Seeing through the noise: A cross-modal guided framework for hyperspectral image classification under multi-type degradations

Recent advances in deep learning and multimodal data fusion technologies have significantly enhanced hyperspectral image (HSI) classification performance. Nevertheless, classification accuracy of hyperspectral data continues to degrade substantially under diverse degradation scenarios, such as noise interference, spectral distortion, or reduced resolution. To robustly address this challenge, this paper proposes a novel cross-modal guided classification framework that integrates active remote sensing data (e.g., LiDAR) to improve classification resilience under degraded conditions. Specifically, we introduce a Cross-Modal Feature Pyramid Guidance (CMFPG) module, which effectively utilizes cross-modal information across multiple levels and scales to guide hyperspectral feature extraction and fusion, thereby enhancing modeling stability in degraded environments. Additionally, we develop the HyperGroupMix module, which enhances cross-domain adaptability through grouping spectral bands, extracting statistical features, and transferring features across samples. Experimental results conducted under complex degradation conditions demonstrate that our proposed method exhibits stable high-level classification accuracy and robustness in overall performance. The code is accessible at: https://github.com/miliwww/CMGF