In low-shot weakly supervised object detection (LS-WSOD), a small number of strong (instance-level) labels are introduced to a weakly (image-level) annotated dataset, thus balancing annotation costs and model performance. To address issues in LS-WSOD in remote sensing images (RSIs) such as part domination, context confusion, class imbalance, and noise, we propose a novel active learning strategy and an enhanced fine-tuning mechanism. Specifically, we designed a part domination-based adaptive active learning (PDAAL) strategy to discover the most informative and challenging samples for instance-level annotation. PDAAL also applies an adaptive threshold to balance sampling frequencies for long-tailed class distributions. For enhanced fine-tuning, we first developed a parameter-efficient attention for context (PAC) module that learns spatial attention relationships, mitigating context confusion and accelerating the convergence of fine-tuning. Furthermore, we present an adaptive category resampling for tuning (ACRT) mechanism for resampling strong annotation data. ACRT contributes to refining the model at different active stages, especially for under-performed classes, and to reducing the impact of noisy predictions. Experimental results on the NWPU VHR-10.v2 and DIOR datasets show that our method outperforms state-of-the-art LS-WSOD baselines by 4.5% and 3.1% in mAP, respectively, demonstrating that our framework offers an efficient solution for LS-WSOD in RSIs.

The Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) employs a 532 nm laser with strong water-penetration capability, making it well suited for satellite-derived bathymetry in shallow waters; however, the effective denoising of photon-counting data remains essential due to strong solar background and intrinsic instrument noise. To address this challenge, this study proposes a novel photon denoising method, termed the Directional Nearest Neighbor Distance-based Algorithm (DNNDA), for robust extraction of signal photons from shallow-water ICESat-2 data. Unlike existing methods that rely heavily on density or terrain features and often degrade under high-noise conditions, DNNDA systematically exploits both scale-corrected spatial relationships and directional distribution characteristics of photons. By quantitatively characterizing the directional features of photon distributions and embedding this information into a density representation, DNNDA amplifies the density contrast between signal and noise photons, rendering the seafloor signal photons more distinct and easier to extract. An evaluation index was further designed to automate optimal parameter determination. Validation using multiple global ICESat-2 datasets demonstrates that DNNDA achieves superior seafloor photon extraction performance, with F1-scores exceeding 95%. Further regression analysis against high-precision CUDEM data in the Puerto Rico region yields root-mean-square errors below 0.57 m. By jointly correcting scale anisotropy and incorporating directional information, DNNDA enables reliable and adaptive signal photon extraction across local and global scales, providing a robust solution for shallow-water bathymetry in complex, high-noise environments.