Remote Sensing

Hyperspectral image (HSI) and light detection and ranging (LiDAR) data offer complementary spectral and structural information; however, the integration of these high-dimensional, heterogeneous modalities poses significant challenges. We propose a Global–Local Mamba dual-modality fusion framework (GL-Mamba) for HSI–LiDAR classification. Each sensor’s input is decomposed into low- and high-frequency sub-bands: lightweight 3D/2D CNNs process low-frequency spectral–spatial structures, while compact transformers handle high-frequency details. The outputs are aggregated using a global–local Mamba block, a state-space sequence model that retains local context while capturing long-range dependencies with linear complexity. A cross-attention module aligns spectral and elevation features, yielding a lightweight, efficient architecture that preserves fine textures and coarse structures. Experiments on Trento, Augsburg, and Houston2013 datasets show that GL-Mamba outperforms eight leading baselines in accuracy and kappa coefficient, while maintaining high inference speed due to its dual-frequency design. These results highlight the practicality and accuracy of our model for multimodal remote-sensing applications.

Satellite precipitation products serve as valuable global data sources for hydrological modeling, yet their applicability across different hydrological models remains insufficiently explored. The distributed physics-informed deep learning model (DPDL), as a representative of emerging differentiable, physics-based hydrological models, requires a systematic evaluation of the suitability of multi-source precipitation products within its modeling framework. This study focuses on the Xiangjiang River Basin in southern China, where both a DPDL model and a Soil and Water Assessment Tool (SWAT) model were constructed. In addition, two model training strategies were designed: S1 (fixed parameters) and S2 (product-specific recalibration). Multiple precipitation products were used to drive both hydrological models, and their streamflow simulation performance was evaluated under different training schemes to analyze the compatibility between precipitation products and hydrological modeling frameworks. The results show that: (1) In the Xiangjiang River Basin of southern China, GSMaP demonstrated the best overall performance with a Critical Success Index of 0.70 and a correlation coefficient (Corr) of 0.79; IMERG-F showed acceptable accuracy with a Corr of 0.75 but had a relatively high false alarm rate (FAR) of 0.32; while CMORPH exhibited the most significant systematic underestimation with a relative bias (RBIAS) of −8.48%. (2) The DPDL model more effectively captured watershed hydrological dynamics, achieving a validation period correlation coefficient of 0.82 and a Nash–Sutcliffe efficiency (NSE) of 0.79, outperforming the SWAT model. However, the DPDL model showed a higher RBIAS of +16.69% during the validation period, along with greater overestimation fluctuations during dry periods, revealing inherent limitations of differentiable hydrological models when training samples are limited. (3) The S2 strategy (product-specific recalibration) improved the streamflow simulation accuracy for most precipitation products, with the maximum increase in the NSE coefficient reaching 15.8%. (4) The hydrological utility of satellite products is jointly determined by model architecture and training strategy. For the DPDL model, IMERG-F demonstrated the best overall robustness, while GSMaP achieved the highest accuracy under the S2 strategy. This study aims to provide theoretical support for optimizing differentiable hydrological modeling and to offer new perspectives for evaluating the hydrological utility of satellite precipitation products.

To address the challenges of cross-modal feature misalignment and ineffective information fusion caused by the inherent differences in imaging mechanisms, noise statistics, and semantic representations between visible and synthetic aperture radar (SAR) imagery, this paper proposes a multimodal remote sensing object detection method, namely YOLO-CMFM. Built upon the Ultralytics YOLOv11 framework, the proposed approach designs a Cross-Modal Fusion Module (CMFM) that systematically enhances detection accuracy and robustness from the perspectives of modality alignment, feature interaction, and adaptive fusion. Specifically, (1) a Learnable Edge-Guided Attention (LEGA) module is constructed, which leverages a learnable Gaussian saliency prior to achieve edge-oriented cross-modal alignment, effectively mitigating edge-structure mismatches across modalities; (2) a Bidirectional Cross-Attention (BCA) module is developed to enable deep semantic interaction and global contextual aggregation; (3) a Context-Guided Gating (CGG) module is designed to dynamically generate complementary weights based on multimodal source features and global contextual information, thereby achieving adaptive fusion across modalities. Extensive experiments conducted on the OGSOD 1.0 dataset demonstrate that the proposed YOLO-CMFM achieves an of 96.2% and an :95 of 75.1%. While maintaining competitive performance comparable to mainstream approaches at lower IoU thresholds, the proposed method significantly outperforms existing counterparts at high IoU thresholds, highlighting its superior capability in precise object localization. Also, the experimental results on the OSPRC dataset demonstrate that the proposed method can consistently achieve stable gains under different kinds of imaging conditions, including diverse SAR polarizations, spatial resolutions, and cloud occlusion conditions. Moreover, the CMFM can be flexibly integrated into different detection frameworks, which further validates its strong generalization and transferability in multimodal remote sensing object detection tasks.