Digital Surface Model Extraction Research Articles

DSM Extraction Based on Gaofen-6 Satellite High-Resolution Cross-Track Images with Wide Field of View.

Digital Surface Model (DSM) is a three-dimensional model presenting the elevation of the Earth's surface, which can be obtained by the along-track or cross-track stereo images of optical satellites. This paper investigates the DSM extraction method using Gaofen-6 (GF-6) high-resolution (HR) cross-track images with a wide field of view (WFV). To guarantee the elevation accuracy, the relationship between the intersection angle and the overlap of the cross-track images was analyzed. Cross-track images with 20-40% overlaps could be selected to conduct DSM extraction. First, the rational function model (RFM) based on error compensation was used to realize the accurate orientation of the image. Then, the disparity map was generated based on the semi-global block matching (SGBM) algorithm with epipolar constraint. Finally, the DSM was generated by forward intersection. The GF-6 HR cross-track images with about 30% overlap located in Taian, Shandong Province, China, were used for DSM extraction. The results show that the mountainous surface elevation features were retained completely, and the details, such as houses and roads, were presented in valleys and urban areas. The root mean square error (RMSE) of the extracted DSM could reach 6.303 m, 12.879 m, 14.929 m, and 19.043 m in valley, ridge, urban, and peak areas, respectively. The results indicate that the GF-6 HR cross-track images with a certain overlap can be used to extract a DSM to enhance its application in land cover monitoring.

A novel solution for stereo three-dimensional localization combined with geometric semantic constraints based on spaceborne SAR data

Improved resolution and geolocation accuracy of spaceborne SAR images make refined digital surface model extraction and delicate three-dimensional reconstruction promising. Currently, extracting a refined digital surface model by stereo SAR remains challenging due to the significant matching errors. In this paper, we quantitatively analyze the effect of matching errors on stereo SAR based on a simulation method. Subsequently, a novel stereo SAR approach combined with geometric semantic constraints is proposed to reduce the effect of matching errors on localization. In this approach, a stereo localization model combined with geometric semantic constraints is utilized to jointly locate a group of points with planar or linear structures. The least squares solution of the system of equations will be solved by an iterative method based on correcting characteristic values. Finally, the simulation experiments show that the planar and linear semantic constraints improve the relative positioning accuracy by about 26% and 70%, respectively. Experiments based on real data also demonstrate that the proposed method is significantly better than traditional stereo SAR.

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.

A Robust Stereo Positioning Solution for Multiview Spaceborne SAR Images Based on the Range–Doppler Model

In recent years, the stereo positioning technology based on multiview spaceborne synthetic aperture radar (SAR) images has been widely applied in digital surface model extraction. In this letter, problems of the existing methods based on the range–Doppler (RD) model in a multiview stereo solution are pointed out. A robust stereo positioning solution for multiview spaceborne SAR images based on the RD model is proposed. In the proposed method, the traditional RD model is normalized to reduce the model errors caused by the different scales of the range equation and Doppler equation. A weighting strategy is also proposed to improve the positioning accuracy. This strategy is useful for multiview stereo positioning when using images of different satellites with orbital data of different accuracies. The experiments based on GaoFen-3 and TerraSAR-X satellite data sets validate the effectiveness of the proposed method.

Extraction of Dense Urban Buildings From Photogrammetric and LiDAR Point Clouds

Point clouds derived from LiDAR (Light Detection and Ranging) and photogrammetry systems are used to extract building footprints in dense urban areas. Two extraction methods based on DSM (Digital Surface Model) images and point clouds are comprehensively evaluated and compared. Firstly, photogrammetric point clouds are generated from aerial images of downtown Guangzhou, China, and compared with corresponding LiDAR point clouds. Then, DSM images are created using these point clouds and a threshold segmentation method is applied for building extraction. Although regularized buildings can be extracted according to the selection of appropriate height thresholds for the LiDAR DSM and photogrammetric DSM, blurry building boundaries exist for results of photogrammetric DSM when high trees are available nearby. LiDAR DSM extraction performs better in terms of Precision, Recall, and $F$ -score metrics. A DoN (Difference of Normals) approach based on point cloud datasets is also quantitatively and qualitatively demonstrated. Our experiments show that when a suitable radius threshold is selected, the method provides satisfactorily normal calculation results and can successfully isolate building roofs from other objects in densely built-up areas. The majority of building extraction results have a precision >0.9 and favorable Recall and $F$ -score results. There is high consistency between photogrammetric and LiDAR point clouds. Although LiDAR provides higher extraction accuracy, photogrammetry is also useful for its more convenient acquisition and higher point cloud densities.

Filtering DSM extraction from Worldview-3 images to DTM using open source software

Today, the Indonesia one map policy has encouraged for producing maps up to scale 1/5.000 – 1/10.000. Therefore, all efforts to accelerate large-scale map production are still needed. One of the challenges is for producing DEM (Digital Elevation Model). DEM data extraction from high-resolution optical satellite imagery is an efficient choice for the production of large-area DEM. This study uses Worldview 3 stereo ready images for DSM (Digital Surface Model) Extraction. Although the DSM data have a high resolution 30cm x 30cm, unfortunately, the DEM extraction techniques from optical images produce DSM (Digital Surface Model) data instead of DTM. While topographic maps generally use DTM data instead of DSM. So, the goal of this study is to use an appropriate procedures method in Open-Source (OS) software towards the generation of Bare-Earth data by filtering DSM. The OS software is SAGA-GIS that it is can be used for any point cloud data. The quality of DTM generated by SAGA-GIS is evaluated by visual interpretation from Worldview 3 orthoimage itself. A good filtering result will remove the non-terrain point. Some independent check point used to evaluate the error in elevation. The error in elevation still less than 2 pixels. Final product is a high resolution DTM which has 30cm × 30cm grid spacing.

Digital surface model extraction with high details using single high resolution satellite image and SRTM global DEM based on deep learning

Digital surface model extraction with high details using single high resolution satellite image and SRTM global DEM based on deep learning

Normalized Digital Surface Model Extraction and Slope Parameter Determination through Region Growing of UAV Data

Normalized Digital Surface Model Extraction and Slope Parameter Determination through Region Growing of UAV Data

Designing Software Prototype for Digital Surface Model Extraction from Stereo Satellite Imagery Based on Rational Function Model

There are many commercial software that can be used to perform Digital Surface Model (DSM) extraction from stereo satellite imagery based on Rational Function Model (RFM), however the license price is quite expensive. Therefore, this research aims to build the new software prototype for DSM extraction from stereo satellite imagery based on RFM, by using free development tool and integrating various components or libraries that are non-commercial (free and/or open source). The method applied in the software prototype consists of 3 main steps, i.e. conjugate points collection manually and automatically using image matching with RFM based Cross Correlation method, Forward RFM based Virtual Intersection to perform 3D reconstruction of conjugate points, and interpolate data conjugate points with Ordinary Kriging method to establish DSM in the raster format. Stereo satellite imagery of WorldView-2 which is equipped with Rational Polynomial Coefficients (RPCs) from the vendor was applied to verify the DSM accuracy that obtained from the software prototype. Based on RFM formed from the RPCs can be obtained DSM with accuracy of 2.47639575534581 meters in planimetry (CE90) and 4.21487430387638 meters in height (LE90).

HIGH PERFORMANCE COMPUTING FOR DSM EXTRACTION FROM ZY-3 TRI-STEREO IMAGERY

Abstract. ZY-3 has been acquiring high quality imagery since its launch in 2012 and its tri-stereo (three-view or three-line-array) imagery has become one of the top choices for extracting DSM (Digital Surface Model) products in China over the past few years. The ZY-3 tri-stereo sensors offer users the ability to capture imagery over large regions including an entire territory of a country, such as China, resulting in a large volume of ZY-3 tri-stereo scenes which require timely (e.g., near real time) processing, something that is not currently possible using traditional photogrammetry workstations. This paper presents a high performance computing solution which can efficiently and automatically extract DSM products from ZY-3 tri-stereo imagery. The high performance computing solution leverages certain parallel computing technologies to accelerate computation within an individual scene and then deploys a distributed computing technology to increase the overall data throughput in a robust and efficient manner. By taking advantage of the inherent efficiencies within the high performance computing environment, the DSM extraction process can exploit all combinations offered from a set of tri-stereo images (forward-backword, forward-nadir and backword-nadir). The DSM results merged from all of the potential combinations can minimize blunders (e.g., incorrect matches) and also offer the ability to remove potential occlusions which may exist in a single stereo pair, resulting in improved accuracy and quality versus those that are not merged. Accelerated performance is inherent within each of the individual steps of the DSM extraction workflow, including the collection of ground control points and tie points, image bundle adjustment, the creation of epipolar images, and computing elevations. Preliminary experiments over a large area in China have proven that the high performance computing system can generate high quality and accurate DSM products in a rapid manner.

HIGH PERFORMANCE COMPUTING FOR DSM EXTRACTION FROM ZY-3 TRI-STEREO IMAGERY

ZY-3 has been acquiring high quality imagery since its launch in 2012 and its tri-stereo (three-view or three-line-array) imagery has become one of the top choices for extracting DSM (Digital Surface Model) products in China over the past few years. The ZY-3 tri-stereo sensors offer users the ability to capture imagery over large regions including an entire territory of a country, such as China, resulting in a large volume of ZY-3 tri-stereo scenes which require timely (e.g., near real time) processing, something that is not currently possible using traditional photogrammetry workstations. This paper presents a high performance computing solution which can efficiently and automatically extract DSM products from ZY-3 tri-stereo imagery. The high performance computing solution leverages certain parallel computing technologies to accelerate computation within an individual scene and then deploys a distributed computing technology to increase the overall data throughput in a robust and efficient manner. By taking advantage of the inherent efficiencies within the high performance computing environment, the DSM extraction process can exploit all combinations offered from a set of tri-stereo images (forward-backword, forward-nadir and backword-nadir). The DSM results merged from all of the potential combinations can minimize blunders (e.g., incorrect matches) and also offer the ability to remove potential occlusions which may exist in a single stereo pair, resulting in improved accuracy and quality versus those that are not merged. Accelerated performance is inherent within each of the individual steps of the DSM extraction workflow, including the collection of ground control points and tie points, image bundle adjustment, the creation of epipolar images, and computing elevations. Preliminary experiments over a large area in China have proven that the high performance computing system can generate high quality and accurate DSM products in a rapid manner.

Geometric Potential of Pléiades 1A Satellite Imagery

Abstract In this paper, the geometrical characteristics of Pléiades 1A satellite imagery (both single and stereo) are analysed. At first the process of digital surface model (DSM) extraction from a Pléiades 1A stereo pair is described and analysed. After that geometric an accuracy of imagery, orthorectified using the extracted DSM and using the SRTM (Shuttle radar topographic mission) was analysed. The Pléiades 1A stereo pair was acquired on October 22, 2012 from the same orbital pass over an urban zone (Kiev, Ukraine). The study area is heterogeneous: there are both built-up and flat areas. The iImage orientation, DSM extraction and orthorectified images generation were performed using the PCI Geomatica 2013 software. The results showed that a strong, positive correlation between reference-derived elevations and DSM-derived elevations can be observed, and the orthorectified image accuracy, generated using that DSM, approximately equal to 1 m can be achieved using a bias compensation sensor model. Different sensor models were used for orthorectification using the SRTM. In this case, the geometric accuracy is а function of a chosen sensor model and a number of ground control points (GCP).

Performance of dense digital surface models based on image matching in the estimation of plot-level forest variables

Recent research results have shown that the performance of digital surface model extraction using novel high-quality photogrammetric images and image matching is a highly competitive alternative to laser scanning. In this article, we proceed to compare the performance of these two methods in the estimation of plot-level forest variables. Dense point clouds extracted from aerial frame images were used to estimate the plot-level forest variables needed in a forest inventory covering 89 plots. We analyzed images with 60% and 80% forward overlaps and used test plots with off-nadir angles of between 0° and 20°. When compared to reference ground measurements, the airborne laser scanning (ALS) data proved to be the most accurate: it yielded root mean square error (RMSE) values of 6.55% for mean height, 11.42% for mean diameter, and 20.72% for volume. When we applied a forward overlap of 80%, the corresponding results from aerial images were 6.77% for mean height, 12.00% for mean diameter, and 22.62% for volume. A forward overlap of 60% resulted in slightly deteriorated RMSE values of 7.55% for mean height, 12.20% for mean diameter, and 22.77% for volume. According to our results, the use of higher forward overlap produced only slightly better results in the estimation of these forest variables. Additionally, we found that the estimation accuracy was not significantly impacted by the increase in the off-nadir angle. Our results confirmed that digital aerial photographs were about as accurate as ALS in forest resources estimation as long as a terrain model was available.

Airborne photogrammetry and lidar for DSM extraction and 3D change detection over an urban area – a comparative study

A digital surface model (DSM) extracted from stereoscopic aerial images, acquired in March 2000, is compared with a DSM derived from airborne light detection and ranging (lidar) data collected in July 2009. Three densely built-up study areas in the city centre of Ghent, Belgium, are selected, each covering approximately 0.4 km2. The surface models, generated from the two different 3D acquisition methods, are compared qualitatively and quantitatively as to what extent they are suitable in modelling an urban environment, in particular for the 3D reconstruction of buildings. Then the data sets, which are acquired at two different epochs t 1 and t 2, are investigated as to what extent 3D (building) changes can be detected and modelled over the time interval. A difference model, generated by pixel-wise subtracting of both DSMs, indicates changes in elevation. Filters are proposed to differentiate ‘real’ building changes from false alarms provoked by model noise, outliers, vegetation, etc. A final 3D building change model maps all destructed and newly constructed buildings within the time interval t 2 – t 1. Based on the change model, the surface and volume of the building changes can be quantified.

Digital Surface Model Extraction and Re nement through Image Segmentation - Application to the ISPRS Benchmark Stereo Dataset

Digital Surface Model Extraction and Re nement through Image Segmentation - Application to the ISPRS Benchmark Stereo Dataset

TESTFIELD TRENTO: GEOMETRIC EVALUATION OF VERY HIGH RESOLUTION SATELLITE IMAGERY

Abstract. Today the use of spaceborne Very High Spatial Resolution (VHSR) optical sensors for automatic 3D information extraction is increasing in the scientific and civil communities. The 3D Optical Metrology (3DOM) Unit of the Bruno Kessler Foundation (FBK) in Trento (Italy) has collected stereo VHSR satellite imagery, as well as aerial and terrestrial data over Trento, with the aim to create a complete data collection with state-of-the-art datasets for investigations on image analysis, automatic digital surface model (DSM) generation, 2D/3D feature extraction, city modelling and data fusion. The testfield region covers the city of Trento, characterised by very dense urban (historical centre), residential and industrial areas, and the surrounding hills and steep mountains (approximate height range 200-2100 m) with cultivations, forests and bare soil. This paper reports the analysis conducted in FBK on the VHSR spaceborne imagery of Trento testfield for 3D information extraction. The data include two stereo-pairs acquired by WorldView-2 in August 2010 and by GeoEye-1 in September 2011 in panchromatic and multispectral mode, together with their original Rational Polynomial Coefficients (RPC), and the position and description of well distributed ground points. For reference and validation, a DSM from airborne LiDAR acquisition is used. The paper gives details on the project and the dataset characteristics. The results achieved by 3DOM on DSM extraction from WorldView-2 and GeoEye-1 stereo-pairs are shown and commented.

DSM EXTRACTION AND EVALUATION FROM GEOEYE-1 STEREO IMAGERY

Abstract. The newest very high resolution (VHR) commercial satellites, such as GeoEye-1 or WorldView-2, open new possibilities for cartographic applications, orthoimages generation and extraction of Digital Surface Models (DSMs). These DSMs are generated by image matching strategies from VHR satellite stereopairs imagery, reconstructing the 3D surface corresponding to the first surface view of the earth containing both microrelief (buildings, trees and so on) and bare terrain. The main aim of this work is to carry out an accuracy assessment test on the DSMs extracted from a GeoEye-1 stereopair captured in August 2011. A LiDAR derived DSM taken at the same month that the satellite imagery was used as ground truth. The influence of factors such as number of Ground Control Points (GCPs), sensor models tested and the geoid employed to transform the ellipsoid to orthometric heights were going to be evaluated. In this way, different sets of GCPs ranging from 7 to 45, two sensor models and two geoids (EGM96 and EGM08, the last adapted for Spain vertical network by the Spanish's National Geographic Institute) were tested in this work. The photogrammetric software package used was OrthoEngine from PCI Geomatica v. 10.3.2. OrthoEngine implements both sensor models tested: (i) the physical model developed by Toutin (CCRS) and, (ii) the rational function model using rational polynomial coefficients supplied by the vendor and later refined by means of the zero order linear functions (RPC0). When high accurate and well-distributed GCPs were used, the planimetric and vertical accuracies of DSMs generated from the GeoEye-1 Geo stereopair were always better than 0.5 m. Using only 7 GCPs and RPC0, a vertical accuracy around 0.43 m measured as standard deviation was attained. The geoid used by OrthoEngine (EGM96) produced similar results that the EGM08 adapted for Spain vertical network.

Effect of Sun Elevation Angle on DSMs Derived from Cartosat-1 Data

Along-track stereoscopic satellite data are increasingly used for automatic extraction of digital surface models (DSM) due to the reduced radiometric variation between the images. Problems remain with the quality of such DSMs, especially in steep terrain. This paper explores the accuracy of DSMs extracted from Cartosat-1 data acquired under high and low sun elevation angle conditions in High Himalayan terrain. The metric accuracy of the DSM was estimated by comparing it with check points obtained with a differential GPS . Additionally, we used spatial discrepancy of drainage lines to estimate errors in the DSM due to spatial auto- correlation. For valleys perpendicular to the satellite track, the DSM extracted from a low sun elevation angle data showed 45 percent higher spatial accuracy than the DSM extracted from high sun elevation angle data. The results indicate that the sun elevation angle and valley orientation affect the spatial accuracy of the DSM, though metric accuracy remains comparable.

Extraction of digital surface models from CORONA satellite stereo images

Satellite images can be utilised for observing surficial changes, especially efficient in the monitoring of larger areas. The comparative analysis of high resolution images from earlier periods with recent data can provide insight in the scale of changes in topography, and with meteorological, hydrological and other historic records, can lead to better understanding and more reliable modelling of the predominant processes causing mass movement. More accurate morphometric and visual analysis of the topographic changes is possible using digital surface model (DSM), which can be obtained from satellite stereo images. In this paper, the authors evaluated methods of creation digital surface models obtained from satellite images from the CORONA program in monitoring surficial mass movement processes in the Fruška Gora mountain area, in the southern part of the Vojvodina province in Serbia. This area is of particular interest because of its favourable geographic location, rich geo- and cultural heritage and increasing demand for exploitation, which results in greater impact of natural hazards. The CORONA images were chosen because of good availability of high resolution coverage for the whole area from the period of past four decades.

Digital surface model generation from CORONA satellite images

Digital surface models (DSMs) are used for various analyses in environmental science, e.g. for erosion and water studies. Aerial photos and maps, which are necessary for the extraction of DSMs, often do not exist due to financial or political reasons. This situation can be also encountered in Morocco and, in particular, a test area of the international research project IMPETUS was used in this study. Therefore, stereo satellite images of CORONA have been used, as they allow DSM generation, have a ground resolution of 1.83 m, reasonable price (US$12–18 per filmstrip of 188×14 km) and large coverage (especially of Asia and eastern Europe). The software program ERDAS IMAGINE OrthoBASE Pro was used to generate DSMs automatically from CORONA satellite images with best vertical accuracy of about 10 m and planimetric accuracy of about 3 m. These DSMs could afterwards be used to generate orthoimages, e.g. for mapping change detection and generating thematic maps or land use classifications.