Hyperspectral image (HSI) restoration tasks including super-resolution, denoising, and inpainting, present significant challenges due to intrinsic spectral-spatial coupling and limited training data availability. Recent advances in RGB image restoration demonstrate that models pretrained on large-scale datasets acquire exceptional generalization capabilities, suggesting potential cross-modal knowledge transfer solutions for HSI recovery. However, existing approaches exhibit two critical limitations: 1) prohibitive computational costs from mandatory fine-tuning procedures, and 2) inadequate cross-modal adaptation causing spectral distortions. To address these challenges, we propose a Two-Stage Cross-Modal Decoupling Network (CMDN) achieves spectral-faithful HSI restoration without fine-tuning the pretrained RGB prior; instead, we perform unsupervised test-time learning only on a lightweight spectral rectifier for sample-specific spectral calibration. Our methodology introduces two fundamental innovations: First, we develop a theoretically grounded framework using Singular Value Decomposition (SVD) to decouple HSIs into orthogonal spatial coefficients and spectral bases. This decomposition enables strategic reconfiguration of spatial coefficients into pseudo-RGB formats through band reorganization, facilitating direct deployment of frozen RGB-pretrained models for spatial textures recovery while preserving spectral integrity. Second, we propose a Physics Motivated Spectral Rectifier (PMSR) that dynamically adjusts spectral reconstruction weights using spatial gradient priors, correcting spectral deviations through physics-consistent optimization rather than explicit error modeling, thereby achieving superior spectral fidelity. Comprehensive experiments confirm our method's superiority in both spatial reconstruction accuracy and spectral consistency over state-of-the-art techniques. Code is available at: https://github.com/QYo-Liu/CMDN.

Contrastive learning achieves good performance on hyperspectral image classification (HSIC), but its application on cross-scene classification is still challenging due to domain shift. The emergence of domain adaptation (DA) techniques can reduce domain discrepancy and transfer a model between two domains. Recently, instance-level contrast adaptation methods can connect two related domains, and domain-invariant features are extracted. However, it is sensitive to noisy samples and only learns low-level discriminative features. To solve these problems, a novel domain invariant and compact prototype contrast adaptation (DIC-proCA) framework is proposed for HSIC. About the proposed DIC-proCA, the prototype is introduced into the contrastive learning framework, which serves as a representative embedding of semantically similar samples, has class representativeness and can alleviate the negative impact of outliers. Taking into account the class representativeness of the prototype and the discriminability of the sample itself, a bidirectional inter-domain instance-to-prototype contrastive loss is proposed. It explicitly expresses feature relationships between categories in different domains, and then extracts domain-invariant features. Meanwhile, the mining of compact discriminative features within the target domain is facilitated by instance-level contrastive learning after data augmentation. In addition, the strategy of label smoothing promotes the clusters in the domain to be more compact and evenly separated, making the model more generalizable. Three cross-scene HSIC tasks demonstrate that the proposed DIC-proCA exhibits superior performance compared to some advanced DA algorithms.

In recent years, few-shot learning (FSL) has made significant progress in hyperspectral image classification (HSIC) by transferring metaknowledge from a labeled source domain to a target domain with very limited labeled samples. Considering that natural images have rich spatial texture information, heterogeneous FSL (HFSL) by using natural images as the source domain and hyperspectral image (HSI) as the target domain has shown excellent performance. However, some problems also exist in the HFSL, such as poor generalization ability from natural images to HSIs, prototype instability due to limited labeled samples, and domain shift between different types of images. To address these problems, we propose a mutual information enhancement FSL (MIEFSL) method for HSIC, which mainly contains three modules, i.e., mutual information enhancement (MIE), intradomain prototype rectification (IPR), and interdomain distribution alignment (IDA). In order to improve the generalization ability of the network and preserve the raw data information as much as possible, an MIE module is designed to maximize the mutual information (MI) between the support set samples and their corresponding masked samples. To stabilize the prototypes, an IPR module is constructed through a distribution expansion strategy. In addition, to alleviate domain shifts between different types of images, an IDA is performed between source and target domains. Experimental results demonstrate that the proposed MIEFSL outperforms existing state-of-the-art FSL methods and achieves the overall accuracy (OA) of 78.34%, 90.31%, and 91.72% on Indian Pines (IP), University of Pavia (UP), and Salinas (SA) in the case of only five labeled samples, respectively.

Deep learning has been extensively used for hyperspectral image (HSI) classification with significant success, but the classification of high-dimensional HSI datasets with a limited amount of labeled samples is still a great challenge. Few-shot learning (FSL) has shown excellent performance in solving small-sample classification problems. However, most of the existing FSL methods usually suffer from the prototype instability and domain shift. In order to address these problems, this paper proposes a category-specific prototype self-refinement contrastive learning (CPSRCL) method for cross-domain FSL of HSIs. Our method uses a supervised contrastive learning (SCL) strategy to promote intra-class compactness and inter-class dispersion of features in the metric space. To stabilize and refine the prototypes of the support set, a category-specific prototype self-refinement (CSPSR) module is designed to adaptively learn different updating rules for different category prototypes using rich labeled information in the query set. Furthermore, a local discriminative domain adaptation (LDDA) method is constructed to align the global distribution between source and target domains while preserving domain-specific discriminative information. Experimental results on four public HSI datasets demonstrate that CPSRCL outperforms existing FSL and deep learning methods for HSI classification.

Recently, prototypical network based few-shot learning (FSL) has been introduced for small-sample hyperspectral image (HSI) classification and shown good performance. However, existing prototypical-based FSL methods have two problems: prototype instability and domain shift between training and testing datasets. To solve these problems, we propose a refined prototypical contrastive learning network for few-shot learning (RPCL-FSL) in this paper, which incorporates supervised contrastive learning and FSL into an end-to-end network to perform small-sample HSI classification. To stabilize and refine the prototypes, RPCL-FSL imposes triple constraints on prototypes of the support set, i.e., contrastive learning (CL), self-calibration (SC) and cross-calibration (CC) based constraints. The CL module imposes internal constraint on the prototypes aiming to directly improve the prototypes using support set samples in the CL framework, and the SC and CC modules impose external constraints on the prototypes by using the prediction loss of support set samples and the query set prototypes, respectively. To alleviate domain shift in the FSL, a fusion training strategy is designed to reduce the feature differences between training and testing datasets. Experimental results on three HSI datasets demonstrate that the proposed RPCL-FSL outperforms existing state-of-the-art deep learning and FSL methods.

Cross-scene hyperspectral image classification (HSIC) is a challenging topic in remote sensing, especially when there are no labels in target domain. Domain adaptation (DA) techniques for cross-scene HSIC aim to label a target domain by associating it with a labeled source domain. Most existing DA methods learn domain-invariant features by reducing feature distance across domains. Recently, contrastive learning has shown excellent performance in computer vision tasks, but there is little or no research on the performance of cross-scene HSIC. Considering that its idea is similar to reducing feature distance, this paper attempts to explore whether contrastive learning can achieve cross-scene HSIC. In this work, an instance-to-instance contrastive learning framework based on category matching (CLCM) is designed. The main idea is to take the category information as the premise in the feature space, regard the source sample as an anchor, and find its positive and negative matching samples across domains. The instance-level discriminative feature embeddings are learned through positive matching pairs attracting each other and negative matching pairs repelling each other. Among them, the target label is a pseudo-label. To further improve the quality of contrastive learning, it is considered to focus on extracting the spectral-spatial features of HSI to more accurately represent semantic information. Simultaneously, high-confidence target samples are screened to update the network. Three DA tasks confirm the effectiveness and feature discriminativeness of CLCM, while also providing new ideas for cross-scene image classification.