Satellite-based hyperspectral (HS) imagery holds great potential in remote sensing applications due to its fine spectral resolution. However, the low spatial resolution limits its practical utility. Combining ancillary high resolution panchromatic (PAN) or multispectral (MS) images has become a common practice to improve the spatial quality of HS images. Most present approaches, however, are based on dual-sensor fusion (e.g., MS-HS or PAN-HS), which generally falls short of comprehensively integrating their complementary spatial and spectral information of PAN, MS, and HS images. Meanwhile, existing few integrated fusion methods suffer from two key limitations: modality mismatch due to inconsistent spatial-spectral characteristics among PAN, MS, and HS data, and shallow and redundant cross-modal coupling caused by inadequate modeling of inter-modal relationships. In this paper, we propose a progressive spatial-spectral interactive network (PSSNet) for the integrated fusion of panchromatic, multispectral, and hyperspectral images. Specifically, a context-aware fusion block is introduced to extract and enhance contextual spatial and spectral information across different modalities. To ensure an effective integration of spatial and spectral details, the entire network is structured progressively, allowing for a smooth transition and fusion of features from HS, MS, and PAN images. Additionally, a spatial-spectral feature recombination module is designed to dynamically adjust the contribution of spectral features at various levels. This module, in combination with a spatial enhancement component, facilitates the optimal fusion of spatial and spectral information by enhancing their interactions. Extensive experiments on simulated and real datasets, both qualitatively and quantitatively, demonstrate the superiority of PSSNet compared to other state-of-the-art methods.

UAV aerial Visible-Infrared (RGBT) object detection has been widely applied in fields such as military operations and rescue missions. However, although numerous UAV aerial RGBT object detection methods exist, several challenges remain in this field. On the one hand, drones typically operate at high altitudes, and objects only occupy a small number of pixels in imaging, posing a significant challenge to object detection. On the other hand, spatial misalignment between modalities remains a major obstacle in cross-modal fusion—especially given the small size of the objects. To address the above issues, this paper proposes IM-CMDet, an intra-modal enhancement and cross-modal fusion network for small object detection in UAV-based RGBT imagery, which comprises three effective modules: the Detail-Semantics Joint Enhancement module (DSJE), the Differential-based Fusion Weight Generation module (DFWG) and the Feature Reconstruction Network (FRN). The DSJE module prevents small object features from being overwhelmed by background noise through optimizing feature representations across different levels. The FRN module is designed to overcome modality differences and build inter-modality information correlation via swin-Transformer architecture. To further enhance the network’s sensitivity to small objects, the DFWG combines differential and spatial attention to generate the final fusion weights while reducing the impact of background noise on detection performance. Extensive experiments on RGBTDronePerson and two additional benchmarks demonstrate that IM-CMDet achieves state-of-the-art performance through effective cross-modal fusion, significantly advancing small-object detection in complex aerial scenarios. The code is available at https://github.com/RS-Minchao/IM-CMDet.

The integrated fusion of the high-spatial-resolution (HR) panchromatic image (PAN), the relative “moderate”-spatial-resolution (MR) multispectral image (MSI), and the low-spatial-resolution (LR) hyperspectral image (HSI), to generate the optimal HR HS fused image, is promising but challenging. On the one hand, existing mainstream fusion models mostly focus on the “pairwise fusion” between HR PAN, MR MSI, and LR HSI, which cannot sufficiently integrate their complementary spatial and spectral advantages. On the other hand, one of the few integrated fusion methods roughly introduced the MR MSI as a simple intermediate medium; however, the role of MSIs as a spatial and spectral “bridge” between HR PAN and LR HSI, generally characterized by significant scale differences, remains largely unexplored. To solve these problems, we proposed an integrated PAN–MSI–HSI fusion method from the perspective of MSIs, by comprehensively considering the scale difference among the multisource observations. In the proposed method, a spatial–spectral feature transfer network was designed by comprehensively exploring the spatial–spectral variations and connections among the HR PAN, MR MSI, and LR HSI. Then, a spatial–spectral joint reconstruction module was constructed to reconstruct the HR HSI with optimal spatial and spectral fidelity. Experiments were conducted on simulated and real datasets from qualitative and quantitative aspects. The experimental results demonstrated the competitive effectiveness over other state-of-the-art methods.

Spectral super-resolution (SSR) is the computational process of generating a high-dimensional hyperspectral image from a low-dimensional image through spectral reconstruction techniques. Recently, deep learning has demonstrated remarkable potential in the field of SSR, achieving impressive results. However, existing deep learning-based approaches often fail to deliver high-fidelity SSR outcomes. These methods tend to focus primarily on spectral information while paying insufficient attention to the critical role of spatial features. Furthermore, they lack effective strategies for capturing inter-band relationships, resulting in suboptimal spectral information modeling. To address these limitations, we propose a novel network for SSR, termed Bidirectional Spectral Attention Multi-Scale Aggregation Network (BiSANet). BiSANet features three U-Net-like branches and integrates two advanced attention mechanisms. The bidirectional spectral attention modules dynamically model inter-spectral dependencies through forward and reverse spectral feature extraction, enhanced by a weight-sharing strategy. Specifically, we reverse the spectral order of feature maps to activate complementary global trends and local details, overcoming the limitations of unidirectional modeling in traditional methods. Additionally, an independent spatial reconstruction branch with a dedicated loss function ensures precise spatial detail preservation. Experimental results demonstrate that BiSANet outperforms state-of-the-art methods across three benchmarks. For instance, on the DFC2018 Houston dataset, it achieves a 4.26% PSNR improvement and an 11.52% SAM reduction, highlighting its robustness and accuracy in spectral-spatial reconstruction.

The integration of hyperspectral (HS) imagery and light detection and ranging (LiDAR) data for land cover classification has emerged as a prominent research focus. Despite the satisfactory classification accuracies achieved by existing methodologies, several unaddressed issues that remain warrant consideration. First, current approaches overlook the pronounced spectral and spatial heterogeneities in remote sensing (RS) images designated for multiclassification tasks, limiting the performance of classification models. Moreover, most existing studies amalgamate elevation features with other characteristics through simple addition and interaction operations, and they do not delve deeply into exploiting elevation height information, leading to an imbalance in the representation of elevation height. In light of the aforementioned issues, this article introduces a spatial-spectral heterogeneity-aware network (S2HANet) for the joint classification of HS and LiDAR data. Specifically, a shared spectral correction module (SSCM) is designed in the spectral branch to preliminarily alleviate the problem of large intraclass variance, followed by the use of a contrastive learning framework to enhance the intraclass compactness and interclass separability of spectral features. A multichannel signed distance discrimination module (MCSDDM) is developed to learn the distance relationships between intra- and interclass pixels and boundaries, and using prior boundary information to improve spatial boundary information. In addition, an elevation boost module (EBM) and an elevation injection module (EIM) are meticulously designed to phase-in elevation height information, further enhancing the utilization of elevation data and better facilitating the fusion of the two modalities. The proposed S2HANet has demonstrated exceptional classification performance across three opening benchmark datasets.

The integration of hyperspectral (HS) imagery and light detection and ranging (LiDAR) for land cover classification has become a significant research topic. Numerous existing methods aim to interactively fuse the complementary features of HS and LiDAR to enhance the classification accuracy. However, most existing studies overlook the fact that spectral, spatial, and elevation features of HS and LiDAR possess significant discriminative information for specific categories. The rough and simple interacting or stacking these features may hinder the effective expression of this significant discriminative information. Moreover, existing approaches neglect the shared spatial characteristics between HS and LiDAR. In this article, an uncertain category-aware fusion network (UCAFNet) is proposed to tackle the above challenges. Specifically, we proposed an uncertain category-aware fusion strategy (UCAFS) that dynamically weights the spectral, spatial, and elevation branches based on their respective capabilities in identifying different categories to achieve targeted information aggregation. Moreover, we introduce the spatial information purification module (SIPM) and adaptive weighted fusion module (AWFM), to extract and enhance shared spatial features from HS and LiDAR for effective integration. The experimental results on three public benchmark datasets demonstrate the superior performance of the proposed UCAFNet.