Recent studies have shown that deep domain adaptation techniques perform excellently in cross-domain hyperspectral image classification. However, these methods typically assume that the source domain and the target domain share the same class set, while in practice, the target domain may include unknown classes, and direct alignment can result in negative transfer. Moreover, in hyperspectral image classification based on deep learning, using the label of the central pixel to represent the label of the image patch may lead to feature bias due to the uncertainty of the labels of neighboring pixels, thereby reducing the generalization performance of the model. To address this, this paper proposes an open-set domain adaptation framework, including a Pixel-Aware Weight Learning (PAWL) module and a Decoupled Dual Alignment (DDA) strategy. The PAWL module effectively reduces the feature bias caused by inconsistency in neighboring pixel labels by analyzing the uncertainty of neighboring pixel labels and utilizing adaptive weight learning, thereby improving recognition performance in open-set environments. The DDA strategy decouples the features of the source domain and target domain into known and unknown classes and aligns them separately to mitigate negative transfer. Experiments on two cross-scene hyperspectral datasets validated the effectiveness of the method.

Closed Set Domain Adaptation (CSDA) assumes identical class sets between source and target domains and is an important solution for reducing domain bias. Compared to CSDA, Open Set Domain Adaptation (OSDA) is closer to realworld applications by allowing unknown class samples in the target domain. In addition, previous OSDA methods mainly rely on similarity detection between the target and source domains to identify unknown classes, which does not fully capture the characteristics of the target domain. To address these limitations, this letter proposes an open set domain adaptation method integrating entropy-guided weighted adversarial networks and contrastive self-supervised learning for hyperspectral image (HSI) classification. The approach introduces an entropy-guided weighted adversarial network to distinguish between known and unknown classes in the target domain, while weighing their importance for aligning the feature distributions. Contrastive self-supervised learning is introduced to learn the intrinsic structure and discriminative features of the target domain from unlabeled target domain data. Experimental validation on two HSI cross-domain datasets demonstrates significant performance improvements over existing methods.

With the rapid advancement of hyperspectral imaging technology, there has been a dramatic surge in the volume of hyperspectral data, presenting unprecedented challenges for the incremental classification of hyperspectral images (HSI). In cross-scene HSI incremental classification, two critical challenges emerge: (1) the effectiveness of traditional sample replay methods is limited by high-dimensional data storage and privacy protection constraints; and (2) diverse scenes lead to catastrophic forgetting during new task learning. To address these challenges, this paper proposes an Incremental Classification algorithm based on Dual Constraints and Knowledge Transfer (IC-DCKT) for cross-scene hyperspectral images. First, to overcome the limitations of traditional replay methods in data storage and privacy protection, IC-DCKT innovatively introduces a sample-free storage approach. By combining regularization with knowledge distillation techniques, it achieves efficient knowledge transfer and retention. Second, to effectively mitigate catastrophic forgetting, the algorithm implements a dual-constraint mechanism: the approximate Null Space Projection Constraint (NSPC) restricts gradient update directions to preserve historical task feature distributions, while the Cosine Similarity Distillation Constraint (CSDC) enforces feature alignment between old and new models, significantly enhancing the model’s ability to retain knowledge of old tasks. Finally, this paper pioneers the integration of the pretrained hyperspectral large model HyperSIGMA into an incremental learning framework. The Feature Alignment Loss (FAL) not only improves the speed of new task learning but also compensates for the loss of critical information in new tasks caused by gradient update direction constraints imposed by NSPC. Experimental results on three hyperspectral datasets demonstrate that the proposed IC-DCKT method outperforms existing state-of-the-art incremental learning approaches.

Meta-learning has demonstrated significant potential in addressing the limited annotation data challenge in hyperspectral target detection. However, existing meta-learningbased methods face two major challenges: 1) weak inter-task correlation leading to unstable optimization directions, and 2) meta-knowledge adaptation based on single prior spectral information fails to effectively characterize spectral variation properties of targets in new scenarios. To overcome these challenges, this letter proposes a cooperative meta-learning framework with spectral diversity adaptation for hyperspectral target detection. The framework introduces a co-learner through cooperative learning to dynamically capture cross-task knowledge and stabilize optimization directions, while designing a spectral diversity-based meta-knowledge adaptation strategy to enhance the model’s ability to understand the spectral variation characteristics of targets in new scenarios and precisely distinguish the spectral features between targets and backgrounds. Experimental results on two public datasets demonstrate that the proposed method outperforms state-of-the-art hyperspectral target detection algorithms. The code is available at https://github.com/Li-ZK/CMLSDA.

Recently, few-shot learning (FSL) has shown promising results in hyperspectral image (HSI) classification. However, in practical applications, insufficient labeled training data makes it difficult to capture the intra-class variation of novel classes, making it challenging for the model to learn inaccurate feature distributions, which in turn leads to inaccurate decision boundaries. To solve this problem, we propose an entropy-driven clustering and semantic association framework (ECSA-FSL). We design a deep semantic association feature enhancement module (FEA), which first explores the potential semantic relationship between the source and target domains, and then constructs a cross-domain feature enhancement strategy to generate more discriminative features. In addition, we employ an entropy-driven clustering mechanism (EDC) to optimize the feature space distribution of the target domain. Our approach achieves remarkable classification accuracy with a small number of samples, particularly excelling in scenarios with high intra-class variability and limited training data. Experiments on two publicly available HSI datasets confirm that ECSA-FSL significantly outperforms existing few-shot learning methods under similar conditions. The code is available at https://github.com/Li-ZK/ECSA-FSL-2025.

Recent studies have shown that the deep domain adaptation (DA) technique has achieved remarkable results in cross-domain hyperspectral image (HSI) classification task. However, these DA methods assume that the source and target domains share the same classes, which may not hold true in real-world applications. Under open-set conditions, since the target domain may contain classes unseen in the source domain, direct domain alignment can lead to negative transfer phenomena. Moreover, the presence of multiple unknown classes in the target domain makes it difficult to learn more discriminative classification boundaries between known and unknown classes. To address these issues, we propose an open-set DA (OSDA) method for HSI classification based on weighted generative adversarial networks and dynamic thresholding (WGDT). First, we introduce a class anchor (CA) strategy to learn the metric space of known classes in the source domain. By calculating the similarity between the target-domain samples and the CA, we compute the reliability weights of the samples belonging to known classes. Then, based on these weights, we design an instance-level weighted-domain adversarial learning strategy to better align samples that are more likely to belong to known classes, avoiding negative transfer phenomena. Finally, we propose a dynamic thresholding method to learn the classification boundaries between known and unknown classes in the feature space and reject unknown class samples, thereby separating known class samples in the target domain. The experimental results on four cross-scene HSI classification tasks demonstrate that our proposed method outperforms some existing methods. The code is available athttps://github.com/Li-ZK/WGDT.

With the rapid growth in satellite imagery acquisition and decreasing revisit intervals, efficient on-orbit processing of hyperspectral data has become critical due to limited onboard computing resources. In this context, lifelong learning (LLL) offers a promising solution to enable continuous learning from new data without storing all previous data or retraining from scratch. However, the plasticity-stability dilemma remains a significant challenge, particularly in hyperspectral image (HSI) classification under class-incremental scenarios. To address this, we propose a novel network architecture that integrates contrastive learning and an angular penalty loss. The contrastive learning module facilitates adaptive knowledge fusion, enabling the model to effectively incorporate new information while preserving prior knowledge. The angular penalty loss allows the classifier to dynamically expand for new classes while maintaining discrimination between old and new categories. Together, these components ensure robust knowledge retention, transfer, and adaptability. Experimental results on three benchmark hyperspectral datasets demonstrate that our method significantly outperforms existing approaches, highlighting its efficacy in addressing LLL challenges in HSI classification. The code is available athttps://github.com/Li-ZK/LLL-AFCA.

Recently, metric-based few-shot learning (FSL) methods have achieved good performance in 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.

Although hyperspectral image (HSI) classification has made great progress, most classification methods assume that the training and test data have the same class, and that there are no classes in the test data that are not present in the training data. As a result, unknown classes are ignored during model building, which requires the use of open-set classification (OSC) methods to reject unknown classes. However, the current OSC methods do not consider the constraints during feature learning, which can lead to the problem that the feature spaces of known and unknown classes may tend to be consistent. To ensure the discriminability of the feature space and improve the accuracy of the OSC, we propose a novel open-set HSI classification framework based on supervised contrastive learning (OSC-SCL). By adding SCL to spectral and spatial feature learning respectively, not only samples in the same class can be pulled closer, but also unknown classes can be distinguished from known classes. We also introduce a class anchor-based clustering strategy, which can effectively reject unknown classes while ensuring that known classes are correctly classified. Our method is validated on two HSI datasets and outperforms existing state-of-the-art methods.

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 athttps://github.com/Li-ZK/FSCF-SSL-2023.

Deep domain adaptation has achieved promising results in cross-domain hyperspectral image (HSI) classification. However, existing methods often focus on aligning data distributions without sufficient consideration of separability of source and target domain data themselves. In addition, current adversarial domain adaptation methods aim to achieve similar distributions between domains by confusing the discriminator, rather than obtaining a more compact distribution. In particular, existing methods are not discriminative enough for the target domain due to the difficulty of obtaining high-confidence labeled samples of the target domain. To address the above challenges, we propose a supervised contrastive learning-based unsupervised domain adaptation for HSI classification. A supervised contrastive learning strategy is then performed in both the source and target domains, which allows samples from the same category to be pulled closer together and samples from different categories to be pushed further apart, thus enhancing the separability of the data within the domain. The domain adaptation task is treated as a one-class classification (OCC) task, and a novel domain similarity loss based on OCC is introduced to reduce the discrepancy between domains. Finally, a confidence learning-based sample selection strategy is designed to select high-confidence labeled samples from the target domain to fine-tune the domain adaptation model, which can enhance the discrimination of the model to the target domain. Experimental results on three cross-domain datasets demonstrate that our proposed method outperforms existing domain adaptation methods. Our source code is available at https://github.com/Li-ZK/SCLUDA-2023.

Deep learning has achieved great success in hyperspectral image (HSI) classification. However, its success relies on the availability of sufficient training samples. Unfortunately, the collection of training samples is expensive, time-consuming, and even impossible in some cases. Natural image datasets that are different from HSI, such as Image Net and mini-ImageNet, have abundant texture and structure information. Effective knowledge transfer between two heterogeneous datasets can significantly improve the accuracy of HSI classification. In this letter, heterogeneous few-shot learning (HFSL) for HSI classification is proposed with only a few labeled samples per class. First, few-shot learning is performed on the mini-ImageNet datasets to learn the transferable knowledge. Then, to make full use of the spatial and spectral information, a spectral–spatial fusion network is devised. Spectral information is obtained by the residual network with pure 1-D operators. Spatial information is extracted by a convolution network with pure 2-D operators, and the weights of the spatial network are initialized by the weights of the model trained on the mini-ImageNet datasets. Finally, few-shot learning is fine-tuned on HSI to extract discriminative spectral–spatial features and individual knowledge, which can improve the classification performance of the new classification task. Experiments conducted on two public HSI datasets demonstrate that the HFSL outperforms the existing few-shot learning methods and supervised learning methods for HSI classification with only a few labeled samples. Our source code is available athttps://github.com/Li-ZK/HFSL.

This paper proposes a simple, yet very powerful local face representation, called the Gradient Orientations and Euler Mapping (GOEM). GOEM consists of two stages: gradient orientations and Euler mapping. In the first stage, we calculate gradient orientations of a central pixel and get the corresponding orientation representations by performing convolution operator. These representation results display spatial locality and orientation properties. To encompass different spatial localities and orientations, we concatenate all these representation results and derive a concatenated orientation feature vector. In the second stage, we define an explicit Euler mapping which maps the space of the concatenated orientation into a complex space. For a mapping image, we find that the imaginary part and the real part characterize the high frequency and the low frequency components, respectively. To encompass different frequencies, we concatenate the imaginary part and the real part and derive a concatenated mapping feature vector. For a given image, we use the two stages to construct a GOEM image and derive an augmented feature vector which resides in a space of very high dimensionality. In order to derive low-dimensional feature vector, we present a class of GOEM-based kernel subspace learning methods for face recognition. These methods, which are robust to changes in occlusion and illumination, apply the kernel subspace learning model with explicit Euler mapping to an augmented feature vector derived from the GOEM representation of face images. Experimental results show that our methods significantly outperform popular methods and achieve state-of-the-art performance for difficult problems such as illumination and occlusion-robust face recognition.