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.

Accurate segmentation of very high-resolution remote sensing images is vital for downstream tasks. Most semantic segmentation methods fail to fully consider the inherent characteristics of the images, such as intricate backgrounds, significant intraclass variance, and spatial interdependence of geographic object distribution. To address these challenges, we propose an efficient global–local scene awareness network with rotary position embedding (RoGLSNet). Specifically, we introduce the dynamic global filter (DGF) module to adaptively select frequency components, thereby mitigating interference from background noise. For high intraclass variance, the class center aware block (CCAB) performs class-level contextual modeling with spatial information integration. Additionally, the rotary position embedding (RoPE) is incorporated into vanilla attention to indirectly model the positional and distance relationships of geographic target objects. Extensive experimental results on two widely used datasets demonstrate that RoGLSNet outperforms the state-of-the-art (SOTA) segmentation methods. The code is available athttps://github.com/bai101315/RoGLSNet

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.

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 athttps://github.com/Li-ZK/MSDA-2024.

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.

Hyperspectral image (HSI) has hundreds of continuous bands that contain a lot of redundant information. Besides, a spatial patch of a hyperspectral cube often contains some pixels different from the center pixel category, which are usually called interference pixels. The existence of such interference pixels has a negative effect on extracting more discriminative information. Therefore, in this letter, a multiattention fusion network (MAFN) for HSI classification is proposed. Compared with the current state-of-the-art methods, MAFN uses band attention module (BAM) and spatial attention module (SAM), respectively, to alleviate the influence of redundant bands and interfering pixels. In this way, MAFN realizes feature reuse and obtains complementary information from different levels by combining multiattention and multilevel fusion mechanisms, which can extract more representative features. Experiments were conducted on two public HSI data sets to demonstrate the effectiveness of MAFN. Our source code is available athttps://github.com/Li-ZK/MAFN-2021.

Cross-domain hyperspectral image classification is one of the major challenges in remote sensing, especially for target domain data without labels. Recently, deep learning approaches have demonstrated effectiveness in domain adaptation. However, most of them leverage unlabeled target data only from a statistical perspective but neglect the analysis at the instance level. For better statistical alignment, existing approaches employ the entire unevaluated target data in an unsupervised manner, which may introduce noise and limit the discriminability of the neural networks. In this article, we propose confident learning-based domain adaptation (CLDA) to address the problem from a new perspective of data manipulation. To this end, a novel framework is presented to combine domain adaptation with confident learning (CL), where the former reduces the interdomain discrepancy and generates pseudo-labels for the target instances, from which the latter selects high-confidence target samples. Specifically, the confident learning part evaluates the confidence of each pseudo-labeled target sample based on the assigned labels and the predicted probabilities. Then, high-confidence target samples are selected as training data to increase the discriminative capacity of the neural networks. In addition, the domain adaptation part and the confident learning part are trained alternately to progressively increase the proportion of high-confidence labels in the target domain, thus further improving the accuracy of classification. Experimental results on four datasets demonstrate that the proposed CLDA method outperforms the state-of-the-art domain adaptation approaches. Our source code is available athttps://github.com/Li-ZK/CLDA-2022.

Hyperspectral image (HSI) classification has drawn increasing attention recently. However, it suffers from noisy labels that may occur during field surveys due to a lack of prior information or human mistakes. To address this issue, this article proposes a novel dual-channel residual network (DCRN) to resolve HSI classification with noisy labels. Currently, the influence of noisy labels is reduced by simply detecting and removing those anomalous samples. Different from such a specifically designed noise cleansing method, DCRN is easy to implement but highly effective. It enhances its model robustness to noisy labels to a great extent by employing a novel dual-channel structure and a noise-robust loss function. In this way, DCRN can mitigate influence from noisy labels while fully utilizing useful information from mislabeled samples for augmented training. Experiments are conducted on several hyperspectral data sets with manually generated noisy labels to demonstrate its excellent performance. The code is available athttps://github.com/Li-ZK/DCRN-2021.

A lightweight intelligent vehicle system was developed to realize autonomous driving, face recognition, face anti-spoofing, remote control, infrared obstacle avoidance and other functions to improve the security of contactless delivery. In this system, BCM2711 was used as kernel control chip, and it was equipped with deep network learning models such as LaneNet, ResNet and LSTM. It had been proved that this system could realize the above functions and achieve real-time effects, thus gaining great economic value and market space in contactless delivery service.

An improved NS traffic flow model was built in this paper to simulate two safety factors of vehicle-pedestrian avoidance and vehicle-vehicle avoidance under different weather conditions. Then the regulations of changes on lanes and vehicle speed under two-lane conditions were optimized as well as the improved NS model based on cellular automata. Results showed that the improved NS model can predict road conditions effectively, thereby improving the safety of roads.