  Although recent masked image modeling (MIM)-based HSI-LiDAR/SAR
classification methods have gradually recognized the importance of the spectral
information, they have not adequately addressed the redundancy among different
spectra, resulting in information leakage during the pretraining stage. This
issue directly impairs the representation ability of the model. To tackle the
problem, we propose a new strategy, named Mining Redundant Spectra (MRS).
Unlike randomly masking spectral bands, MRS selectively masks them by
similarity to increase the reconstruction difficulty. Specifically, a random
spectral band is chosen during pretraining, and the selected and highly similar
bands are masked. Experimental results demonstrate that employing the MRS
strategy during the pretraining stage effectively improves the accuracy of
existing MIM-based methods on the Berlin and Houston 2018 datasets.
