Densely Connected Transformer with Frequency Awareness and Sam Guidance for Semi-Supervised Hyperspectral Image Classification

Advancements in Hyperspectral Image (HSI) spatial resolution pose challenges in pixel-wise classification. Semi-supervised self-training shows potential by using pseudo-labels from unlabeled samples. However, the Hughes phenomenon and environmental factors often lead to spectral variability and undermine pseudo-label credibility. To address above issues, we propose a densely connected Transformer leveraging Discrete Wavelet Transform for extracting nuanced spatial-spectral features and redundancy removal, and we design a filtering strategy guided by the Segment Anything Model (SAM) to retain reliable pseudo labeled samples given the spatial and semantic consistency of HSI regions. Experiments show promising performance of proposed model on high-resolution HSIs compared to trending methods under limited supervision.