Space debris detection is important in space situation awareness and space asset protection. In this paper, we propose a method to detect space debris using feature learning of candidate regions. The acquired optical image sequences are first processed to remove hot pixels and flicker noise, and the nonuniform background information is removed by the proposed one dimensional mean iteration method. Then, the feature learning of candidate regions (FLCR) method is proposed to extract the candidate regions and to detect space debris. The candidate regions of space debris are precisely extracted, and then classified by a trained deep learning network. The feature learning model is trained using a large number of simulated space debris with different signal to noise ratios (SNRs) and motion parameters, instead of using real space debris, which make it difficult to extract a sufficient number of real space debris with diverse parameters in optical image sequences. Finally, the candidate regions are precisely placed in the optical image sequences. The experiment is performed using the simulated data and acquired image sequences. The results show that the proposed method has good performance when estimating and removing background, and it can detect low SNR space debris with high detection probability.

Fast space debris detection is valuable to space safety and space vehicle operation. In this paper, we proposed a fast space debris detection method with grid-based learning. The image divided into 14×14 grids, and then the fast grid-based neural network (FGBNN) is used to locate the position of the space debris in the grids. The experiments show that, In the test set, the model can reach the accuracy of 98.83% and recall of 95.99% with SNR 2 of space debris, it can process 430 images per second with the given experiment settings in the prediction process. It is proved that the proposed method has excellent performance and fast detection speed.

Matching drone images to satellite reference images is a critical step for achieving UAV self-localization. Existing drone visual localization datasets mainly focus on target localization, where each drone image is paired with a corresponding satellite image slice, typically with identical coverage. However, this one-to-one approach does not reflect real-world UAV self-localization needs as it cannot guarantee exact matches between drone images and satellite tiles nor reliably identify the correct satellite slice. To bridge this gap, we propose a one-to-many matching method between drone images and satellite reference tiles. First, we enhance the UAV-VisLoc dataset, making it the first in the field tailored for one-to-many imperfect matching in UAV self-localization. Second, we introduce a novel loss function, Incomp-NPair Loss, which better reflects real-world imperfect matching scenarios than traditional methods. Finally, to address challenges such as limited dataset size, training instability, and large-scale differences between drone images and satellite tiles, we adopt a Vision Transformer (ViT) baseline and integrate CNN-extracted features into its patch embedding layer.

Geolocation is a crucial step in the processing of Synthetic Aperture Radar (SAR) images. High-altitude airborne SAR systems present unique geolocation challenges due to travel long distances through the troposphere. However, the impact of tropospheric delay on geolocation is often overlooked in existing airborne SAR studies, which can lead to inaccuracies. To address this issue, we propose a new positioning method, the Tropospheric Delay Compensated Range-Doppler (TDC-RD) model. The TDC-RD model leverages reference atmospheric models to estimate and compensate for the tropospheric delay in SAR images. This model effectively mitigates the impact of tropospheric delay in SAR Geolocation. To further optimize the TDC-RD model solution, a Digital Elevation Model (DEM)-assisted dual iteration method is proposed. This method iteratively adjusts the target’s plane position and elevation in an alternating manner. The effectiveness of the TDC-RD model has been validated through both simulation experiments and actual flight experiments. The results show a significant improvement in geolocation accuracy compared to existing methods, with a maximum reduction of 11.39 m and 24.33% in the Mean Absolute Error (MAE) of SAR geolocation. The TDC-RD model has a great advantage in long-range SAR geolocation. Our research enhances the accuracy and stability of high-altitude airborne SAR geolocation without requiring ground control points.

Accurate geolocation of high-altitude airborne Synthetic Aperture Radar (HAA-SAR) imagery is pivotal for applications in Earth observation and remote sensing. However, errors in tropospheric delay modeling can severely affect positioning accuracy. Despite numerous existing compensation approaches, these methods inadequately address the complex atmospheric spatiotemporal variations encountered in HAA-SAR systems. This study presents the first comprehensive investigation of spatiotemporal variations of tropospheric delay in HAA-SAR systems, revealing their impact on positioning accuracy. To address these challenges, a Spatiotemporal Adaptive Tropospheric Delay Compensation (SATDC) approach is introduced. The proposed method integrates high-resolution ERA5 meteorological data with ray-tracing techniques, while employing a cross-iterative algorithm to simultaneously optimize target height and delay estimates. The method was validated through flight experiments using an X-band HAASAR system across diverse atmospheric conditions and terrains. Experimental results show that the proposed method improves positioning accuracy compared to existing approaches. Specifically, the SATDC method achieves an average range position mean absolute error (MAE) of 2.24 m, representing a reduction of 33.12% outperforming our previous tropospheric delay compensation work TDC-RD. Furthermore, the implementation of spatiotemporally adaptive compensation reduces the slant range measurement MAE from 12.94 m (without compensation) and 2.97 m (with conventional compensation) to 1.71 m. These findings highlight the method's effectiveness in compensating for tropospheric delay, enhancing the precision and reliability of HAA-SAR geolocation in challenging environments.