Hyperspectral imaging (HSI) holds immense potential for medical diagnostics by capturing tissue-specific spectral signatures that facilitate precise disease detection. However, effective HSI classification in clinical settings is hindered by two main challenges: (i) the severe lack of labelled medical HSI samples constrains model training. Prototypical networks, as a few-shot learning paradigm, have been adopted to address label scarcity. However, current Euclidean-based prototypical methods typically assume equal feature variance and spherical distributions, while ignoring intraclass covariance and spectral correlations; (ii) significant domain shifts across heterogeneous medical HSI datasets undermine model generalisation, impair multi-domain interpretability, and force expensive per-dataset retraining. To overcome these limitations, we propose a novel distance-learning-based prototypical network with multi-domain adaptation for few-shot hyperspectral medical image classification. First, by embedding a class-covariance-aware Mahalanobis metric within the prototypical block, our module adapts similarity measures to each class's intrinsic spectral-spatial covariance and scale variations, thereby enhancing prototype robustness under severe label scarcity and significantly reducing misclassification compared with existing few-shot networks. Secondly, we introduce the domain-aware adapter block designed to address domain shift and multi-domain variability by dynamically fusing shared spectral-spatial representations with domain-specific characteristics via spectral integration and switchable adapters. We undertook extensive experiments on three publicly available hyperspectral medical datasets: skin dermoscopy, multidimensional choledochal, and in-vivo brain dataset. Compared to state-of-the-art classifiers, the proposed method achieved excellent performance on all three datasets, paving the way for generalisable HSI solutions in clinical workflows and biomedical research.

The integration of hyperspectral imaging (HSI) and Light Detection and Ranging (LiDAR) data provides complementary spectral and spatial information for remote sensing applications. While previous studies have explored the role of band selection and grouping in HSI classification, little attention has been given to how the spectral sequence—or band order—affects classification outcomes when fused with LiDAR. In this work, we systematically investigate the influence of band order on HSI-LiDAR fusion performance. Through extensive experiments, we demonstrate that band order significantly impacts classification accuracy, revealing a previously overlooked factor in fusionbased models. Motivated by this observation, we propose a novel fusion architecture that not only integrates HSI and LiDAR data but also learns from multiple band order configurations. The proposed method enhances feature representation by adaptively fusing different spectral sequences, leading to improved classification accuracy. Experimental results on the Houston 2013 and Trento datasets show that our approach outperforms state-of-the-art fusion models. Data and code are available at https://github.com/Judyxyang/HSLiNets.

The fusion of hyperspectral and LiDAR data has been an active research topic. Existing fusion methods have ignored the high-dimensionality and redundancy challenges in hyperspectral images, despite that band selection methods have been intensively studied for hyperspectral image (HSI) processing. This paper addresses this significant gap by introducing a cross-attention mechanism from the transformer architecture for the selection of HSI bands guided by LiDAR data. LiDAR provides high-resolution vertical structural information, which can be useful in distinguishing different types of land cover that may have similar spectral signatures but different structural profiles. In our approach, the LiDAR data are used as the “query” to search and identify the “key” from the HSI to choose the most pertinent bands for LiDAR. This method ensures that the selected HSI bands drastically reduce redundancy and computational requirements while working optimally with the LiDAR data. Extensive experiments have been undertaken on three paired HSI and LiDAR data sets: Houston 2013, Trento and MUUFL. The results highlight the superiority of the cross-attention mechanism, underlining the enhanced classification accuracy of the identified HSI bands when fused with the LiDAR features. The results also show that the use of fewer bands combined with LiDAR surpasses the performance of state-of-the-art fusion models.

Deep learning-based methods have greatly improved the performance of hyperspectral image classification over the past several years. Nevertheless, current deep learning methods require training and deploying an independent model for each hyperspectral data domain. Representations learned for one data domain can hardly be generalized to other data domains, so multiple models are needed in real-world applications when data from multiple domains are involved. In this paper, we design a single neural network that learns universal representations simultaneously from multiple hyperspectral remote sensing data domains. The universal convolutional neural network adapts its behaviour to different hyperspectral datasets. The majority of parameters of the network are shared to learn common knowledge from multiple datasets. A small number of domain-specific parameters are assigned to handle the domain shift. In addition, we propose a two-step training strategy to fully utilize the capacity of the universal network. Experiments conducted on seven hyperspectral image datasets demonstrate that the proposed universal network outperforms multiple individual specialized single domain networks.

Band selection is an effective means to alleviate the curse of dimensionality in hyperspectral data. Many methods select a compact and low redundant band subset, which is inadequate as it may degrade the classification performance. Instead, more emphasis shall be put on selecting representative bands. In this article, we propose a robust unsupervised band selection method to address this issue. Our method reveals bandwise representativeness based on the comprehensive interband neighborhood structure. It incorporates an interband neighborhood graph into a sparse self-contained regression model in order to provide a reasonable measure for bandwise representativeness. The derived coefficient matrix not only uncovers bandwise importance values but also is coherent to the generalized interband local neighborhood structure. For constructing the interband neighboring structural graph, an integrated multigraph model is employed to achieve better generalization performance. It combines the benefit of multiple graphs but is insusceptible to the defects of a single one. To enhance the reliability of this model, a joint trace minimum and nonnegative constraint is imposed on the coefficient matrix. Accordingly, a multigraph integrated embedding and robust self-contained regression model (MGRSR) is formulated. In addition, an iterative update algorithm is developed to solve the problem. Comparative experiments on three hyperspectral data sets illustrate that MGRSR is robust to various data and has superior performance compared with several state-of-the-art methods.

Hyperspectral (HS) imaging and light detection and ranging (LiDAR) are widely used in remote sensing to acquire data from a same area of earth surface. HS image and LiDAR data contain complementary information of the target objects. Jointly using these two data modalities has great potential in land cover classification. In recent years, deep learning based fusion methods demonstrated promising performance on this task. However, how to better model the relationship of heterogeneous features from HS and LiDAR and their importance for the classification remains a challenging task. In this paper, we propose a spectral and spatial residual attention network for HS and LiDAR fusion and classification. A spectral residual attention module and a spatial residual attention module are designed in the network for better feature learning and fusion. Experiments on widely adopted Houston dataset demonstrate the superiority of the proposed method.

Hyperspectral videos contain images with a large number of light wavelength indexed bands that can facilitate material identification for object tracking. Most hyperspectral trackers use hand-crafted features rather than deep learning generated features for image representation due to limited training samples. To fill this gap, this paper introduces a band attention aware ensemble network (BAE-Net) for deep hyperspectral object tracking, which takes advantages of deep models trained on color videos for feature representation. Specifically, an autoencoder-like band attention block is introduced to learn the dependencies among bands and generate band-wise weights. Guided by these weights, hyperspectral images are then divided into a number of three-channel images. These three-channel images are fed into a deep color tracking network, producing several weak trackers. Finally, weak trackers are fused using ensemble learning for target location. Experimental results on hyperspectral datasets show the effectiveness and advantages of the proposed deep hyperspectral tracker.

Hyperspectral imaging is widely used in remote sensing because of its capability to capture the detailed spectral reflection of the ground object. The acquired rich band information brings significant benefits to better discriminate the target pixels. However, this imaging method also introduces redundant and noisy bands which may lower the classification accuracy. In addition, the contribution of different bands towards the final classification task are not necessarily the same. Therefore, band weighting and band selection are often adopted to model the relationship among the bands and remove the irrelevant ones. Attention mechanism is a method in neural networks to guide the algorithm to focus on the important information. In this paper, we propose an attention based deep learning framework to achieve band weighting and selection. The experimental results on two hyperspectral image datasets show the effectiveness of the proposed framework.

This study investigates the security of the two‐way relaying system with untrusty relay nodes. Cooperative jamming schemes are considered for bi‐directional secrecy communications. The transmit power of each source node is divided into two parts corresponding to the user and jamming signals, respectively. Two different assumptions of the jamming signals are considered. When the jamming signals are a priori known at the two source nodes, closed‐form power allocation expressions at two source nodes are derived. Under the assumption of unknown jamming signals, it is proven that the cooperative jamming is useless for the system secrecy capacity, because that all the power should be allocated to the user signals at each source node. Relay selection is also investigated based on the analysis of cooperative jamming. Simulation results are presented to compare the system secrecy capacities under the two jamming signal assumptions.

The security of the two-way relaying system with an eavesdropper is investigated in this paper. A cooperative jamming and power allocation scheme is proposed to enhance the system secrecy capacity. Both user and pre-defined jamming signals are transmitted by each source node simultaneously. The optimum power allocation between the user and jamming signals at each source node is derived. Our analytical results suggest that the proposed cooperative jamming scheme improves on the system secrecy capacity, especially when the channel gains of the two source-relay links are of large difference. Simulation results in close agreement with analytical results clearly demonstrate the advantage of the proposed cooperative jamming scheme.

The security of the three-phase two-way relaying system with an eavesdropper is investigated in this paper. A cooperative jamming and power allocation scheme is proposed to enhance the system secrecy capacity. When a source node transmits user signals to the relay node, the other source node interferes the relay node with pre-defined jamming signals simultaneously. Optimum power allocation between the user and jamming signals at each source node is analyzed. Our analytical results suggest that the proposed cooperative jamming scheme improves on the system secrecy capacity, especially when the channel gains of the two source-relay links are of large difference. Simulation results in close agreement with analytical results clearly demonstrate the advantage of the proposed cooperative jamming scheme.