BERT) models have achieved superior performance in hyperspectral images (HSIs). It can capture the long-range correlations between HSI elements, but the local space and spectral band information of HSI is insufficient. We propose a spatially augmented guided sequence BERT network for HSI classification study, referred to as SAS-BERT, which makes more effective use of HSI's spatial and spectral information by improving the BERT model. First, a spatial augmentation learning module is added in the preprocessing stage to obtain more significant spatial features before the input network and better guide the spatial sequence. Then a spectral correlation module was used to represent the spectral band features of the HSI and to establish a correlation with the spatial location of the images to obtain better classification performance. Experimental results on three datasets show that the method proposed achieves better classification performance than other state-of-the-art methods.(c) The Authors. Published by SPIE under a Creative Commons Attribution 4.0 International License.Distribution or reproduction of this work in whole or in part requires full attribution of the originalpublication, including its DOI.

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