Accuracy Of Digital Surface Models Research Articles

Constraining the Geometry of NeRFs for Accurate DSM Generation from Multi-View Satellite Images

Neural Radiance Fields (NeRFs) are an emerging approach to 3D reconstruction that use neural networks to reconstruct scenes. However, its applications for multi-view satellite photogrammetry, which aim to reconstruct the Earth’s surface, struggle to acquire accurate digital surface models (DSMs). To address this issue, a novel framework, Geometric Constrained Neural Radiance Field (GC-NeRF) tailored for multi-view satellite photogrammetry, is proposed. GC-NeRF achieves higher DSM accuracy from multi-view satellite images. The key point of this approach is a geometric loss term, which constrains the scene geometry by making the scene surface thinner. The geometric loss term alongside z-axis scene stretching and multi-view DSM fusion strategies greatly improve the accuracy of generated DSMs. During training, bundle-adjustment-refined satellite camera models are used to cast rays through the scene. To avoid the additional input of altitude bounds described in previous works, the sparse point cloud resulting from the bundle adjustment is converted to an occupancy grid to guide the ray sampling. Experiments on WorldView-3 images indicate GC-NeRF’s superiority in accurate DSM generation from multi-view satellite images.

Conceptual Study and Performance Analysis of Tandem Multi-Antenna Spaceborne SAR Interferometry

Multi-baseline synthetic aperture radar interferometry (InSAR), capable of mapping 3D surface model with high precision, is able to overcome the ill-posed problem in the single-baseline InSAR. Current tandem SAR mission utilizes a two-stage global coverage to get the dual-baseline interferograms, which achieves the trade-off between the unwrapping errors and height precision. However, the baseline adjustment will decrease the timeliness of the data acquisition, which is not suitable for monitoring temporal changes of the ground targets. Designing a SAR mission with the single-pass multi-baseline acquisition will improve the practical capability in fast 3D reconstruction. Following the asymptotic 3D phase unwrapping proposed for the airborne array InSAR system, it is possible to get a reliable 3D reconstruction using very sparse acquisitions but the interferograms should follow the optimal baseline configuration. In this article, a new concept of tandem multi-antenna SAR interferometry system for acquiring optimal single-pass multi-baseline interferograms is proposed. Two indicators, i.e., expected relative height precision and successful phase unwrapping rate, are selected to optimize the system parameters. Additionally, taking the satellites with two antennas as an example, the performances of various baseline configurations in typical scenarios and the impact of different error sources are investigated correspondingly. The simulation-based experiments demonstrate that the proposed system acquires the optimal MB interferograms for asymptotic 3D phase unwrapping, and thus enables good performance in both urban and forest area in a single flight. This system has the potential applications in accurate digital surface model acquisition, 3D target recognition, and biomass estimation.

Modeling forest canopy surface retrievals using very high-resolution spaceborne stereogrammetry: (II) optimizing acquisition configurations

The optimal configurations for very high-resolution (VHR, <2 m) spaceborne imagery collection to support stereogrammetry over complex forested terrain remain uncertain. We conducted a comprehensive sensitivity study of digital surface models (DSMs) derived from thousands of simulated along-track VHR stereopairs over two lidar-reconstructed forested scenes of closed and open canopies using the discrete anisotropic radiative transfer (DART) model. We evaluated the influence of convergence angle (CA), solar illumination, and image resolution on the derived DSM accuracy relative to the reference DSM and digital terrain model (DTM) products from airborne lidar data. Our results confirmed that the CA is the most critical acquisition parameter for DSM accuracy. Compared to the frequently used CA of ∼35∘ for along-track stereopair acquisitions by WorldView satellites, a smaller CA can provide better accuracy for forest canopy shape estimation by reducing occlusions and mitigating radiometric variance caused by the bidirectional reflectance characteristics of vegetation. For forested scenes over relatively flat terrain, oblique solar zenith angles (50−70∘) yielded more consistent DSMs with better accuracy, whereas images with a hotspot configuration generated elevations that were closer to the DTM. Image pairs with smaller ground sample distance (GSD) improved the DSM accuracy, and combinations of small (nadir) and large (off-nadir) GSDs had accuracy between those derived from homogeneous GSDs. These simulation results suggest that available global archives of DSMs from VHR stereo imagery collected under a range of acquisition configurations will yield inconsistent estimates of canopy surfaces. This study also provides a benchmark dataset and configuration guide for 1) selecting existing data to retrieve the forest canopy surface shape, and 2) defining requirements for future satellite missions to characterize the forest canopy surface using stereogrammetry.

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.

Validation of FABDEM, a global bare-earth elevation model, against UAV-lidar derived elevation in a complex forested mountain catchment

Space-based, global-extent digital elevation models (DEMs) are key inputs to many Earth sciences applications. However, many of these applications require the use of a ‘bare-Earth’ DEM versus a digital surface model (DSM), the latter of which may include systematic positive biases due to tree canopies in forested areas. Critical topographic features may be obscured by these biases. Vegetation-free datasets have been created by using statistical relationships and machine learning to train on local-scale datasets (e.g., lidar) to de-bias the global-extent datasets. Recent advances in satellite platforms coupled with increased availability of computational resources and lidar reference products has allowed for a new generation of vegetation- and urban-canopy removals. One of these is the Forest And Buildings removed Copernicus DEM (FABDEM), based on the most recent and most accurate global DSM Copernicus-30. Among the more challenging landscapes to quantify surface elevations are densely forested mountain catchments, where even airborne lidar applications struggle to capture surface returns. The increasing affordability and availability of UAV-based lidar platforms have resulted in new capacity to fly modest spatial extents with unrivalled point densities. These data allow an unprecedented ability to validate global sub-canopy DEMs against representative UAV-based lidar data. In this work, the FABDEM is validated against up-scaled lidar data in a steep and forested mountain catchment considering elevation, slope, and Terrain Position Index (TPI) metrics. Comparisons of FABDEM with SRTM, MERIT, and the Copernicus-30 dataset are made. It was found that the FABDEM had a 24% reduction in elevation RMSE and a 135% reduction in bias compared to the Copernicus-30 dataset. Overall, the FABDEM provides a clear improvement over existing deforested DEM products in complex mountain topography such as the MERIT DEM. This study supports the use of FABDEM in forested mountain catchments as the current best-in-class data product.

Solar Potential Uncertainty in Building Rooftops as a Function of Digital Surface Model Accuracy

Solar cadasters are excellent tools for determining the most suitable rooftops and areas for PV deployment in urban environments. There are several open models that are available to compute the solar potential in cities. The Solar Energy on Building Envelopes (SEBE) is a powerful model incorporated in a geographic information system (QGIS). The main input for these tools is the digital surface model (DSM). The accuracy of the DSM can contribute significantly to the uncertainty of the solar potential, since it is the basis of the shading and sky view factor computation. This work explores the impact of two different methodologies for creating a DSM to the solar potential. Solar potential is estimated for a small area in a university campus in Madrid using photogrammetry from google imagery and LiDAR data to compute different DSM. Large differences could be observed in the building edges and in the areas with a more complex and diverse topology that resulted in significant differences in the solar potential. The RSMD at a measuring point in the building rooftop can range from 10% to 50% in the evaluation of results. However, the flat and clear areas are much less affected by these differences. A combination of both techniques is suggested as future work to create an accurate DSM.

A Hierarchical Deformable Deep Neural Network and an Aerial Image Benchmark Dataset for Surface Multiview Stereo Reconstruction

Multiview stereo (MVS) aerial image depth estimation is a research frontier in the remote sensing field. Recent deep learning-based advances in close-range object reconstruction have suggested the great potential of this approach. Meanwhile, the deformation problem and the scale variation issue are also worthy of attention. These characteristics of aerial images limit the applicability of the current methods for aerial image depth estimation. Moreover, there are few available benchmark datasets for aerial image depth estimation. In this regard, this article describes a new benchmark dataset called the LuoJia-MVS dataset ( <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://irsip.whu.edu.cn/resources/resources_en_v2.php</uri> ), as well as a new deep neural network known as the hierarchical deformable cascade MVS network (HDC-MVSNet). The LuoJia-MVS dataset contains 7972 five-view images with a spatial resolution of 10 cm, pixel-wise depths, and precise camera parameters, and was generated from an accurate digital surface model (DSM) built from thousands of stereo aerial images. In the HDC-MVSNet network, a new full-scale feature pyramid extraction module, a hierarchical set of 3-D convolutional blocks, and “true 3-D” deformable 3-D convolutional layers are specifically designed by considering the aforementioned characteristics of aerial images. Overall and ablation experiments on the WHU and LuoJia-MVS datasets validated the superiority of HDC-MVSNet over the current state-of-the-art MVS depth estimation methods and confirmed that the newly built dataset can provide an effective benchmark.

A Two-Step Block Adjustment Method for DSM Accuracy Improvement with Elevation Control of ICESat-2 Data

Digital surface models (DSMs) have been widely utilized in various applications as fundamental geographic information data. Block adjustment is normally performed on satellite images to enhance the geometric accuracy and DSMs are then generated by stereo mapping. However, new errors may be introduced during the stereo mapping processing and geometric discrepancies between DSMs may still exist. In particular, block adjustment is difficult for multisource satellite images. Therefore, this paper presents a two-step block adjustment approach directly performed on DSMs, with high-accuracy ICESat-2 laser altimetry data used as elevation control. In the method, DSM tie-point matching, elevation control/check point selection from ICESat-2 laser points, and planar and elevation block adjustments are performed in sequence. In the experiments, ZY-3 satellite stereo images and corresponding generated DSMs, as well as SRTM and ALOS DSMs, are used for verification. The experimental results show that the absolute elevation accuracy and the relative geometric consistency of the DSMs are both significantly improved after two-step DSM block adjustment and it can efficiently improve the accuracy, not only for DSMs acquired by the same sensor type, but also for DSMs acquired by different sensor types, which demonstrates the feasibility and advantage of the proposed method.

An Illumination-Invariant Shadow-Based Scene Matching Navigation Approach in Low-Altitude Flight

Differences in acquisition time, light conditions, and viewing angle create significant differences among the airborne remote sensing images from Unmanned Aerial Vehicles (UAVs). Real-time scene matching navigation applications based on fixed reference maps are error-prone and have poor robustness. This paper presents a novel shadow-based matching method for the localization of low-altitude flight UAVs. A reference shadow map is generated from an accurate (0.5 m spatial resolution) Digital Surface Model (DSM) with the known date and time information; a robust shadow detection algorithm is employed to detect shadows in aerial images; the shadows can then be used as a stable feature for scene matching navigation. Combining the conventional intensity-based matching method, a fusion scene navigation scheme that is more robust to illumination variations is proposed. Experiments were performed with Google satellite maps, DSM data, and real aerial images of the Zurich region. The radial localization error of the Shadow-based Matching (SbM) is less than 7.3 m at flight height below 1200 m. The fusion navigation approach also achieves an optimal combination of shadow-based matching and intensity-based matching. This study shows the solution to the inconsistencies caused by changes in light, viewing angle, and acquisition time for accurate and effective scene matching navigation.

COMBINING UNMANNED AERIAL SYSTEMS AND STRUCTURE FROM MOTION PHOTOGRAMMETRY TO RECONSTRUCT THE GEOMETRY OF GROINS

Abstract. In the context of coastal environment, the combined use of Unmanned Aerial Systems (UAS) with Structure-from-Motion (SfM) photogrammetry has been demonstrated to be a suitable tool to collect accurate geoinformation. This work aims at investigating the effectiveness of UAS survey to accurately map the geometry of rouble mound groins for monitoring the structural integrity. A set of nadiral images was processed through SfM photogrammetry to obtain the Digital Surface Model (DSM) of the groin. The DSM accuracy was evaluated through the comparison of five profiles measured by the traditional GNSS technique. Overall, vertical accuracy returned a root mean square error of 2.6 cm. The results suggest that UAS combined with SfM photogrammetry can improve the current monitoring of coastal engineering structures by providing high accurate geospatial products.

ASSESSMENT OF STEREO-EXTRACTED DSM FROM WORLDVIEW-3 OVER DIFFERENT LAND COVERS DEPENDING ON THE IMAGING GEOMETRY

Abstract. WorldView-3's 0.31m resolution in panchromatic mode, makes it one of the highest resolution commercial satellite in the world. This fact, together with its excellent stereo capabilities, make this satellite ideal for digital surface model (DSM) extraction working on very complex morphologies where a higher level of detail is required. In this communication we assess the quality (both completeness and vertical accuracy) of DSM extracted from WorldView-3 stereo pairs depending on the image geometry and sun position. Three different land covers (bare soil, urban areas and agricultural plastic greenhouses) have been tested in the Southeast of Spain (Almería). The well-known semiglobal matching (SGM) algorithm was used for all the extracted DSM. A clear relationship between DSM completeness and the WorldView-3 stereo pair imaging geometry measured as convergence angle was found. The completeness values decreased as convergence angles increased, especially in complex reliefs. In fact, convergence angles lower than 16 degrees is recommended when working on urban or greenhouse land covers. Moreover, sun light can cause glint effect in greenhouse areas. In this land cover, the attained results suggest to use stereo pairs taken when the sun presented a very low elevation. In Almería, the last happens in winter. The best results in all the tested land covers can be obtained by fusing DSM extracted from more than one stereo pair.

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.

ESTIMATING TREE HEIGHT BASED ON TREE CROWN FROM UAV IMAGERY

Tree crown delineation is a significant measurement for forest inventory and management purposes. The increasing availability of high-resolution Unmanned Aerial Vehicle (UAV) data makes it possible to delineate the tree crown of a single tree. Currently, with the advancement in technology UAV has become one of the emerging technologies that offers an affordable, cheaper, and faster technology in acquiring data for numerous applications. More importantly, this technology is embedded with efficient approaches for real-time acquisition of high resolution to produce three-dimensional (3D) information. UAV images provide an accurate digital surface model for planting application. In this study, the UAV technology is explored by applying several algorithms approach to determine tree crown and tree height of a single tree canopy. As a result, this study has obtained the tree crown from the Digital Surface Model (DSM) that is filtered locally based on pixel size using the selected algorithms. Subsequently, the tree height has been calculated based on the tree crown extraction. As a conclusion, this study has contributed to the knowledge extension in this area and presented the result of the tree height values according to the segmentation algorithm at the different flying height data.

High-Precision Digital Surface Model Extraction from Satellite Stereo Images Fused with ICESat-2 Data

The lack of ground control points (GCPs) affects the elevation accuracy of digital surface models (DSMs) generated by optical satellite stereo images and limits the application of high-resolution DSMs. It is a feasible idea to use ICESat-2 (Ice, Cloud, and land Elevation Satellite-2) laser altimetry data to improve the elevation accuracy of optical stereo images, but it is necessary to accurately match the two types of data. This paper proposes a DSM registration strategy based on terrain similarity (BOTS), which integrates ICESat-2 laser altimetry data without GCPs and improves the DSM elevation accuracy generation from optical satellite stereo pairs. Under different terrain conditions, Worldview-2, SV-1, GF-7, and ZY-3 stereo pairs were used to verify the effectiveness of this method. The experimental results show that the BOTS method proposed in this paper is more robust when there are a large number of abnormal points in the ICESat-2 data or there is a large elevation gap between DSMs. After fusion of ICESat-2 data, the DSM elevation accuracy extracted from the satellite stereo pair is improved by 73~92%, and the root mean square error (RMSE) of Worldview-2 DSM reaches 0.71 m.

Accuracy Comparison and Assessment of DSM Derived from GFDM Satellite and GF-7 Satellite Imagery

Digital Surface Model (DSM) derived from high resolution satellite imagery is important for various applications. GFDM is China’s first civil optical remote sensing satellite with multiple agile imaging modes and sub-meter resolution. Its panchromatic resolution is 0.5 m and 1.68 m for multi-spectral images. Compared with the onboard stereo viewing instruments (0.8 m for forward image, 0.65 m for back image, and 2.6 m for back multi-spectrum images) of GF-7, a mapping satellite of China in the same period, their accuracy is very similar. However, the accuracy of GFDM DSM has not yet been verified or fully characterized, and the detailed difference between the two has not yet been assessed either. This paper evaluates the DSM accuracy generated by GFDM and GF-7 satellite imagery using high-precision reference DSM and the observations of Ground Control Points (GCPs) as the reference data. A method to evaluate the DSM accuracy based on regional DSM errors and GCPs errors is proposed. Through the analysis of DSM subtraction, profile lines, strips detection and residuals coupling differences, the differences of DSM overall accuracy, vertical accuracy, horizontal accuracy and the strips errors between GFDM DSM and GF-7 DSM are evaluated. The results show that the overall accuracy of both is close while the vertical accuracy is slightly different. When regional DSM is used as the benchmark, the GFDM DSM has a slight advantage in elevation accuracy, but there are some regular fluctuation strips with small amplitude. When GCPs are used as the reference, the elevation Root Mean Square Error (RMSE) of GFDM DSM is about 0.94 m, and that of GF-7 is 0.67 m. GF-7 DSM is more accurate, but both of the errors are within 1 m. The DSM image residuals of the GF-7 are within 0.5 pixel, while the residuals of GFDM are relatively large, reaching 0.8 pixel.

COMPARING 3D DIGITAL TECHNOLOGIES FOR ARCHAEOLOGICAL FIELDWORK DOCUMENTATION. THE CASE OF THESSALONIKI TOUMBA EXCAVATION, GREECE

Abstract. Nowadays, there are many methods and techniques for the documentation and the restoration of historic structures and historical artifacts that are commonly used due to their completeness, accuracy and fastness. The use of advanced 3D measurement technologies, by either using terrestrial or aerial means of acquiring digital data, has become an efficient and reliable documentation tool. Within this context, this study focuses on combining terrestrial laser scanning, unmanned aerial vehicle photogrammetry, close-range photogrammetry and topographic surveying, and comparing the associated digital data for archaeological fieldwork documentation. The data collected during the Thessaloniki Toumba Excavation (Greece) provided accurate digital surface models and photo-realistic three-dimensional outputs of archaeological trenches. The data elaboration enabled new inferences and knowledge to be gained through the implementation of advanced technologies in heritage documentation.

Evaluating Short-Term Tidal Flat Evolution Through UAV Surveys: A Case Study in the Po Delta (Italy)

The use of Unmanned Aerial Vehicles (UAV) on wetlands is becoming a common survey technique that is extremely useful for understanding tidal flats and salt marshes. However, its implementation is not straightforward because of the complexity of the environment and fieldwork conditions. This paper presents the morphological evolution of the Po della Pila tidal flat in the municipality of Porto Tolle (Italy) and discusses the reliability of UAV-derived Digital Surface Models (DSMs) for such environments. Four UAV surveys were performed between October 2018 and February 2020 on an 8 ha young tidal flat that was generated, amongst others, as a consequence of the massive sediment injection into the Po Delta system due to the floods of the 1950s and 1960s. The DSM accuracy was tested by processing (i.e., photogrammetry) diverse sets of pictures taken at different altitudes during the same survey day. The DSMs and the orthophotos show that the tidal flat is characterised by several crevasse splays and that the sediment provision depends strictly on the river. During the study period, the sediment budget was positive (gaining 800 m3/year and an average rate of vertical changes of 1.3 cm/year). Comparisons of DSMs demonstrated that neither lower flight altitudes (i.e., 20–100 m) nor the combination of more photos from different flights during the same surveys necessarily reduce the error in such environments. However, centimetric errors (i.e., RMSEs) are achievable flying at 80–100 m, as the increase of GCP (Ground Control Point) density is the most effective solution for enhancing the resolution. Guidelines are suggested for implementing high-quality UAV surveys in wetlands.

Accuracy of DSM By Using Unmanned Aerial Vehicles on the Downstream of Welang Riverbank, District of Pasuruan, Jawa Timur

The Digital Surface Model (DSM) is commonly used in studies on flood map modeling. The lack of accurate, high-resolution topography data has hindered flood modeling. The use of the Unmanned Aerial Vehicle (UAV) can help data acquisition with sufficient accuracy. This research aims to provide high-resolution DSM-generated maps by Ground Control Points (GCPs) settings. Improvement of the model's accuracy was pursued by distributing 20 GCPs along the edges of the study area. Agrisoft software was used to generate the DSM. The generated DSM can be used for various planning purposes. The model's accuracy is measured in Root Mean Square Error (RMSE) based on the generated DSM. The RMSE values are 0.488 m for x-coordinates and y-coordinates (horizontal direction) and 0.161 m for z-coordinates (vertical direction).

Photogrammetric Digital Surface Model Reconstruction in Extreme Low-Light Environments

Digital surface models (DSM) have become one of the main sources of geometrical information for a broad range of applications. Image-based systems typically rely on passive sensors which can represent a strong limitation in several survey activities (e.g., night-time monitoring, underground survey and night surveillance). However, recent progresses in sensor technology allow very high sensitivity which drastically improves low-light image quality by applying innovative noise reduction techniques. This work focuses on the performances of night-time photogrammetric systems devoted to the monitoring of rock slopes. The study investigates the application of different camera settings and their reliability to produce accurate DSM. A total of 672 stereo-pairs acquired with high-sensitivity cameras (Nikon D800 and D810) at three different testing sites were considered. The dataset includes different camera configurations (ISO speed, shutter speed, aperture and image under-/over-exposure). The use of image quality assessment (IQA) methods to evaluate the quality of the images prior to the 3D reconstruction is investigated. The results show that modern high-sensitivity cameras allow the reconstruction of accurate DSM in an extreme low-light environment and, exploiting the correct camera setup, achieving comparable results to daylight acquisitions. This makes imaging sensors extremely versatile for monitoring applications at generally low costs.

Urban Feature Extraction from Merged Airborne LiDAR Data and Digital Camera Data

Until recently, the most highly accurate digital surface models were obtained from airborne lidar. With the development of a new generation of large format digital photogrammetric aerial camera, a fully digital photogrammetric workflow became possible. Digital airborne images are sources for elevation extraction and orthophoto generation. This research concerned with the generation of digital surface models and orthophotos as applications from high-resolution images. In this research, the following steps were performed. A Benchmark data of LIDAR and digital aerial camera have been used. Firstly, image orientation, AT have been performed. Then the automatic digital surface model DSM generation has been produced from the digital aerial camera. Thirdly true digital ortho has been generated from the digital aerial camera also orthoimage will be generated using LIDAR digital elevation model (DSM). Leica Photogrammetric Suite (LPS) module of Erdsa Imagine 2014 software was utilized for processing. Then the resulted orthoimages from both techniques were mosaicked. The results show that automatic digital surface model DSM that been produced from digital aerial camera method has very high dense photogrammetric 3D point clouds compared to the LIDAR 3D point clouds. It was found that the true orthoimage produced from the second approach is better than the true orthoimage produced from the first approach. The five approaches were tested for classification of the best-orthorectified image mosaic using subpixel based (neural network) and pixel-based ( minimum distance and maximum likelihood).Multicues were extracted such as texture(entropy-mean),Digital elevation model, Digital surface model ,normalized digital surface model (nDSM) and intensity image. The contributions of the individual cues used in the classification have been evaluated. It was found that the best cue integration is intensity (pan) +nDSM+ entropy followed by intensity (pan) +nDSM+mean then intensity image +mean+ entropy after that DSM )image and two texture measures (mean and entropy) followed by the colour image. The integration with height data increases the accuracy. Also, it was found that the integration with entropy texture increases the accuracy. Resulted in fifteen cases of classification it was found that maximum likelihood classifier is the best followed by minimum distance then neural network classifier. We attribute this to the fine resolution of the digital camera image. Subpixel classifier (neural network) is not suitable for classifying aerial digital camera images.