Digital Surface Model Generation Research Articles

Estimation of Small-Stream Water Surface Elevation Using UAV Photogrammetry and Deep Learning

Unmanned aerial vehicle (UAV) photogrammetry allows the generation of orthophoto and digital surface model (DSM) rasters of terrain. However, DSMs of water bodies mapped using this technique often reveal distortions in the water surface, thereby impeding the accurate sampling of water surface elevation (WSE) from DSMs. This study investigates the capability of deep neural networks to accommodate the aforementioned perturbations and effectively estimate WSE from photogrammetric rasters. Convolutional neural networks (CNNs) were employed for this purpose. Two regression approaches utilizing CNNs were explored: direct regression employing an encoder and a solution based on prediction of the weight mask by an autoencoder architecture, subsequently used to sample values from the photogrammetric DSM. The dataset employed in this study comprises data collected from five case studies of small lowland streams in Poland and Denmark, consisting of 322 DSM and orthophoto raster samples. A grid search was employed to identify the optimal combination of encoder, mask generation architecture, and batch size among multiple candidates. Solutions were evaluated using two cross-validation methods: stratified k-fold cross-validation, where validation subsets maintained the same proportion of samples from all case studies, and leave-one-case-out cross-validation, where the validation dataset originates entirely from a single case study, and the training set consists of samples from other case studies. Depending on the case study and the level of validation strictness, the proposed solution achieved a root mean square error (RMSE) ranging between 2 cm and 16 cm. The proposed method outperforms methods based on the straightforward sampling of photogrammetric DSM, achieving, on average, an 84% lower RMSE for stratified cross-validation and a 62% lower RMSE for all-in-case-out cross-validation. By utilizing data from other research, the proposed solution was compared on the same case study with other UAV-based methods. For that benchmark case study, the proposed solution achieved an RMSE score of 5.9 cm for all-in-case-out cross-validation and 3.5 cm for stratified cross-validation, which is close to the result achieved by the radar-based method (RMSE of 3 cm), which is considered the most accurate method available. The proposed solution is characterized by a high degree of explainability and generalization.

Digital surface model generation from high‐resolution satellite stereos based on hybrid feature fusion network

AbstractRecent studies have demonstrated that deep learning‐based stereo matching methods (DLSMs) can far exceed conventional ones on most benchmark datasets by both improving visual performance and decreasing the mismatching rate. However, applying DLSMs on high‐resolution satellite stereos with broad image coverage and wide terrain variety is still challenging. First, the broad coverage of satellite stereos brings a wide disparity range, while DLSMs are limited to a narrow disparity range in most cases, resulting in incorrect disparity estimation in areas with contradictory disparity ranges. Second, high‐resolution satellite stereos always comprise various terrain types, which is more complicated than carefully prepared datasets. Thus, the performance of DLSMs on satellite stereos is unstable, especially for intractable regions such as texture‐less and occluded regions. Third, generating DSMs requires occlusion‐aware disparity maps, while traditional occlusion detection methods are not always applicable for DLSMs with continuous disparity. To tackle these problems, this paper proposes a novel DLSM‐based DSM generation workflow. The workflow comprises three steps: pre‐processing, disparity estimation and post‐processing. The pre‐processing step introduces low‐resolution terrain to shift unmatched disparity ranges into a fixed scope and crops satellite stereos to regular patches. The disparity estimation step proposes a hybrid feature fusion network (HF2Net) to improve the matching performance. In detail, HF2Net designs a cross‐scale feature extractor (CSF) and a multi‐scale cost filter. The feature extractor differentiates structural‐context features in complex scenes and thus enhances HF2Net's robustness to satellite stereos, especially on intractable regions. The cost filter filters out most matching errors to ensure accurate disparity estimation. The post‐processing step generates initial DSM patches with estimated disparity maps and then refines them for the final large‐scale DSMs. Primary experiments on the public US3D dataset showed better accuracy than state‐of‐the‐art methods, indicating HF2Net's superiority. We then created a self‐made Gaofen‐7 dataset to train HF2Net and conducted DSM generation experiments on two Gaofen‐7 stereos to further demonstrate the effectiveness and practical capability of the proposed workflow.

SPARSESAT-NERF: DENSE DEPTH SUPERVISED NEURAL RADIANCE FIELDS FOR SPARSE SATELLITE IMAGES

Abstract. Digital surface model generation using traditional multi-view stereo matching (MVS) performs poorly over non-Lambertian surfaces, with asynchronous acquisitions, or at discontinuities. Neural radiance fields (NeRF) offer a new paradigm for reconstructing surface geometries using continuous volumetric representation. NeRF is self-supervised, does not require ground truth geometry for training, and provides an elegant way to include in its representation physical parameters about the scene, thus potentially remedying the challenging scenarios where MVS fails. However, NeRF and its variants require many views to produce convincing scene’s geometries which in earth observation satellite imaging is rare. In this paper we present SparseSat-NeRF (SpS-NeRF) – an extension of Sat-NeRF adapted to sparse satellite views. SpS-NeRF employs dense depth supervision guided by crosscorrelation similarity metric provided by traditional semi-global MVS matching. We demonstrate the effectiveness of our approach on stereo and tri-stereo Pléiades 1B/WorldView-3 images, and compare against NeRF and Sat-NeRF. The code is available at https://github.com/LulinZhang/SpS-NeRF

Solving photogrammetric cold cases using AI-based image matching: New potential for monitoring the past with historical aerial images

With the ongoing digitization in archives, an increasing number of historical data becomes available for research. This includes historical aerial images which provide detailed information about the depicted area. Among the applications enabled by these images are change detection of land use, land cover, glaciers, and coastal environments as well as the observation of land degradation, and natural hazards. Studying the depicted areas and occurring 3D deformations requires the generation of a digital surface model (DSM) which is usually obtained via photogrammetric Structure-from-Motion (SfM). However, conventional SfM workflows often fail in registering historical aerial images due to their radiometric characteristics introduced by digitization, original image quality, or vast temporal changes between epochs. We demonstrate that the feature matching step in the Structure from Motion (SfM) pipeline is particularly crucial. To address this issue, we apply the two synergetic neural network methods SuperGlue and DISK, improving feature matching for historical aerial images. This requires several modifications to enable rotational invariance and leveraging the high resolution of aerial images. In contrast to other studies our workflow does not require any prior information such as DSMs, flight height, focal lengths, or scan resolution which are often no more extent in archives. It is shown that our methods using adapted parameter settings are even able to deal with quasi texture-less images. This enables the simultaneous processing of various kind of mono-temporal and multi-temporal data handled in a single workflow from data preparation over feature matching through to camera parameter estimation and the generation of a sparse point cloud. It outperforms conventional strategies in the number of correct feature matches, number of registered images and calculated 3D points and allows the generation of multi-temporal DSMs with high quality.With the flexibility of the method, it enables the automatic processing of formerly unusable or only to be interactively processed data, e.g. aerial images where the flight route is unknown, or with difficult radiometric properties. This makes it possible to go back even further in time, where the data quality usually decreases, and enables a holistic monitoring and comparison of environments of high interest. The code is made publicly available at https://github.com/tudipffmgt/HAI-SFM.

Digital terrain, surface, and canopy height model generation with dual-baseline low-frequency InSAR over forest areas

Digital terrain, surface, and canopy height model generation with dual-baseline low-frequency InSAR over forest areas

Leveraging Chinese GaoFen-7 imagery for high-resolution building height estimation in multiple cities

Accurate building heights are essential to understand the mechanisms of urban systems and provide new insights into research on infrastructure equality, urban environment, population modeling, etc. However, relatively few studies have been conducted to estimate high-resolution building heights using Chinese GaoFen-7 imagery across multiple cities. In this study, we developed an effective Building Height Estimation method by synergizing Photogrammetry and Deep learning methods (BHEPD) to leverage Chinese multi-view GaoFen-7 (GF-7) images for high-resolution building height estimation. In particular, BHEPD incorporates the StereoNet model and is capable of generating an edge-preserving digital surface model (DSM) from GF-7 images. This improvement enhances the accuracy of heights estimation, particularly for high-rise, high-density, and multi-scale buildings. BHEPD extracted over 700,000 building heights in local areas of Tianjin, Lanzhou, Chongqing, Ningbo, Guangzhou, Foshan, Shenzhen, and Hong Kong. The estimated building heights in the different cities are highly consistent with the reference building height data obtained from field-measured building height data, building floor data, and airborne light detection and ranging (LiDAR) data. The R2 varies from 0.75 to 0.90, while the root mean square error (RMSE) ranges from 4.06 m to 8.06 m. The validation of the estimated building heights over 106,366 buildings using airborne LiDAR height data yielded R2 and RMSE of 0.90 and 6.72 m for Shenzhen and 0.89 and 4.06 m for Hong Kong, respectively. Furthermore, a comparison with the building height estimation based on the traditional DSM generation method (i.e., semi-global matching (SGM) algorithm) confirmed the superiority of BHEPD in alleviating the underestimation of high-rise buildings (>60 m). Overall, BHEPD shows great potential for high-resolution building height estimation and can be expected to reduce the manual workload involved in collecting building height information on large scale.

MULTI-SATELLITE IMAGE ALIGNMENT OVER LARGE AREAS WITH FEATURELESS REGIONS

Abstract. There is a great deal of interest in fusing together the information provided by different satellites for real-time change detection on the surface of the earth. For detecting important types of changes on the ground, it is necessary to inject geometry into the data provided by low-res satellites using multi-view imaging satellites that record each point on the ground from multiple perspectives. Combining these perspectives and generating a DSM (Digital Surface Model) gives us the geometry needed for a more meaningful analysis of satellite data. Before such an analysis can be carried out, it is necessary to align the images from all available satellites. Automatic image alignment, however, requires features on the ground that can be identified and correctly matched across different images using computer vision algorithms. While such features are common in urban areas, that is not always the case in predominantly rural areas that present a more-or-less uniform texture to the sensors. In this paper we present methods for automatic identification and alignment of featureless regions. Featureless regions are identified using point spread maps, which are a byproduct of DSM generation. The subsequent strategy for aligning featureless regions depends on the proportion of featureless regions to feature-rich regions. If most of the AOI (Area of Interest) is feature-rich, we ignore featureless regions when estimating inter-satellite image alignment parameters and apply those parameters to the entire AOI. Finally, we present a technique to propagate and fuse parameters from feature-rich regions to featureless regions.

Study on the Differences between the Extraction Results of the Structural Parameters of Individual Trees for Different Tree Species Based on UAV LiDAR and High-Resolution RGB Images

Light Detection and Ranging (LiDAR) points and high-resolution RGB image-derived points have been successfully used to extract tree structural parameters. However, the differences in extracting individual tree structural parameters among different tree species have not been systematically studied. In this study, LiDAR data and images were collected using unmanned aerial vehicles (UAVs) to explore the differences in digital elevation model (DEM) and digital surface models (DSM) generation and tree structural parameter extraction for different tree species. It was found that the DEMs generated based on both forms of data, LiDAR and image, exhibited high correlations with the field-measured elevation, with an R2 of 0.97 and 0.95, and an RMSE of 0.24 and 0.28 m, respectively. In addition, the differences between the DSMs are small in non-vegetation areas, whereas the differences are relatively large in vegetation areas. The extraction results of individual tree crown width and height based on two kinds of data are similar when all tree species are considered. However, for different tree species, the Cinnamomum camphora exhibits the greatest accuracy in terms of crown width extraction, with an R2 of 0.94 and 0.90, and an RMSE of 0.77 and 0.70 m for LiDAR and image points, respectively. In comparison, for tree height extraction, the Magnolia grandiflora exhibits the highest accuracy, with an R2 of 0.89 and 0.90, and an RMSE of 0.57 and 0.55 m for LiDAR and image points, respectively. The results indicate that both LiDAR and image points can generate an accurate DEM and DSM. The differences in the DEMs and DSMs between the two data types are relatively large in vegetation areas, while they are small in non-vegetation areas. There are significant differences in the extraction results of tree height and crown width between the two data sets among different tree species. The results will provide technical guidance for low-cost forest resource investigation and monitoring.

An Optimized Workflow for Digital Surface Model Series Generation Based on Historical Aerial Images: Testing and Quality Assessment in the Beach-Dune System of Sa Ràpita-Es Trenc (Mallorca, Spain)

We propose an optimized Structure-from-Motion (SfM) Multi-View Stereopsis (MVS) workflow, based on minimizing different errors and inaccuracies of historical aerial photograph series (1945, 1979, 1984, and 2008 surveys), prior to generation of elevation-calibrated historical Digital Surface Models (hDSM) at 1 m resolution. We applied LiDAR techniques on Airborne Laser Scanning (ALS) point clouds (Spanish PNOA LiDAR flights of 2014 and 2019) for comparison and validation purposes. Implementation of these products in multi-temporal analysis requires quality control due to the diversity of sources and technologies involved. To accomplish this, (i) we used the Mean Absolute Error (MAE) between GNSS-Validation Points and the elevations observed by DSM-ALS to evaluate the elevation accuracy of DSM-ALS generated with the LAScatalog processing engine; (ii) optimization of the SfM sparse clouds in the georeferencing step was evaluated by calculating the Root Mean Square Error (RMSE) between the Check Points extracted from DSM-ALS and the predicted elevations per sparse cloud; (iii) the MVS clouds were evaluated by calculating the MAE between ALS-Validation Points and the predicted elevations per MVS cloud; iv) the accuracy of the resulting historical SfM-MVS DSMs were assessed using the MAE between ALS-Validation Points and the observed elevations per historical DSM; and (v) we implemented a calibration method based on a linear correction to reduce the elevation discrepancies between historical DSMs and the DSM-ALS 2019 reference elevations. This optimized workflow can generate high-resolution (1 m pixel size) hDSMs with reasonable accuracy: MAE in z ranges from 0.41 m (2008 DSM) to 5.21 m (1945 DSM). Overall, hDSMs generated using historical images have great potential for geo-environmental processes monitoring in different ecosystems and, in some cases (i.e., sufficient image overlapping and quality), being an acceptable replacement for LiDAR data when it is not available.

Deep Neural Network Regression for Normalized Digital Surface Model Generation With Sentinel-2 Imagery

Deep Neural Network Regression for Normalized Digital Surface Model Generation With Sentinel-2 Imagery

Digital surface model generation from aerial imagery using bridge probability relaxation matching

Optical image dense matching is a crucial step in the process of generating digital surface models (DSMs). Many existing dense matching methods have adopted pixelwise matching strategy and have achieved precise matching results; however, the methods are time consuming and have limited efficiency in high surveying and mapping production. We introduce a bridge probability relaxation matching method for automatic DSM generation. The method adopts a coarse-to-fine hierarchical strategy and achieves high matching accuracy and fast processing speed simultaneously. This method builds a self-adaptive disparity surface model in a local area and constrains the disparity surface using the spatial relationship between feature points and adjacent pixels. Finally, the disparity is optimized by calculating the increment of the relaxation iteration probability. Experiments are based on different areas with different textures and terrain types. Compared with the DSMs derived from semi-global matching, our proposed approach achieves high levels of accuracy and efficiency in automatic DSM generation.

Shading aware DSM generation from high resolution multi-view satellite images

ABSTRACT In many cases, the Digital Surface Models (DSMs) and Digital Elevation Models (DEMs) are obtained with Light Detection and Ranging (LiDAR) or stereo matching. As an active method, LiDAR is very accurate but expensive, thus often limiting its use in small-scale acquisition. Stereo matching is suitable for large-scale acquisition of terrain information as the increase of satellite stereo sensors. However, underperformance of stereo matching easily occurs in textureless areas. Accordingly, this study proposed a Shading Aware DSM GEneration Method (SADGE) with high resolution multi-view satellite images. Considering the complementarity of stereo matching and Shape from Shading (SfS), SADGE combines the advantage of stereo matching and SfS technique. First, an improved Semi-Global Matching (SGM) technique is used to generate an initial surface expressed by a DSM; then, it is refined by optimizing the objective function which modeled the imaging process with the illumination, surface albedo, and normal object surface. Different from the existing shading-based DEM refinement or generation method, no information about the illumination or the viewing angle is needed while concave/convex ambiguity can be avoided as multi-view images are utilized. Experiments with ZiYuan-3 and GaoFen-7 images show that the proposed method can generate higher accuracy DSM (12.5–56.3% improvement) with sound overall shape and temporarily detailed surface compared with a software solution (SURE) for multi-view stereo.

A linear pushbroom satellite image epipolar resampling method for digital surface model generation

Epipolar resampling, also known as stereo rectification, is a prerequisite for optical satellite image stereo matching in digital surface model (DSM) generation. In epipolar resampling, image pairs are warped and thus can be matched pixel-to-pixel along the epipolar line. Different from the pinhole imaging model of frame cameras, the multi-center projection model of linear pushbroom sensors does not obey the epipolar geometric constraint, making it impossible to conjugate epipolar lines from stereo images. The epipolar resampling methods can resolve some of the inherent epipolar error, but not completely. The topographic relief brings high disparity values between epipolar image pairs, posing a challenge to the subsequent stereo matching to recover disparity map. In this paper, we introduce a novel epipolar resampling method for satellite imagery based on ortho rectification. This method takes full advantage of a rational function model to establish a connection between image space and object space coordinates, and thereby transform the two-dimensional disparity in ortho rectification products into one dimension. We propose a coarse-to-fine DSM generation pipeline to overcome the high-disparity-problem without additional time costs or precision decrement. We conducted experiments on various satellite stereo images, and validated both the extreme suppression on epipolar error as well as the feasibility of the proposed coarse-to-fine DSM generation pipeline. The experimental results show that the generated epipolar image pairs from our proposed method effectively solved the high disparity problem. Furthermore, the novel DSM generation method obtained an internal error below one pixel for a rectified stereo image pair in mountain area, and the lowest vertical error in the IARPA MVS dataset when compared with the common open-source programs and commercial software.

EVALUATION OF AN UNMANNED AERIAL VEHICLE (UAV) FOR MEASURING AND MONITORING NATURAL DISASTER RISK AREAS

Abstract. This work presents an evaluation of a small UAV-RPAS-PPK to be used in the generation of digital surface models (DSM), without the need for control points, having as main application the monitoring of disaster risk areas (landslide and flooding). The areas to be measured are difficult to access, which prevents or makes access to the land difficult. In this evaluation, a study area of approximately 13 km2 was flown over, an average pixel of 11.6 cm and a total of 417 photos. The equipment used to acquire the images was a SenseFly eBee X, equipped with GNSS PPK for Direct Georeferencing (DG) and a camera model S.O.D.A. In all, 42 ground checkpoints were measured using a dual-frequency GNSS receiver. For both the measurement of the checkpoints and for the Direct Georeferencing (DG) base of the Unmanned Aerial Vehicles (UAV), a relative processing was performed, using the Brazilian Network for Continuous Monitoring (RBMC) as a reference. With this evaluation, it was possible to achieve a result (RMSE) for phototriangulation better than 1.2 pixels for horizontal and 1.5 pixels for vertical, without the need to measure any control points on the ground.

ICESAT-2 LASER ALTIMETRY DATA-GUIDED HIGH-ACCURACY POSITIONING OF SATELLITE STEREO IMAGES

Abstract. Positioning accuracy is a key indicator that restricts the accuracy of satellite image mapping. Laser altimetry acquires surface height in decimeters or higher accuracy, providing high-accuracy topographic information for satellite photogrammetry. This study proposes a four-step high-accuracy positioning method called laser altimetry data-guided stereo images positioning (LGSP): photon outlier removal, digital surface model (DSM) generation by a variant of the tube-shape semi-global matching (tSGM), fast point cloud alignment, and DSM and rational function model (RFM) refining. First, outliers of the laser altimetry data are filtered out via confidence selection and open access DSM constraints. Second, the DSM around high-accuracy photons is generated via the variant of tSGM. Third, fast point cloud alignment is performed to align the DSM with laser altimetry data. Finally, we propose a high-resolution stereo model refinement method based on alignment parameters. Experiments on two satellite stereo datasets with different loads, resolutions, and topographies show that LGSP improved the planimetry accuracy of satellite stereo images after adding laser altimetry data. What’s more, LGSP significantly improved the height accuracy of satellite images. Specifically, LGSP reduced the height errors from the original 5.99 m to 1.00 m for the Ziyuan-3 stereo dataset and from the original 20.39 m to 0.83 m for the Gaofen-7 stereo dataset, and slightly outperformed bundle adjustment in which four ground control points were deployed around the four corners of the stereo images. RFM refining based on alignment parameters has been made publicly available at https://mega.nz/file/EFQASJ6A#BKjoupHVshylQR4wCxGd9BDtg08bhy-_aY-c_rHnl-0.

Radargrammetric DSM Generation by Semi-Global Matching and Evaluation of Penalty Functions

Radargrammetry is a useful approach to generate Digital Surface Models (DSMs) and an alternative to InSAR techniques that are subject to temporal or atmospheric decorrelation. Stereo image matching in radargrammetry refers to the process of determining homologous points in two images. The performance of image matching influences the final quality of DSM used for spatial-temporal analysis of landscapes and terrain. In SAR image matching, local matching methods are commonly used but usually produce sparse and inaccurate homologous points adding ambiguity to final products; global or semi-global matching methods are seldom applied even though more accurate and dense homologous points can be yielded. To fill this gap, we propose a hierarchical semi-global matching (SGM) pipeline to reconstruct DSMs in forested and mountainous regions using stereo TerraSAR-X images. In addition, three penalty functions were implemented in the pipeline and evaluated for effectiveness. To make accuracy and efficiency comparisons between our SGM dense matching method and the local matching method, the normalized cross-correlation (NCC) local matching method was also applied to generate DSMs using the same test data. The accuracy of radargrammetric DSMs was validated against an airborne photogrammetric reference DSM and compared with the accuracy of NASA’s 30 m SRTM DEM. The results show the SGM pipeline produces DSMs with height accuracy and computing efficiency that exceeds the SRTM DEM and NCC-derived DSMs. The penalty function adopting the Canny edge detector yields a higher vertical precision than the other two evaluated penalty functions. SGM is a powerful and efficient tool to produce high-quality DSMs using stereo Spaceborne SAR images.

Digital surface model generation from high-resolution satellite stereo imagery based on structural similarity

In this study, we proposed an automatic approach to extract DSMs from VHR satellite imagery. This study aims to apply the histogram of orientated phase congruency (HOPC) descriptor and SIFT-Flow algorithm to match multi-view VHR satellite images densely and extract detailed DSMs competitive with ones generated from Light Detection and Ranging (LiDAR) point clouds. The HOPC descriptor relies on detecting features by calculating the phase congruency model, which allows the image features to be highly robust against illumination changes. Moreover, we developed an automatic algorithm to extract precise and homogenously distributed tie-points and subsequently conduct bias correction of the Rational Polynomial Coefficients (RPC). We tested our proposed algorithm on four WorldViw-3 datasets captured from different viewpoints, and compared the results with the DSMs obtained by the Semi-Global Matching (SGM) algorithm. The experimental results indicated the proficiency of our proposed method in generating accurate and reliable DSMs using VHR satellite data.

Uncertainty-Guided Depth Fusion from Multi-View Satellite Images to Improve the Accuracy in Large-Scale DSM Generation

The generation of digital surface models (DSMs) from multi-view high-resolution (VHR) satellite imagery has recently received a great attention due to the increasing availability of such space-based datasets. Existing production-level pipelines primarily adopt a multi-view stereo (MVS) paradigm, which exploit the statistical depth fusion of multiple DSMs generated from individual stereo pairs. To make this process scalable, these depth fusion methods often adopt simple approaches such as the median filter or its variants, which are efficient in computation but lack the flexibility to adapt to heterogenous information of individual pixels. These simple fusion approaches generally discard ancillary information produced by MVS algorithms (such as measurement confidence/uncertainty) that is otherwise extremely useful to enable adaptive fusion. To make use of such information, this paper proposes an efficient and scalable approach that incorporates the matching uncertainty to adaptively guide the fusion process. This seemingly straightforward idea has a higher-level advantage: first, the uncertainty information is obtained from global/semiglobal matching methods, which inherently populate global information of the scene, making the fusion process nonlocal. Secondly, these globally determined uncertainties are operated locally to achieve efficiency for processing large-sized images, making the method extremely practical to implement. The proposed method can exploit results from stereo pairs with small intersection angles to recover details for areas where dense buildings and narrow streets exist, but also to benefit from highly accurate 3D points generated in flat regions under large intersection angles. The proposed method was applied to DSMs generated from Worldview, GeoEye, and Pleiades stereo pairs covering a large area (400 km2). Experiments showed that we achieved an RMSE (root-mean-squared error) improvement of approximately 0.1–0.2 m over a typical Median Filter approach for fusion (equivalent to 5–10% of relative accuracy improvement).

Robust Correction of Relative Geometric Errors Among GaoFen-7 Regional Stereo Images Based on Posteriori Compensation

Correcting the relative geometric errors is an essential step in the generation of digital ortho maps and digital surface models from GaoFen-7 (GF-7) regional images, in which the block adjustment (BA) technology is always adopted. However, the traditional BA methods are typically ineffective and not universal due to their dependence on sufficient control data. Although recently developed BA methods using a large number of observation images can substantially eliminate the additional control data, they are not suitable for the BA with few images in the absence of sufficient control data, which is a situation that is often encountered in practical applications. In such situation, the inevitable elevation error caused by the initial positioning error of images will transfer and accumulate in the horizontal direction of the block, resulting in the inconsistent geometric accuracies among images. Aiming at this problem, this article proposed a practical method of combining a stable free BA and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">a posteriori</i> compensation. Through the stepwise correction and integration of geometric errors, the elevation error and its cumulative effect in free BA could be eliminated at a small control cost, and a robust result with consistent accuracy could be achieved, even with only a few observation images. This method was validated through experiments performed on GF-7 stereo images, and the satisfactory results indicated that it effectively restrained error accumulation and improved the geo-positioning consistency of regional GF-7 images.

ИССЛЕДОВАНИЕ РЕЗУЛЬТАТОВ ОБРАБОТКИ ПОЛУЧЕННЫХ РАЗЛИЧНЫМИ МОДЕЛЯМИ НАЗЕМНЫХ ЛАЗЕРНЫХ СКАНЕРОВ ДАННЫХ ДЛЯ КОНТРОЛЯ КАЧЕСТВА РЕМОНТА АВТОМОБИЛЬНЫХ ДОРОГ

One of the tasks solved through the application of terrestrial laser scanning technology is the quali-ty control of road works. With the stated accuracy of measuring distances of the order of several mil-limeters, terrestrial laser scanning allows conducting continuous control of the road construction or repair. Nowadays a large number of different terrestrial laser scanners have been produced, each of which allows identifying most of the roadway defects and determining its flatness due to the ability to perform high-density surveys. The reliability of determining flatness and detecting defects from scan-ning data can vary significantly because of the fact that almost every laser scanner has its own unique technical characteristics. To achieve the highest reliability values, it is necessary to adhere both to techniques of performing the field part of all surveying works and to data processing techniques, which consist in obtaining point clouds and constructing digital road surface models. If the main es-sence of these works does not depend on the used laser scanner model, then the individual stages of the applied techniques may differ. 2 models of laser scanners produced by different manufacturers are discussed to solve the task of asphalt pavement laying quality. Their comparative characteristics and peculiarities of performing all surveying works with their usage are given. The results of digital road surface model generation are analyzed, and peculiarities of processing of data obtained with var-ious laser scanners are discussed. Based on the results of the analysis, it is shown that not all models of laser scanners can be suitable for controlling the quality of road repairs. The technique of prelimi-nary data processing is presented, which makes it possible to increase reliability of solving this task for laser scanners with insufficient quality of obtained point clouds.