Laser waveform data that contain rich three-dimensional structural object information hold significant value in forest resource monitoring. However, traditional waveform decomposition algorithms are often constrained by complex waveform structures and depend on the initial parameter selections, which affect the accuracy and robustness of the results. To address the issues of the strong dependence on initial parameters, susceptibility to local optima, and difficulty in detecting hidden peaks during waveform overlap in the traditional satellite laser waveform decomposition algorithms, this study proposes a waveform decomposition method that combines hidden peak detection and an adaptive genetic algorithm (HAGA). This method uses hidden peak detection algorithms to improve the accurate extraction of the Gaussian components from the original waveform and provides the initial parameters. The high-precision extraction of waveform parameters is achieved through the adaptive genetic algorithm (AGA) combined with Levenberg–Marquardt (LM) optimization. In the experimental validation, the proposed method outperformed the traditional methods in both waveform decomposition fitting accuracy and tree height extraction. The average waveform decomposition accuracy $R_{mean}^2$ for more than 2000 laser spots reaches 0.955, whereas the $RMSE$ of the tree height extractions is better than 2 m, demonstrating strong robustness and applicability.

Satellite laser altimetry technology, a novel space remote sensing technique, actively acquires high-precision elevation information about the Earth’s surface. However, the accuracy of laser altimetry can be compromised by alterations in the satellite-ground environment, thermal dynamics, and cosmic radiation. These factors may induce subtle variations in the installation and internal structure of the spaceborne laser altimeter on the satellite platform, diminishing measurement precision. In-orbit calibration is thus essential to enhancing the precision of laser altimetry. Through collaborative calculations between satellite and ground stations, we can derive correction parameters for laser pointing and ranging, substantially improving the accuracy of satellite laser altimetry. This paper introduces a sophisticated calibration method for laser altimeter pointing and ranging that utilizes dense control points. The approach interpolates discrete ground control point data into continuous simulated terrain using empirical Bayesian kriging, subsequently categorizing the data for either pointing or ranging calibration according to their respective functions. Following this, a series of calibration experiments are conducted, prioritizing “pointing” followed by “ranging” and continuing until the variation in the ranging calibration results falls below a predefined threshold. We employed experimental data from ground control points (GCPs) in Xinjiang and Inner Mongolia, China, to calibrate the GaoFen-7 (GF-7) satellite Beam 2 laser altimeter as per the outlined method. The calibration outcomes were then benchmarked against those gleaned from infrared laser detector calibration, revealing disparities of 1.12 s in the pointing angle and 2 cm in the ranging correction value. Post validation with ground control points, the measurement accuracy was refined to 0.15 m. The experiments confirm that the proposed calibration method offers accuracy comparable to that of infrared laser detector calibration and can facilitate the updating of 1:10,000 topographic maps utilizing stereo optical imagery. Furthermore, this method is more cost-effective and demands fewer personnel for ground control point collection, enhancing resource efficiency compared to traditional infrared laser detector calibration. The proposed approach surpasses terrain-matching limitations when calibrating laser ranging parameters and presents a viable solution for achieving frequent and high-precision in-orbit calibration of laser altimetry satellites.

Inland lakes and reservoirs are critical components of global freshwater resources. However, traditional water level monitoring stations are costly to establish and maintain, particularly in remote areas. As an alternative, satellite altimetry has become a key tool for lake water level monitoring. Nevertheless, conventional radar altimetry techniques face accuracy limitations when monitoring small water bodies. The Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2), equipped with a single-photon counting lidar system, offers enhanced precision and a smaller ground footprint, making it more suitable for small-scale water body monitoring. However, the water level data obtained from the ICESat-2 ATL13 inland water surface height product are limited in quantity, while the lake water level accuracy derived from the ATL08 product is relatively low. To overcome these challenges, this study proposes a Spatial Distribution-Based Hierarchical Clustering for Photon-Counting Laser altimeter (SD-HCPLA) for enhanced water level extraction, validated through experiments conducted at the Danjiangkou Reservoir. The proposed method first employs Landsat 8/9 imagery and the Normalized Difference Water Index (NDWI) to generate a water mask, which is then used to filter ATL03 photon data within the water body boundaries. Subsequently, a Minimum Spanning Tree (MST) is constructed by traversing all photon points, where the vertical distance between adjacent photons replaces the traditional Euclidean distance as the edge length, thereby facilitating the clustering and denoising of the point cloud data. The SD-HCPLA algorithm successfully obtained 41 days of valid water level data for the Danjiangkou Reservoir, achieving a correlation coefficient of 0.99 and an average error of 0.14 m. Compared with ATL08 and ATL13, the SD-HCPLA method yields higher data availability and improved accuracy in water level estimation. Furthermore, the proposed algorithm was applied to extract water level data for five lakes and reservoirs in Hubei Province from 2018 to 2023. The temporal variations and inter-correlations of water levels were analyzed, providing valuable insights for regional ecological environment monitoring and water resource management.

Linear Variable Filter (LVF) hyperspectral cameras possess the advantages of high spectral resolution, compact size, and light weight, making them highly suitable for unmanned aerial vehicle (UAV) platforms. However, challenges arise in data registration due to the imaging characteristics of LVF data and the instability of UAV platforms. These challenges stem from the diversity of LVF data bands and significant inter-band differences. Even after geometric processing, adjacent flight lines still exhibit varying degrees of geometric deformation. In this paper, a progressive grouping-based strategy for iterative band selection and registration is proposed. In addition, an improved Scale-Invariant Feature Transform (SIFT) algorithm, termed the Double Sufficiency–SIFT (DS-SIFT) algorithm, is introduced. This method first groups bands, selects the optimal reference band, and performs coarse registration based on the SIFT method. Subsequently, during the fine registration stage, it introduces an improved position/scale/orientation joint SIFT registration algorithm (IPSO-SIFT) that integrates partitioning and the principle of structural similarity. This algorithm iteratively refines registration based on the grouping results. Experimental data obtained from a self-developed and integrated LVF hyperspectral remote sensing system are utilized to verify the effectiveness of the proposed algorithm. A comparison with classical algorithms, such as SIFT and PSO-SIFT, demonstrates that the registration of LVF hyperspectral data using the proposed method achieves superior accuracy and efficiency.

Star trackers commonly suffer from star point trailing during stellar imaging under dynamic observation conditions. Traditional non-blind deconvolution methods rely on a known Point Spread Function (PSF), whereas blind deconvolution approaches are plagued by a complex solution space and a high tendency to fall into local optima. These drawbacks make it difficult to meet the requirements of high-precision star centroid extraction. To address these challenges, this paper proposes a novel blind restoration method based on physical model guidance and alternating iterative optimization. Firstly, the parameters of the blurred PSF are blindly estimated using image moment analysis, and a motion blur physical model with controllable direction and length is constructed. Secondly, the iterative ideal physical model is embedded as a strong prior into curvature filtering to achieve guided denoising, which effectively suppresses noise while maintaining the original trailing structure. Finally, a dual-layer alternating optimization framework grounded in the ideal physical model was developed. The inner layer employs the Richardson–Lucy (RL) algorithm integrated with intelligent convergence criteria for high-precision image restoration. The outer layer utilizes a gradient descent algorithm equipped with a confidence-based full step-length strategy to optimize PSF parameters. This architecture establishes a self-correcting closed-loop mechanism characterized by iterative image restoration–model refinement cycles. The simulation results demonstrate that the proposed method generally maintains the star centroiding error below 0.1 pixel without prior knowledge of the PSF, with a maximum observed error of 0.105 pixel under the most challenging high-background condition. Its performance is close to non-blind restoration and significantly outperforms traditional blind deconvolution algorithms. It provides an effective solution for high-precision star centroiding under dynamic conditions.

A periodic rotational-scanning panoramic imaging system (PRS imaging system) can acquire large-scale, continuous, and high-resolution panoramic images through rotational scanning. However, non-ideal camera motion during exposure introduces spatially varying motion blur, which degrades image quality and affects subsequent visual interpretation. To address this problem in a self-developed PRS device, this paper proposes an adaptive image deblurring framework based on inertial measurement unit (IMU) assistance and the motion-based motion blur metric (MMBM). First, IMU data collected during exposure are used to calculate the MMBM, which represents the motion blur degree of the current image. The metric is then used to adaptively determine the iteration number of the Richardson–Lucy (R-L) deconvolution algorithm, avoiding unnecessary restoration and reducing artifacts caused by over-restoration. Second, the point spread function (PSF) size is adaptively determined from the camera motion trajectory, and the corresponding PSF is constructed to match different jitter intensities. Finally, an adaptive image partitioning strategy is introduced to handle spatially non-uniform blur caused by camera rotation. Experiments on real images collected by the self-developed dual-spectrum PRS imaging system show that the proposed method achieves stable restoration performance, preserves image naturalness, suppresses unnatural distortions, and reduces computational cost.