MWIR–Visible Dual-Channel Airborne Imaging integration for High-Resolution Urban Mapping

A Unified On-Orbit Relative Radiometric Calibration Framework for Optical Remote Sensing Satellites Based on Side-Slither Observations

On-orbit relative radiometric calibration (RRC) is a fundamental prerequisite for quantitative remote sensing analysis and high-level product generation. Although side-slither maneuvers provide a robust means for calibration, existing methodologies are often constrained by regularization inaccuracies, uneven distribution of gray-scale samples, and limited adaptability to complex multisensor architectures. This article proposes a unified RRC framework to overcome these challenges. The process begins with a prior-guided optimization method for data regularization, which operates independently of linear features or edges. To handle the inherently uneven distribution of gray-scale samples across natural scenes, an adaptive clustering-based method is implemented to estimate calibration coefficients. This approach ensures stable performance across the observed effective dynamic range, particularly in gray-scale ranges where samples are sparse. Furthermore, the framework incorporates a detail-aware strategy to achieve high-precision calibration across the full field of view (FOV). Specifically, the virtual steady reimaging (VSRI) model is first leveraged to achieve rigorous spatial alignment of identical ground features across multiple sensors. Based on this precise geometric alignment, the radiometric calibration is subsequently anchored to an optimal reference radiometric state. This approach effectively eliminates cross-chip inconsistencies while preserving structural details. Validation using side-slither and push-broom data from the Intelligent Remote Sensing Satellite-1 (IRSS-1), Luojia3-02 (LJ3-02), and Ziyuan-1F (ZY-1F) satellites demonstrates the effectiveness of our proposed approach across diverse scenes, sensor architectures, and spectral bands. Comparative analyses show that the proposed method achieves overall superior performance over four state-of-the-art methods in removing striping artifacts and maintaining radiometric fidelity.

Enabling More Efficient Dynamic Earth Observation: Wide-Area, High-Resolution Dual-Axis Bidirectional Scanning Imaging and the Associated Multi-Parameter Optimization

Achieving wide-area, near-real-time, and fine-detail Earth observation is crucial for advanced remote sensing applications in the future. This study presents high-resolution dual-axis bidirectional scanning (HRBS), an innovative Earth observation modality that achieves optimal balance between swath expansion and spatial resolution through vertical-axis reciprocating scanning. This imaging architecture exhibits substantial theoretical potential for cost-efficient deployment in future commercial Earth observation satellite constellations (EOSs), due to its inherent decoupling of scanning functionality from satellite platform maneuvers. To address the image motion degradation (IMD) caused by motion mismatches during wide-field scanning, this study proposes a multi-parameter optimized scanning strategy framework. First, we delineate the imaging principle of wide-field, high-resolution bidirectional time-delay integration (TDI) using a two-dimensional scanning mirror. Building on this foundation, we establish a multi-element model to quantify spatiotemporal matching errors (STMEs) and introduce an imaging quality evaluation metric. By integrating engineering constraints into the scanning trajectory solution space, we develop an iterative optimization method for bidirectional scanning paths coupled with a real-time adaptive row transfer frequency (RTF) adjustment strategy. Experimental implementation through high-fidelity numerical simulations validates optimized dual-axis scanning trajectories and detector RTF configurations. Comparative high-fidelity numerical simulations reveal that the optimized HRBS strategy reduces full-orbit phase residuals to below 0.1 pixels, theoretically satisfying stringent image quality requirements for this highly dynamic time-varying imaging modality. These advancements lay a solid theoretical foundation for HRBS, demonstrating its potential as a promising solution for future commercial Earth observation systems demanding both resolution and coverage efficiency, while potentially supporting high-precision remote sensing applications.

Adaptive Spatial–Frequency Information Fusion for SAR Ship Detection

Synthetic-aperture radar (SAR) ship detection is a fundamental task in maritime remote sensing, supporting wide-area surveillance, traffic monitoring, and emergency response under all-weather imaging conditions. Existing deep detectors mainly rely on spatial cues such as intensity, shape and context, but structured sea clutter and near-shore interference can still produce ship-like responses, while fine scattering details are weakened by deep downsampling. We address two practical representation limitations: incomplete preservation of shallow high-resolution details, and limited explicit modeling of local directional variation. To this end, we propose HMF-RTMDet, a shallow-neck spatial–frequency fusion detector. A P2 high-resolution path combines C2 features with upsampled P3 semantics. HybridMFBlock then processes the fused feature through a morphology branch and a trainable depthwise branch initialized by fractional Gabor templates, followed by channel-wise fusion. In the reported main HRSID run, HMF-RTMDet improves RTMDet-s from 67.9% to 72.6% in AP50:95, from 90.2% to 94.2% in AP50, and from 68.2% to 73.4% in APs. Across three runs, however, its AP50:95 is 72.17 ± 0.38%, comparable to the SFS-Conv and MCU-only controls. The evidence therefore identifies the P2 path as the main gain source but does not establish a stable advantage for HybridMFBlock over these controls. On SSDD, overall AP50:95 remains nearly unchanged and large-target performance decreases, defining an important boundary of the current design.

A Geographic Consistency-Constrained Cross-Modal Super-Resolution Matching Method for UAV Geo-Localization

Visual geo-localization is a predominant approach for unmanned aerial vehicles (UAVs) operating in Global Navigation Satellite System (GNSS)-denied environments, typically achieved by matching UAV-captured visible optical images with satellite base maps. However, under low-light conditions, visible cameras struggle to capture distinct features. While infrared sensors can capture clear features in such scenarios, the significant modality gap between thermal infrared images and optical satellite base maps makes accurate matching highly challenging. In this paper, we propose a novel cross-modal super-resolution matching and geo-localization method constrained by geographic consistency. First, a geographic consistency normalization module is introduced to narrow the modality gap between satellite optical images and thermal infrared images, thereby enhancing cross-modal matchability. Subsequently, a thermal infrared super-resolution enhancement module is employed to improve the spatial resolution and detail representation of the images, effectively increasing feature discriminability in low-texture regions. Finally, an end-to-end dense matching module is utilized to strengthen the stability of cross-modal correspondence estimation, ultimately improving geo-localization accuracy in low-light environments. Extensive experiments conducted on both a self-constructed network dataset and a real-world flight dataset demonstrate that the proposed method outperforms current competitive approaches. The proposed framework is not a simple combination of existing enhancement and matching modules, but a task-driven design that jointly addresses cross-modal discrepancy, low-resolution thermal imagery, and robust correspondence estimation. Experiments on self-constructed and public datasets demonstrate its robustness and superiority, achieving average geo-localization errors of 1.31 m and 8.04 m, respectively.

Signal Processing, Optimization, and Multi-Modal Fusion for Next-Generation Automotive FMCW Radar Systems

Seeing above the waves: A modular sensing framework for data acquisition at sea

Advancing autonomy for surface vessels requires systematic evaluation of their sensing and perception subsystems. Yet, maritime environments impose unique challenges: sensor installation is constrained by vessel layout, environmental conditions such as fog or sea clutter are difficult to reproduce, and long-duration missions complicate data collection. This work addresses the question: How can we design a modular and reproducible sensor platform for maritime autonomy? We present a comprehensive design blueprint that incorporates diverse modalities - RADAR, LiDAR, IMU, GNSS, AIS, RGB and LWIR cameras, and weather sensors - to enhance environmental awareness and vessel proprioception. Supported by a dedicated ROS2-based software framework for data management, our modular platform enables long-term data collection, hardware-in-the-loop testing, and integration with existing sensors and algorithms. By unifying hardware design and data capture methodology, the platform enhances reproducibility and comparability across vessels and research projects. The proposed framework bridges engineering implementation and research methodology, providing the foundation for standardized, verifiable datasets essential to advancing situational awareness and autonomous maritime navigation.