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.

To address the challenges of multi-scale object recognition and complex background in-terference in remote sensing images, this paper proposes a lightweight improved algo-rithm based on YOLOv8-obb. By integrating a hybrid local-channel attention mechanism (MLCA), dynamic upsampling (DySample), and a reparameterized cross-scale feature aggregation module (RepNCSPELAN), the algorithm achieves dual enhancements in de-tection accuracy and computational efficiency. The MLCA module enhances feature dis-criminability in complex backgrounds through a dual-path mechanism combining local and global pooling. The DySample module employs content-aware sampling point gen-eration to improve feature reconstruction for multi-scale targets. The RepNCSPELAN module reduces model parameters by 29% while preserving cross-scale feature fusion ca-pabilities. Experimental results on the DOTA dataset demonstrate a 2.3% improvement in mAP50, with parameters reduced to 2.2M and FLOPs decreased by 27%. Cross-dataset validation on DIOR further confirms a 1.5% mAP50 gain. Compared to mainstream lightweight models (e.g., YOLOv5n, YOLOv8n), the proposed algorithm exhibits superior performance in accuracy (65.3% vs. 60.8%), parameter efficiency (2.2M vs. 3.1M), and computational cost (6.2G vs. 8.5G FLOPs). Ablation studies validate the efficacy of each module, while visualizations highlight robustness in dense small-object detection and ro-tated target localization. This work provides an efficient solution for real-time remote sensing object detection in complex scenarios and offers a novel technical pathway for lightweight deep learning model design.