Search Results (306)

As unmanned aerial vehicles (UAVs) have increasingly become aerial imaging platforms, the reliance of traditional photogrammetry on ground control points (GCPs) remains a major limitation in complex terrain, confined spaces, and scenarios where control points are difficult to deploy. To address this issue, this study proposes a proof-of-concept framework for free-flight photogrammetry based on the fusion of monocular vision and sensor-group displacement information. The framework employs a rigid point set station-displacement algorithm to compute the exterior orientation elements between adjacent measurement stations, providing a feasible approach for multi-station pose propagation under control-point-free conditions. In addition, a composite weighting strategy incorporating the effects of optical distortion and rigid-body consistency evaluation is developed to improve the rational use of point-set information during station-displacement computation. To evaluate the feasibility of the proposed method, numerical simulations were first conducted to analyze the variation patterns of exterior orientation computation and target-point reconstruction under different sampling intervals and error conditions. Subsequently, an indoor controlled bench-top experimental platform was constructed to physically validate the complete workflow of the proposed method. The bench-top experimental results show that the overall mean three-dimensional positioning error of the two cross-station image pairs was 15.450 mm, and the maximum three-dimensional positioning error was 36.685 mm. The mean absolute distance errors for station 1–station 2 and station 1–station 3 were 9.230 mm and 12.436 mm, respectively. These results indicate that the proposed method can complete station-displacement-based exterior orientation computation and three-dimensional target measurement in a controlled physical scenario, demonstrating clear proof-of-concept significance. It should be noted that UAV measurement experiments under real flight conditions have not yet been completed in this study, and further validation on an actual UAV platform is still required. Full article

With the rapid and ongoing expansion of urban areas, the need for accurate, reliable, and regularly updated topographic maps has become increasingly critical for planning and sustainable development. While traditional aerial photogrammetry—whether analog or digital—has long been the standard for such tasks, it remains costly, time-consuming, and logistically demanding, particularly when large or inaccessible regions are involved. This study proposes an alternative approach based on very-high-resolution satellite imagery, focusing specifically on data acquired from Morocco’s Mohammed VI A and B satellites. The research evaluates the capacity of this satellite imagery to support large-scale topographic mapping, both in terms of geometric accuracy and the ability to identify essential urban features. To validate the results, we conducted a comparative analysis of satellite data with conventional photogrammetric imagery from analog cameras (RMK TOP) and digital sensors (ADS, DMC), using ground control points (GCPs) and differential GPS (DGPS) measurements for calibration and accuracy assessment. The outcomes demonstrate that planimetric accuracy from satellite imagery meets the required standards for mapping at 1:10,000 and 1:5000 scales. However, altimetric accuracy is closer to the upper permissible limits, especially in applications requiring finer detail. While major urban elements such as roads, buildings, and vegetation are well identified, smaller infrastructure components, such as power lines, remain challenging to detect. Despite these limitations, the study highlights the growing potential of satellite imagery as a cost-effective and operationally efficient alternative to traditional methods, particularly in rapidly evolving urban environments where frequent map updates are essential. Integration with GeoAI workflows is identified as a key direction for future research and is not part of the current methodology. Full article

Ground Control Points (GCPs) are essential for accurate georeferencing of optical imagery; however, their selection is often heuristic and affected by temporal changes in image geometry. This challenge is particularly acute for Google Earth imagery, where acquisition conditions and mosaicking processes vary over time. This paper presents a multi-epoch robust framework for the automatic design of GCP networks to precisely georeference multi-temporal Google Earth images. GCP selection is formulated within an affine optimal experimental design setting, in which candidate configurations are evaluated against the most challenging acquisition epoch to promote consistency over time. A hybrid DI-optimality criterion balances transformation stability and interior prediction accuracy without requiring interior control points. The framework also includes an automated method for determining the optimal number of GCPs using marginal-gain stopping and cost-regularized μ-sweep analysis. Experiments on two urban case studies show that compact, well-conditioned GCP networks can match the accuracy of larger heuristic networks and achieve top 10% root-mean-square error (RMSE) performance on a random feasible subset benchmark. Results demonstrate that a carefully designed GCP network can greatly reduce the number of control points needed while maintaining stable geometric performance across acquisition sessions. Full article

We investigate the potential of frequent repeat imagery acquired by the PlanetScope Dove small satellite constellation to overcome temporal and spatial limitations in automated surface topography mapping. While individual PlanetScope Dove stereo pairs produce low-quality Digital Surface Models (DSMs) with large height uncertainties, the high temporal frequency enables multiple DSMs to enhance accuracy through multiple-pair image matching. We present a fully automated SETSM framework by improving the quality of PlanetScope Dove DSMs based on SETSM Multi-Pair Matching Procedure (SETSM MMP). This framework enhances stereo pair quality through an optimized stereo pair selection by sequential conditional filtering and a Weighted Stereo Pair Index (WSPI). A novel inter-plane vertical coregistration, which minimizes scaling errors between single stereo pair DSMs, was developed to improve consistency and accuracy in DSM quality without reference surfaces. Applied to the cloud-obscured Pantasma crater region in Nicaragua, the optimized stereo pair selection automatically selects well-defined stereo pairs. The inter-plane vertical coregistration without existing reference surfaces achieves up to a 43% Root Mean Square Error (RMSE) reduction and 26% improvement in distribution within a 5 m vertical error. DSM quality correlated strongly with tile size, stereo pair convergence angle, asymmetric angle and terrain-dependent scale variability. The proposed framework provides fully automatic, high quality PlanetScope Dove DSMs without Ground Control Points (GCPs). Full article

This study examines residual low-order (linear and constant) phase errors in interferometric synthetic aperture radar (InSAR) when compact, high-frequency radar sensors are mounted on commercial uncrewed aerial vehicles (UAVs). Although higher carrier frequencies and shorter standoff ranges enable fine-resolution interferometry, the same characteristics—together with UAV platform instability—make the system highly vulnerable to motion-induced phase errors, which can significantly degrade or even invalidate DEM reconstruction. This paper first quantifies the admissible motion-error bounds for reliable multi-baseline phase-gradient estimation, and then introduces a post-autofocus correction scheme that estimates the residual linear term from the interferometric fringe frequency and refines it via an FFT-based correlation objective, while the constant term is calibrated using ground control points (GCPs). The method is validated through simulations of a 24 GHz UAV demonstrator. To the best of our knowledge, this work provides the first post-autofocus demonstration of linear-and-constant residual-error mitigation for UAV-based high-frequency multi-baseline InSAR. In the considered K-band setting, the proposed approach reduces the DEM error from 42 m to 0.2 m (≈98% improvement). Full article

Rotating scanning systems are capable of acquiring ultra-wide swath satellite imagery, but they suffer from significant positioning accuracy degradation due to complex geometric distortions and the difficulty of obtaining ground control points (GCPs) over vast areas. To address these issues, this paper proposes a precise positioning method based on multi-source satellite data fusion. By comprehensively utilizing high-resolution images from ZY-3 and GF-2 satellites alongside DEM data, we establish a framework that integrates grid-based feature point extraction, high-precision matching, and multi-image joint adjustment. Specifically, we introduce a matching strategy combining geometric constraints with Least Squares Minimization (LSM) and a robust joint adjustment model to suppress geometric distortions. Experimental validation was conducted using a dataset covering the Beijing area. The results demonstrate that after joint adjustment, the planar accuracy of the imagery reached 4.01 m, and the edge matching Root Mean Square Error (RMSE) between adjacent images was 2.52 m. Furthermore, the cooperative positioning accuracy for segmented simulation data achieved 4.68 m in mountainous areas and 5.22 m in plain areas, meeting the requirements for meter-level positioning. These results verify the effectiveness of multi-source cooperative adjustment in correcting geometric distortions and significantly improving the positioning accuracy of rotating scanning imagery. Full article

Monitoring steep slopes in mountainous canyon areas has always been a challenging problem, especially during the construction of large hydropower projects. Effective monitoring is crucial for construction safety and operational security. However, under complex terrain conditions, existing monitoring methods have significant limitations and cannot comprehensively and accurately cover steep slopes. To address the above challenges, this study proposes a multi-temporal UAV-based photogrammetric offset tracking (POT) monitoring method assisted by terrestrial laser scanning (TLS), which is primarily applicable to rocky and texture-rich steep slopes. This method utilizes TLS point cloud data to provide supplementary ground control points (TLS-GCPs) for UAV image modeling, effectively overcoming the difficulty of deploying conventional RTK ground control points (RTK-GCPs) on high and steep slopes, thereby significantly improving the accuracy of UAV-based Structure-from-Motion (SfM) models. In a case study at a hydropower station, we employed TLS-assisted UAV modeling to produce high-precision UAV images. Using POT technology, we successfully identified signs of slope deformation between January 2024 and December 2024. Comparative experiments with traditional algorithms demonstrated that in areas where RTK-GCPs cannot be deployed, this method greatly enhances UAV modeling accuracy, fully meeting the monitoring requirements for steep slopes in complex terrains. Full article

Photogrammetric reconstruction from indoor imagery requires either labor-intensive ground control points (GCPs) or positioning sensor integration. While global navigation satellite system technology revolutionized aerial photogrammetry by enabling direct georeferencing through integrated sensor orientation (ISO), indoor environments lack an equivalent positioning solution. Before indoor positioning systems can be adopted for photogrammetric applications, their fundamental measurement precision must be established. This study characterizes the repeatability and temporal stability of the ZeroKey Quantum real-time location system (RTLS) as a prerequisite to testing reconstruction accuracy when RTLS measurements provide camera pose constraints in photogrammetric bundle adjustment. Through systematic tripod-mounted observations across 30 test locations in a controlled laboratory environment, optimal data collection protocols were determined, temporal stability was investigated, and measurement precision was quantified. An automated position-based stationary detection algorithm using a 20 mm threshold successfully identified all 30 stationary periods for durations of 30 s or less. Optimal duration analysis revealed that 1 s observation windows achieve 3 mm position precision and 1° orientation precision after brief settling, enabling practical workflows with worst-case total collection time of 2.5 s per station. Per-axis uncertainties were quantified as 1.6 mm, 1.7 mm, and 1.1 mm root mean square (RMS) for position and 0.08°, 0.09°, and 0.07° RMS for orientation. These findings demonstrate that ultrasonic RTLS achieves millimeter-level position repeatability and sub-degree orientation repeatability, establishing the measurement precision necessary to justify subsequent accuracy testing through photogrammetric bundle adjustment. Full article

Coastal environments are fragile ecosystems threatened by various factors, both natural and anthropogenic. The preservation and protection of these environments, and in particular, the sand dune systems, which contribute significantly to the defense of the inland from flooding, require continuous monitoring. To this end, high-resolution and high-precision multitemporal data acquired with various techniques can be used, such as, among other things, the global navigation satellite system (GNSS) using the network real-time kinematic (NRTK) approach to acquire 3D points, UAS-based structure-from-motion photogrammetry (SfM), terrestrial laser scanning (TLS), and handheld mobile laser scanning (HMLS)-based light detection and ranging (LiDAR). These techniques were used in this work for the 3D survey of a portion of vegetated sand dunes in the Caleri area (Po River Delta, northern Italy) to assess their applicability in complex environments such as coastal vegetated dune systems. Aerial-based and ground-based acquisitions allowed us to produce point clouds, georeferenced using common ground control points (GCPs), measured both with the GNSS NRTK method and the total station technique. The 3D data were compared to each other to evaluate the accuracy and performance of the different techniques. The results provided good agreement between the different point clouds, as the standard deviations of the differences were lower than 9.3 cm. The GNSS NRTK technique, used with the kinematic approach, allowed for the acquisition of the bare-ground surface but at a cost of lower resolution. On the other hand, the HMLS represented the poorest ability in the penetration of vegetation, providing 3D points with the highest elevation value. UAS-based and TLS-based point clouds provided similar average values, with significant differences only in dense vegetation caused by a very different platform of acquisition and point of view. Full article

Coastal sand dunes, shaped by aeolian and marine processes, are critical to natural ecosystems and human societies, making their morphological monitoring essential for effective conservation. However, large-scale, high-precision monitoring of topographic change remains a persistent challenge, a challenge that advanced sensing technologies can address. In this study, we propose an integrated, sensor-based approach using a UAV-mounted light detection and ranging (LiDAR) system, combined with a GNSS-RTK positioning unit and a novel ground control point (GCP) design to acquire high-resolution topographic data. Field surveys were conducted at four time points between October 2022 and February 2023 in the Tottori Sand Dunes, Japan. The digital elevation models (DEMs) derived from LiDAR point clouds achieved centimeter-level accuracy, enabling reliable detection of subtle topographic changes. Analysis of DEM differencing revealed that wind-driven sand deposition and erosion resulted in elevation changes of up to 0.4 m. These results validate the efficacy of the UAV-LiDAR sensor system for high-resolution, multitemporal monitoring of coastal sand dunes, highlighting its potential to advance the development of environmental sensing frameworks and support data-driven conservation strategies. Full article

Synthetic Aperture Radar (SAR) images generated via range-Doppler (RD) model-based geometric correction often suffer from non-negligible systematic geolocation errors due to cumulative impacts of platform positioning inaccuracies, payload time synchronization offsets, and atmospheric propagation delays. These errors limit the applicability of SAR data in high-precision geometric applications, especially in scenarios where ground control points (GCPs)—traditionally used for calibration—are inaccessible or costly to acquire. To address this challenge, this study proposes a novel GCP-free high-precision geolocation method based on multi-view SAR image fusion, integrating outlier detection, weighted fusion, and refined estimation strategies. The method first establishes a positioning error correlation model for homologous point pairs in multi-view SAR images. Under the assumption of approximately equal positioning errors, initial systematic error estimates are obtained for all arbitrary dual-view combinations. It then identifies and removes outlier images with inconsistent systematic errors via coefficient of variation analysis, retaining a subset of multi-view images with stable calibration parameters. A weighted fusion strategy, tailored to the geometric error propagation model, is applied to the optimized subset to balance the influence of angular relationships on error estimation. Finally, the minimum norm least-squares method refines the fusion results to enhance consistency and accuracy. Validation experiments on both simulated and actual airborne SAR images demonstrate the method’s effectiveness. For actual measured data, the proposed method achieves an average positioning accuracy improvement of 84.78% compared with dual-view fusion methods, with meter-level precision. Ablation studies confirm that outlier removal and refined estimation contribute 82.42% and 22.75% to accuracy gains, respectively. These results indicate that the method fully leverages multi-view information to robustly estimate and compensate for 2D systematic errors (range and azimuth), enabling high-precision planar geolocation of airborne SAR images without GCPs. Full article

Unmanned Aerial Vehicle (UAV) photogrammetry technique offers significant potential for generating highly detailed digital surface models (DSM) of gullies. However, different flight schemes can considerably influence measurement accuracy. The objectives were (i) to evaluate the influences of flight altitude, photo overlap, Ground Control Points (GCPs), and other environmental factors on the accuracy of the UAV-derived DSMs and (ii) to analyze the main factors affecting the accuracy of UAV gully monitoring and explore flight schemes that balance accuracy and efficiency. The results indicated that DSM accuracy improved markedly as the number of GCPs increased from 0 to 3, with consideration given to both horizontal and vertical distribution. However, further increases in the number of GCPs did not lead to significant improvements. The accuracy of DSMs increased with a decrease in the flight altitude, but was not substantially affected by photo overlap when it exceeded 50%/40% The accuracy of DSM was significantly reduced by shadows, and flight altitude rather than slope gradient was identified as the key factor leading to high-error checkpoints (error > 0.1 m). The proportion of point clouds penetrating tree canopies decreased when the flight altitude was 150 m or higher, which could help reduce the influence of vegetation on the accuracy of DSMs. In general, with a reasonable spatial distribution of GCPs, flight altitude is the primary factor affecting monitoring accuracy. However, when balancing accuracy and efficiency, the optimal flight scheme was determined to be a flight altitude of 70 m, photo overlap of 80%/70%, and nine GCPs. Full article

Real-time kinematic (RTK) unmanned aerial vehicles (UAVs) have become more popular in recent years, mostly because they can reduce the number of ground control points (GCPs) that have to be measured in the field and are required for aerial triangulation. Additionally, thanks to RTK technology, every image has its exterior orientation parameters measured with centimeter accuracy; thus, the block is more stable and there is a lower risk of some geometric distortions appearing within the block, especially in its central part. In this article, the influence of RTK observations on image orientation is analyzed based on a planned UAV test field in Józefosław, near Warsaw, Poland. As part of the experiment, UAV flights with DJI Phantom 4 RTK and DJI Phantom 4 Pro V2.0 were conducted, and 38 GCPs were located in the area. The results show that RTK observations from UAVs can significantly improve the accuracy of aerial triangulation, as inclusion of oblique images also does. For Phantom 4 RTK images, a single GCP was generally sufficient to achieve satisfactory accuracy, whereas six GCPs were required for the Phantom 4 Pro V2.0. Full article

Bundle adjustment without Ground Control Points (GCPs) using stereo remote sensing images represents a reliable and efficient approach for realizing the demand for regional and global mapping. This paper proposes a bundled-images based geo-positioning method that leverages a Kalman filter to effectively integrate new image observations with their corresponding historical bundled images. Under the assumption that the noise follows a Gaussian distribution, a linear mean square estimator is employed to orient the new images. The historical bundled images can be updated with posterior covariance information to maintain consistent accuracy with the newly oriented images. This method employs recursive computation to dynamically orient the new images, ensuring consistent accuracy across all the historical and new images. To validate the proposed method, extensive experiments were carried out using two satellite datasets comprising both homologous (IKONOS) and heterogeneous (TH-1 and ZY-3) sources. The experiment results reveal that without using GCPs, the proposed method can meet 1:50,000 mapping standards with heterogeneous TH-1 and ZY-3 datasets and 1:10,000 mapping accuracy requirements with homologous IKONOS datasets. These experiments indicate that as the bundled images expand further, the image quantity growth no longer results in substantial improvements in precision, suggesting the presence of an accuracy ceiling. The final positioning accuracy is predominantly influenced by the initial bundled image quality. Experimental evidence suggests that when using the proposed method, the bundled image sets should exhibit superior precision compared to subsequently new images. In future research, we will expand the coverage to regional or global scales. Full article

This study evaluates the positional accuracy of Global Navigation Satellite Systems (GNSS) and Unmanned Aerial vehicle (UAV)-based LiDAR systems in terrain modeling, using a total station as a reference. The research was conducted over 17 Ground Control Points (GCPs), with measurements obtained using a CHCNAV i50 GNSS receiver and a DJI Zenmuse L1 Light Detection and Ranging (LiDAR) sensor mounted on a UAV. Accuracy was assessed for horizontal (X, Y) and vertical (Z) components by comparing the results against total station data. Errors were quantified using statistical metrics such as Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and RMS at 1σ. GNSS exhibited superior horizontal accuracy with an RMS 1σ of 1.1 cm, while LiDAR achieved 1.7 cm. In contrast, GNSS outperformed LiDAR in vertical precision, achieving a 1σ RMS of 6.4 cm compared to 6.6 cm for LiDAR. These findings align with manufacturer specifications and international standards such as those of the American Society for Photogrammetry and Remote Sensing (ASPRS). The results highlight that GNSS is preferable for applications requiring high horizontal precision, while LiDAR is better suited for vertical modeling and terrain analysis. The combination of both systems may offer enhanced results for comprehensive geospatial surveys. Overall, both technologies demonstrated sub-decimetric accuracy suitable for precision agriculture, civil engineering, and environmental monitoring. Full article