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Deep learning-based unsupervised domain adaptation (UDA) has shown potential in cross-scene hyperspectral image (HSI) classification. However, existing methods often experience reduced feature discriminability during domain alignment due to the difficulty of extracting semantic information from unlabeled target domain data. This challenge is exacerbated by ambiguous categories with similar material compositions and the underutilization of target domain samples. To address these issues, we propose a novel masked self-distillation domain adaptation (MSDA) framework, which enhances feature discriminability by integrating masked self-distillation (MSD) into domain adaptation. A class-separable adversarial training (CSAT) module is introduced to prevent misclassification between ambiguous categories by decreasing class correlation. Simultaneously, CSAT reduces the discrepancy between source and target domains through biclassifier adversarial training. Furthermore, the MSD module performs a pretext task on target domain samples to extract class-relevant knowledge. Specifically, MSD enforces consistency between outputs generated from masked target images, where spatial-spectral portions of an HSI patch are randomly obscured, and predictions are produced based on the complete patches by an exponential moving average (EMA) teacher. By minimizing consistency loss, the network learns to associate categorical semantics with unmasked regions. Notably, MSD is tailored for HSI data by preserving the samples’ central pixel and the object to be classified, thus maintaining class information. Consequently, MSDA extracts highly discriminative features by improving class separability and learning class-relevant knowledge, ultimately enhancing UDA performance. Experimental results on four datasets demonstrate that MSDA surpasses the existing state-of-the-art UDA methods for HSI classification. The code is available at https://github.com/Li-ZK/MSDA-2024.

Recently, metric-based few-shot learning (FSL) methods have achieved good performance in the hyperspectral image (HSI) classification. However, existing methods suffer from two problems: over-reliance on image modality information leads to inaccurate prototype representation, where a prototype refers to the centroid of each class in the dataset, and the impact of redundant and noisy pixels on model discriminability is rarely considered. These problems result in insufficient discriminability of the model for the target domain. To address the above issues, we propose a cross-domain few-shot HSI classification framework with cross-modal alignment and supervised contrastive learning (CDFS-CASCL). It is well known that human visual learning greatly benefits from the input of various modal information such as vision, language, and video. Inspired by the way humans abstract image class concepts in language form and understand the essence of classes, we perform cross-modal alignment (CA) between similar image and text prototypes and use abstract text semantics to guide the model to learn semantic-related features with good generalization ability in images, so as to improve the accuracy of image prototypes representation of the prototypes. In addition, through supervised contrastive learning (SCL) based on neighborhood pixel mask in the target domain, the enhanced sample features belonging to the same class are closer, while the enhanced sample features belonging to different classes are pulled further, enabling the model to learn mask-robust discriminative feature representations, suppressing the negative impact of redundant and noisy pixels, and improving the model’s discriminability. The experimental results demonstrate the superiority of the proposed CDFS-CASCL. The code is available at https://github.com/Li-ZK/CDFS-CASCL-2024.

Recently, few-shot learning (FSL) has been introduced for hyperspectral image (HSI) classification with few labeled samples. However, existing FSL-based HSI classification methods mainly focus on the meta-knowledge transfer between HSIs. Compared with HSIs, natural images have sufficient annotated data. To utilize natural images (base class data) to achieve accurate classification of HSIs (novel class data), we propose a novel few-shot classification framework with SSL (FSCF-SSL) for HSIs in this article. The orientation of objects in natural images is relatively unitary, whereas the objects of image patches for each pixel in HSIs have diverse orientations in the spatial domain. To make better use of base classes, we design an SSL with geometric transformations (SSLGTs), which sets rotation labels as supervision to extract low-level features that can better represent diverse orientations, and then conduct SSLGT and FSL on base classes to learn transferable spatial meta-knowledge. Next, a spectral-spatial feature extraction network is carefully designed to better utilize the spatial and spectral information of HSIs, where the weights of the first seven layers of the spatial part are initialized by the weights of the corresponding layers trained on base classes. Finally, to fully explore the few annotated data from novel classes, we design an SSL with contrastive learning (SSLCL) that can mine the category-invariant features contained in the novel class data itself, and then perform SSLCL and FSL on novel classes to learn more discriminative individual knowledge. Experimental results on four HSI datasets show that FSCF-SSL offers a significant improvement over state-of-the-art methods. The code is available at https://github.com/Li-ZK/FSCF-SSL-2023.