Unsupervised domain adaptation methods can effectively mitigate the spectral drift in cross-scene hyperspectral image classification. Among them, adversarial training methods are particularly noteworthy due to their outstanding performance. However, due to the inherent mechanism of adversarial training, these methods suffer from continuing training instability and limited classifier generalizability. To overcome these limitations, this letter proposes a two-stage domain adaptation (TSDA) framework that incorporates self-distillation and test-time adaptation. The self-distillation strategy promotes stability during adversarial training by improving training consistency across iterations. Specifically, each batch includes a subset of data from the previous iteration, and self-distillation ensures that the output of this subset in the current iteration is consistent with the previous one. This mechanism stabilizes gradient computations during the training process, facilitating more robust parameter updates. Subsequently, the test-time adaptation module utilizes a limited set of unlabeled target domain samples to refine the classifier. During this stage, a confident learning module identifies and selects high-confidence pseudo-labels to optimize the classifier, enhancing its generalizability in the target domain. Thus, TSDA facilitates domain adaptation during both the training and testing stages. Experimental results on two cross-domain datasets demonstrate the effectiveness of the proposed method. The code is available at https://github.com/Li-ZK/TSDA-2025.

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.

Hyperspectral target detection faces challenges due to limited labeled data and high spectral similarity across material samples, making feature extraction difficult. Existing methods for spatial-spectral feature learning are often computationally constrained. To address these issues, we propose a framework combining unsupervised spectral contrastive learning and spatial-spectral knowledge distillation (UCLKD). Our approach uses an unsupervised contrastive learning module to enhance spectral feature representation while preventing model collapse. A spatial-spectral knowledge distillation strategy then leverages features from a hyperspectral Foundation Model to guide the spectral feature extraction network, effectively capturing discriminative spectral features. Extensive experiments demonstrate that UCLKD outperforms existing methods in target detection performance. The code is available at https://github.com/Li-ZK/UCLKD.

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.

Deep learning can effectively extract latent information from data to enhance target-background separation in hyperspectral target detection (HTD). However, these models typically require extensive labeled samples, while available target spectra in hyperspectral images (HSI) are scarce. Additionally, existing deep models struggle with target detection in complex backgrounds due to subtle spectral differences. To address these issues, we propose a novel HTD method based on diffusion model and convolutional gated linear unit (HTD-DMCG). First, the diffusion model is integrated with MixUp for data augmentation to generate a diverse and sufficiently large sample set. Next, a Transformer architecture utilizing a convolutional gated linear unit is designed to effectively capture global dependencies and local feature correlations, leading to more discriminative feature representations. Additionally, a new target aggregation and background separation loss is introduced, which emphasizes target sample aggregation while increasing the distance between targets and background samples to enhance separability. The HTD-DMCG method is compared against classical and state-of-the-art HTD methods on four real HSI datasets. Extensive experiments show that it can effectively outperform existing methods in target detection performance. The code is available at https://github.com/Li-ZK/HTD-DMCG.