Bidirectional Stacking Ensemble Curriculum Learning for Hyperspectral Image Imbalanced Classification With Noisy Labels

Hyperspectral imaging has demonstrated substantial advantages in enhancing classification performance in remote sensing applications due to its abundant spectral information. To address the challenges of label noise and class imbalance in hyperspectral image (HSI) classification, we propose an end-to-end Feature-Guided Network (FGN) for HSI. Instead of merely combining spatial and channel attention, FGN leverages feature-level attention interactions to enhance contextual understanding, leading to better feature extraction, especially for underrepresented classes. Furthermore, a bidirectional loss for curriculum learning (CL) is proposed to rank the HSI training data in a descending or ascending order. The top and bottom loss regularizers are designed to make the proposed model suitable for noisy and imbalanced HSI data distributions. In the phase of selecting pace parameter, a stacking ensemble curriculum learning (SECL) model is established to avoid that the outliers and noisy HSI data are involved into the CL training process. A novel instruction matrix based on sample weights is designed for base classifiers. The outputs of the base models, combined with the expected labels, form the input-output pairs for training the second-level classifier. Experiments conducted on multiple hyperspectral imbalanced datasets with noisy labels demonstrate the superior performance of our method.