The fusion of hyperspectral imagery (HSI) and light detection and ranging (LiDAR) data for classification has received widespread attention and has led to significant progress in research and remote sensing applications. However, existing common CNN architectures suffer from the significant drawback of not being able to model remote sensing images globally, while transformer architectures are not able to capture local features effectively. To address these bottlenecks, this paper proposes a classification framework for multisource remote sensing image fusion. First, a spatial and spectral feature projection network is constructed based on parallel feature extraction by combining HSI and LiDAR data, which is conducive to extracting joint spatial, spectral, and elevation features from different source data. Furthermore, in order to construct local–global nonlinear feature mapping more flexibly, a network architecture coupling together multiscale convolution and a multiscale vision transformer is proposed. Moreover, a plug-and-play nonlocal feature token aggregation module is designed to adaptively adjust the domain offsets between different features, while a class token is employed to reduce the complexity of high-dimensional feature fusion. On three open-source remote sensing datasets, the performance of the proposed multisource fusion classification framework improves about 1% to 3% over other state-of-the-art algorithms.

Recent advancements in space exploration technology have significantly increased the number of diverse satellites in orbit. This surge in space-related information has posed considerable challenges in developing space target surveillance and situational awareness systems. However, existing detection algorithms face obstacles such as complex space backgrounds, varying illumination conditions, and diverse target sizes. To address these challenges, we propose an innovative end-to-end Attention-Guided Encoder DETR (AgeDETR) model, since artificial intelligence technology has progressed swiftly in recent years. Specifically, AgeDETR integrates Efficient Multi-Scale Attention (EMA) Enhanced FasterNet block (EF-Block) within a ResNet18 (EF-ResNet18) backbone. This integration enhances feature extraction and computational efficiency, providing a robust foundation for accurately identifying space targets. Additionally, we introduce the Attention-Guided Feature Enhancement (AGFE) module, which leverages self-attention and channel attention mechanisms to effectively extract and reinforce salient target features. Furthermore, the Attention-Guided Feature Fusion (AGFF) module optimizes multi-scale feature integration and produces highly expressive feature representations, which significantly improves recognition accuracy. The proposed AgeDETR framework achieves outstanding performance metrics, i.e., $97.9\%$ in ${mAP}_{0.5}$ and $85.2\%$ in $mA{P}_{0.5:0.95}$, on the SPARK2022 dataset, outperforming existing detectors and demonstrating superior performance in space target detection.