Due to the complex acquisition environment and scarcity of labels, domain adaptation (DA) techniques are widely applied to cross-scenario hyperspectral image (HSI) classification to achieve more precise labeling. Many existing approaches mainly rely on convolutional neural networks (CNNs) to capture local spatial contextual relationships, supplemented by graph convolutional networks (GCNs) for long-range modeling. However, GCNs usually require full batch training and fixed initial graph structures, which significantly limits the exploration of topological structures. To address this, a multi-contrastive and dynamic topological matching network (MCDTM) is introduced to accomplish cross-domain HSI classification. Unlike fixed graph construction methods, mini-batches of samples are utilized to construct dynamic subgraphs within the source and target domains, respectively, with locally extracted features from CNNs serving as the basis for graph construction. More importantly, as the model is optimized and the domain gap narrows, the dynamic graph structure is adaptively adjusted according to the evolving samples, thereby boosting the accuracy of the graph and enhancing the discriminative power of the model. Moreover, the integration of weighted multi-positive contrastive learning and graph matching achieves distribution alignment and graph alignment, enhancing the model's capacity to distinguish and align complex patterns in HSI. Experimental results in three tasks show that the MCDTM surpasses several advanced DA methods, achieving impressive accuracies of 80.10%, 70.01%, and 94.17% on the Houston, HyRank, and YC-YC tasks, thereby showcasing its superior performance.

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.

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.

Domain adaptation (DA) aims to use a well-labeled source domain to predict the labels of the unlabeled or poor-labeled target domain. Most of the existing DA methods focus on the use of feature-level or sample-level information. Recent studies have shown that domain discriminative information is also important for classification. To jointly exploit feature-level information and discriminative information, a new DA method called distribution alignment and discriminative feature learning (DADFL) is proposed for hyperspectral image (HSI) classification in this letter. DADFL incorporates category-discriminative information preservation and structured prediction (SP)-based pseudolabeling into a unified framework to simultaneously reduce distribution and subspace differences between domains. Experimental results on three hyperspectral DA tasks show that the classification performance of the proposed DADFL is better than that of existing DA methods.

Recent studies have shown that deep domain adaptation (DA) techniques have good performance on cross-domain hyperspectral image (HSI) classification problems. However, most existing deep HSI DA approaches directly use deep networks to extract features from the data, which ignores the detailed information of HSI in spectral and spatial dimensions. To effectively exploit the spectral–spatial joint information for DA of HSIs, we propose a two-branch attention adversarial DA (TAADA) network in this article. In the TAADA network, a two-branch feature extraction (TBFE) subnetwork is first designed as a generator to extract the attention-based spectral–spatial features. Then, a discriminator based on two classifiers with the multilayer FC-BN-ReLU-Dropout structure is constructed. Based on adversarial learning between the generator and the discriminator, the ability of discriminative feature extraction and cross-domain classification is improved simultaneously. Finally, the TAADA network can adjust the distribution between the source and target domains and extract domain-invariant features. Experimental results on three cross-scene HSI classification tasks show that our proposed TAADA outperforms some existing DA methods.

Genetic risk score (GRS, also known as polygenic risk score) analysis is an increasingly popular method for exploring genetic architectures and relationships of complex diseases. However, complex diseases are usually measured by multiple correlated phenotypes. Analyzing each disease phenotype individually is likely to reduce statistical power due to multiple testing correction. In order to conquer the disadvantage, we proposed a principal component analysis (PCA)-based GRS analysis approach. Extensive simulation studies were conducted to compare the performance of PCA-based GRS analysis and traditional GRS analysis approach. Simulation results observed significantly improved performance of PCA-based GRS analysis compared to traditional GRS analysis under various scenarios. For the sake of verification, we also applied both PCA-based GRS analysis and traditional GRS analysis to a real Caucasian genome-wide association study (GWAS) data of bone geometry. Real data analysis results further confirmed the improved performance of PCA-based GRS analysis. Given that GWAS have flourished in the past decades, our approach may help researchers to explore the genetic architectures and relationships of complex diseases or traits.

Genome-wide association study (GWAS)-based pathway association analysis is a powerful approach for the genetic studies of human complex diseases. However, the genetic confounding effects of environment exposure-related genes can decrease the accuracy of GWAS-based pathway association analysis of target diseases. In this study, we developed a pathway association analysis approach, named Mendelian randomization-based pathway enrichment analysis (MRPEA), which was capable of correcting the genetic confounding effects of environmental exposures, using the GWAS summary data of environmental exposures. After analyzing the real GWAS summary data of cardiovascular disease and cigarette smoking, we observed significantly improved performance of MRPEA compared with traditional pathway association analysis (TPAA) without adjusting for environmental exposures. Further, simulation studies found that MRPEA generally outperformed TPAA under various scenarios. We hope that MRPEA could help to fill the gap of TPAA and identify novel causal pathways for complex diseases.