Zhen Yan

Platform jitter in stereo mapping satellites introduces periodic stripe artifacts into digital surface models (DSMs), degrading geometric quality, while existing detection methods usually depend on disparity maps or high-frequency attitude data. This study proposes a DSM-based frequency-domain framework for detecting and removing jitter-induced stripes in GF-7 DSMs. First, 2D Fourier narrow-band notch analysis of detrended high-pass DSMs estimates stripe orientation and dominant period from directional and radial spectral peak prominence, and a Gaussian narrow-band stop filter is constructed for global suppression. Then, under these spectral priors, a profile-template method uses 1D median profiles, IIR notch filtering, and a global stripe template with a slowly varying amplitude field to model and remove the stripe component in the spatial domain. Multi-temporal co-registered DSM differencing provides reference stripe characteristics for evaluation. Experiments on three GF-7 DSMs show a stripe normal direction aligned with the subsatellite ground track, a stable dominant stripe period of about 90 m, period errors not exceeding 0.34 m, and consistency above 99.7% between the two methods. The spectral notch filtering suppresses more than 92% of stripe-band energy while preserving the overall spectral shape. The proposed framework therefore enables accurate jitter characterization and effective destriping without attitude or disparity information.

For addressing the technical challenges of photon denoising and water level extraction in ICESat-2 satellite-based water monitoring applications, this paper proposes an innovative solution integrating Gaussian function fitting with Hough transform. The method first employs histogram Gaussian fitting to achieve coarse denoising of water body regions. Subsequently, a probability attenuation model based on elevation differences between adjacent photons is constructed to accomplish refined denoising through iterative optimization of adaptive thresholds. Building upon this foundation, the Hough transform technique from image processing is introduced into photon cloud processing, enabling robust water level extraction from ICESat-2 data. Through rasterization, discrete photon distributions are converted into image space, where straight lines conforming to the photon distribution are then mapped as intersection points of sinusoidal curves in Hough space. Leveraging the noise-resistant characteristics of the Hough space accumulator, the interference from residual noise photons is effectively eliminated, thereby achieving high-precision water level line extraction. Experiments were conducted across five typical water bodies (Qinghai Lake, Long Land, Ganquan Island, Qilian Yu Islands, and Miyun Reservoir). The results demonstrate that the proposed denoising method outperforms DBSCAN and OPTICS algorithms in terms of accuracy, precision, recall, F1-score, and computational efficiency. In water level estimation, the absolute error of the Hough transform-based line detection method remains below 2 cm, significantly surpassing the performance of mean value, median value, and RANSAC algorithms. This study provides a novel technical framework for effective global water level monitoring.

Accurate building geometry information is crucial for urban planning in constrained spaces, fueling the growing demand for large-scale, high-precision 3D city modeling. Traditional methods like oblique photogrammetry and LiDAR prove time consuming and expensive for low-cost 3D reconstruction of expansive urban scenes. Addressing this challenge, our study proposes a novel approach to leveraging single-view remote sensing images. By integrating shadow information with deep learning networks, our method measures building height and employs a semantic segmentation technique for single-image high-rise building reconstruction. In addition, we have designed complex shadow measurement algorithms and building contour correction algorithms to improve the accuracy of building models in conjunction with our previous research. We evaluate the method’s precision, time efficiency, and applicability across various data sources, scenarios, and scales. The results demonstrate the rapid and accurate acquisition of 3D building data with maintained geometric accuracy (mean error below 5 m). This approach offers an economical and effective solution for large-scale urban modeling, bridging the gap in cost-efficient 3D reconstruction techniques.