Document-level Event Extraction (DEE) aims to identify event information from a given document. The two challenges of this task are the event arguments scattering across differrent sentences and the multiple events within a single document. In this paper, we propose a novel Dynamic Structure Hypergraph model to address the issue of limited global modeling capability in traditional graphs. Firstly, we construct a hypergraph to model the global interactions between different sentences and entities in a document. Then, new hyperedges are generated by constructing a mention-mention correlation matrix based on the updated node representations, which evolves the hypergraph into a dynamic structure. This will help the nodes to aware the contextual semantic information in time. Finally, extensive experiments and analysis demonstrate that our method has made significant improvements in addressing the two aforementioned challenges, which outperforms existing state-of-the-art models on two public datasets. Our code is available at https://github.com/1999rq/DSH.

Hyperspectral image (HSI) and multispectral image (MSI) fusion aims to generate high-resolution HSI by leveraging the high spectral fidelity of HSI and the fine spatial details of MSI. However, most existing methods rely on static fusion strategies that assume global consistency in modality contributions, ignoring the inherent regional variability in real-world remote sensing scenes. To address this limitation, we propose an adaptive expert learning framework (AELF) that dynamically models the modal dominance of different regions and adaptively adjusts fusion strategies accordingly. A core component of AELF is the modality-guided complementary module (MGCM), which establishes bidirectional cross-attention pathways between HSI and MSI. It enables each modality to adaptively discover complementary cues across multiple scales while suppressing irrelevant information, providing enhanced feature representation for subsequent fine-grained fusion. Building upon this, we designed the attribute-aware mixture of fusion experts (AMoFE) module, which decomposes the fused features into spectral, spatial, and edge subspaces. Each component is modeled by a specialized expert network, with a soft routing mechanism dynamically adjusting expert contributions based on contextual cues. Extensive experiments on benchmark datasets and a real-world dataset demonstrate that AELF achieves state-of-the-art performance in terms of spectral fidelity and spatial sharpness. Furthermore, our results confirm that the improved data quality brought by the proposed method effectively enhances the overall performance of downstream tasks. The code will be available at https://github.com/Hewq77/AELF.

Hyperspectral image super-resolution aims to reconstruct high-quality spatial-spectral cubes from RGB images. However, the limited spectral coverage of RGB inputs hinders the simultaneous modeling of spatial structures and spectral relationships. To address this limitation, we propose a differential low-rank adaptive spatial-spectral transformer (DiLAST). Initially, a differential operator is employed to enhance RGB features in a bottom-up manner, explicitly amplifying subtle inter-channel differences. The enhanced features are then fed into a U-shaped backbone, which integrates three complementary modules for joint spatial-spectral modeling. Specifically, a center spatial-spectral attention (CSSA) module employs cross-attention mechanisms to capture local-to-global dependencies across both spatial and spectral domains; an adaptive cross-scale fusion (ACF) module utilizes learnable gating weights to establish dynamic interaction pathways between shallow high-frequency details and deep semantic representations; and a low-rank spectral calibration (LRSC) module exploits low-rank matrix priors to reveal low-dimensional manifold structures among spectral bands, thereby enhancing spectral consistency. By leveraging the synergistic effects of spatial non-locality, global spectral correlation, and low-rank properties, the proposed DiLAST achieves PSNR improvements of 33.82 dB, 36.03 dB, and 37.01 dB on benchmark datasets. Moreover, the accuracy and practical applicability of the reconstructed spectra have been effectively validated in remote sensing scenarios and object tracking tasks. The code is accessible at https://github.com/renqi1998/DiLAST.

With its strong capability in modeling long-range dependencies, the Transformer achieves competitive performance in hyperspectral image (HSI) and multispectral image (MSI) fusion. However, existing Transformer-based methods face the trade-off between receptive field size and computational efficiency when dealing with spatially non-local features. Furthermore, the Transformer captures deep spectral relationships by modeling pairwise channel interactions. This global interaction may overlook features that contribute little to the overall context but are critical locally, thus affecting the accurate understanding of HSI content. To overcome these challenges, we propose a novel local-global collaborative network with Transformers (LGCT) specifically designed to achieve high-quality HSI reconstruction. The proposed LGCT includes two inverse feature streams to establish multiscale deep representations of the HSI and MSI features. The feature streams comprise collaborative Transformer blocks (CTBs) explicitly designed for the spectral and spatial domains. By combining global and local processing mechanisms, the proposed CTBs can efficiently emphasize potential crucial features that Transformer ignores when capturing deep spectral and spatial relationships, thus enabling efficient modeling of the spectral and spatial domains from details to the whole. Furthermore, to enhance the reusability of multiscale enhanced features from the spectral and spatial domains, a hierarchical and symmetric strategy is adopted to progressively fuse them to generate high-quality images. The results on both simulated and real datasets demonstrate the superior performance of the proposed method in terms of quantitative metrics and visual quality. The code will be released athttps://github.com/Hewq77/LGCT.

Hyperspectral images (HSIs) provide a large amount of spatial and spectral information to characterize ground objects. However, they also contain a lot of redundant information, which makes it difficult to extract complex local and global spatial-spectral features. Considering that HSIs present multi-scale similarity and anisotropic image features, multi-scale and multi-angle information can be used to effectively model local and global features and reduce the complexity of self-attention. This paper proposes a new multi-scale multi-angle attention network (MMAN) for HSI classification that models the internal relationship between image features at local and global scales. Firstly, three spectral-spatial feature extraction modules (at different scales) are constructed to extract the low-level features of the image. These modules are first used by a 3D convolutional layer for spectral feature extraction, and then input to a 2D convolutional layer for spatial feature extraction. Next, the serialized tokens are input to the multi-angle attention module. Finally, the learnable labels are identified through a linear layer, and the features of different scales are fused through a fully connected layer to realize the classification of samples. Experimental results on four standard datasets show that the proposed exhibits comparable or superior classification performance than other state-of-the-art methods.

Anomaly detection technology relying on memory reconstruction leverages the difference in reconstruction errors between the normal and abnormal frames to achieve superior detection performance. However, there are still some challenges with this technology. First, the memory has insufficient representation capacity for features. Second, there is a contradiction between feature fusion and reconstruction. As feature fusion copies the abnormal patterns into the reconstructed frames, the abnormal frames are effectively reconstructed, reducing the detection performance. In response to these challenges, we use a memory update threshold to improve the representational power of memory. We also propose a dual-encoder anomaly detection model to restrict anomaly feature propagation. Experiment results demonstrate the effectiveness and robustness of our approach.

In this letter, a novel spatial peak-aware collaborative representation (SPaCR) method is proposed for hyperspectral imagery (HSI) classification, which introduces spectral–spatial information among superpixel clusters into regularization terms to construct a new collaborative representation (CR)-based closed-form solution. The proposed method is composed of the following key steps. First, the raw HSI is clustered into many superpixels according to an oversegmentation strategy. Then, cluster pixels are determined based on spectral–spatial correlation between pixels within each superpixel. Next, spectral distance and spatial coherence of superpixel clusters corresponding to training samples and testing pixels are fused to define differences between pixels. Finally, the difference information between clusters as a spectral–spatial feature-induced regularization term is incorporated into the objective function. Experimental results on the Indian Pines and the University of Pavia HSIs indicated that the proposed SPaCR method, without any preprocessing and postprocessing, outperforms well-known and state-of-the-art classifiers on the limited labeled samples.

In this paper, we investigate the joint impact of both the number of small BSs and the transmit powers on the energy efficiency (EE) of Heterogeneous cellular networks (HetNets). Based on a typical HetNet structure, we propose a feasible focusing searching algorithm to find the optimal number of small BSs and the corresponding transmit power under coverage and spectral efficiency constraints that maximize the network EE. Simulation results show that there exists an optimal number of small BSs and the transmit power which can not only maximize network EE but also satisfy the constraints.