Automatic Digital Surface Models Generation Research Articles

Digital Surface Model Generation from Satellite Images Based on Double-Penalty Bundle Adjustment Optimization

Digital Surface Model (DSM) generation from high-resolution optical satellite images is an important topic of research in the remote sensing field. In optical satellite imaging systems, the attitude information of the cameras recorded by satellite sensors is often biased, which leads to errors in the Rational Polynomial Camera (RPC) model of satellite imaging. These errors in the RPC model can mislead the DSM generation. To solve the above problems, we propose an automatic DSM generation method from satellite images based on the Double-Penalty bundle adjustment (DPBA) optimization algorithm. In the proposed method, two penalty functions representing the camera’s attitude and the spatial 3D points, respectively, are added to the reprojection error model of the traditional bundle adjustment optimization algorithm. Instead of acting on images directly, the penalty functions are used to adjust the reprojection error model and improve the RPC parameters. We evaluate the performance of the proposed method using high-resolution satellite image pairs and multi-date satellite images. Through some experiments, we compare the accuracy and completeness of the DSM generated by the proposed method, the Satellite Stereo Pipeline (S2P) method, and the traditional bundle adjustment (BA) method. Compared to the S2P method, the experiment results of the satellite image pair indicate that the proposed method can significantly improve the accuracy and the completeness of the generated DSM by about 1–5 m and 20%–60% in most cases. Compared to the traditional BA method, the proposed method improves the accuracy and completeness of the generated DSM by about 0.01–0.05 m and 1%–3% in most cases. The experiment results can be a testament to the feasibility and effectiveness of the proposed method.

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.

Open source tool for DSMs generation from high resolution optical satellite imagery: development and testing of an OSSIM plug-in

ABSTRACTThe fully automatic generation of digital surface models (DSMs) is still an open research issue. From recent years, computer vision algorithms have been introduced in photogrammetry in order to exploit their capabilities and efficiency in three-dimensional modelling. In this article, a new tool for fully automatic DSMs generation from high resolution satellite optical imagery is presented. In particular, a new iterative approach in order to obtain the quasi-epipolar images from the original stereopairs has been defined and deployed. This approach is implemented in a new Free and Open Source Software (FOSS) named Digital Automatic Terrain Extractor (DATE) developed at the Geodesy and Geomatics Division, University of Rome ‘La Sapienza’, and conceived as an Open Source Software Image Map (OSSIM) plug-in. DATE key features include: the epipolarity achievement in the object space, thanks to the images ground projection (Ground quasi-Epipolar Imagery (GrEI)) and the coarse-to-fine pyramidal scheme adopted; the use of computer vision algorithms in order to improve the processing efficiency and make the DSMs generation process fully automatic; the free and open source aspect of the developed code. The implemented plug-in was validated through two optical datasets, GeoEye-1 and the newest Pléiades-high resolution (HR) imagery, on Trento (Northern Italy) test site. The DSMs, generated on the basis of the metadata rational polynomial coefficients only, without any ground control point, are compared to a reference lidar in areas with different land use/land cover and morphology. The results obtained thanks to the developed workflow are good in terms of statistical parameters (root mean square error around 5 m for GeoEye-1 and around 4 m for Pléiades-HR imagery) and comparable with the results obtained through different software by other authors on the same test site, whereas in terms of efficiency DATE outperforms most of the available commercial software. These first achievements indicate good potential for the developed plug-in, which in a very near future will be also upgraded for synthetic aperture radar and tri-stereo optical imagery processing.

UPGRADE OF FOSS DATE PLUG-IN: IMPLEMENTATION OF A NEW RADARGRAMMETRIC DSM GENERATION CAPABILITY

Abstract. Synthetic Aperture Radar (SAR) satellite systems may give important contribution in terms of Digital Surface Models (DSMs) generation considering their complete independence from logistic constraints on the ground and weather conditions. In recent years, the new availability of very high resolution SAR data (up to 20 cm Ground Sample Distance) gave a new impulse to radargrammetry and allowed new applications and developments. Besides, to date, among the software aimed to radargrammetric applications only few show as free and open source. It is in this context that it has been decided to widen DATE (Digital Automatic Terrain Extractor) plug-in capabilities and additionally include the possibility to use SAR imagery for DSM stereo reconstruction (i.e. radargrammetry), besides to the optical workflow already developed. DATE is a Free and Open Source Software (FOSS) developed at the Geodesy and Geomatics Division, University of Rome “La Sapienza”, and conceived as an OSSIM (Open Source Software Image Map) plug-in. It has been developed starting from May 2014 in the framework of 2014 Google Summer of Code, having as early purpose a fully automatic DSMs generation from high resolution optical satellite imagery acquired by the most common sensors. Here, the results achieved through this new capability applied to two stacks (one ascending and one descending) of three TerraSAR-X images each, acquired over Trento (Northern Italy) testfield, are presented. Global accuracies achieved are around 6 metres. These first results are promising and further analysis are expected for a more complete assessment of DATE application to SAR imagery.

UPGRADE OF FOSS DATE PLUG-IN: IMPLEMENTATION OF A NEW RADARGRAMMETRIC DSM GENERATION CAPABILITY

Synthetic Aperture Radar (SAR) satellite systems may give important contribution in terms of Digital Surface Models (DSMs) generation considering their complete independence from logistic constraints on the ground and weather conditions. In recent years, the new availability of very high resolution SAR data (up to 20 cm Ground Sample Distance) gave a new impulse to radargrammetry and allowed new applications and developments. Besides, to date, among the software aimed to radargrammetric applications only few show as free and open source. It is in this context that it has been decided to widen DATE (Digital Automatic Terrain Extractor) plug-in capabilities and additionally include the possibility to use SAR imagery for DSM stereo reconstruction (i.e. radargrammetry), besides to the optical workflow already developed. <br><br> DATE is a Free and Open Source Software (FOSS) developed at the Geodesy and Geomatics Division, University of Rome “La Sapienza”, and conceived as an OSSIM (Open Source Software Image Map) plug-in. It has been developed starting from May 2014 in the framework of 2014 Google Summer of Code, having as early purpose a fully automatic DSMs generation from high resolution optical satellite imagery acquired by the most common sensors. <br><br> Here, the results achieved through this new capability applied to two stacks (one ascending and one descending) of three TerraSAR-X images each, acquired over Trento (Northern Italy) testfield, are presented. Global accuracies achieved are around 6 metres. These first results are promising and further analysis are expected for a more complete assessment of DATE application to SAR imagery.

Automatic digital surface model generation from Pléiades stereo images

We propose a fully automated stereo pipeline for producing digital elevation models from Pléiades satellite images. The agility of the Pléiades satellites allows them to capture multiple views of the same target in a single pass, enabling new applications that exploit these quasi-simultaneous high-resolution images. Concretely the tri-stereo acquisition modality permits to reduce the occlusions and to cross-validate the observed points. This paper gives an overview of our pipeline, named s2p, and presents some digital elevation models and 3D point clouds built from Pléiades tri-stereo datasets. The data was provided by Airbus DS and the CNES through the RTU program. The particularity of the s2p algorithm is that it permits to use conventional stereo correlation tools, by performing a very precise image rectification of each stereo pair. Although the acquisition system does not fit the pinhole camera model, which is necessary to make the rectification possible, the errors due to the pinhole assumption were shown to be negligible for small enough image sizes. Thus, the whole image can be treated by cutting it into small tiles that are processed independently.

Comparison of Automatic DSM Generation Modules by Processing IKONOS Stereo Data of an Urban Area

This study deals with the evaluation of four different image-processing software modules for the generation of digital surface models from very high-resolution stereo satellite data. The analysis was done in an urban area due to the growing interest in 3-D information over built-up areas. Depending on the different geometric model approaches used by the different software packages, shifts between 3.06-3.27 m between the digital surface models (DSMs) and the reference DSM were measured. The vertical RMSE of the four tested software packages range between 2.96-14.01 m. However, the visual evaluation resulted in a different ranking and does not confirm the quantitative results entirely. The results show that, depending on the building type to be extracted, the choice of software package may vary. The challenges of automatic DSM extraction in urban areas and the performance of current software package modules to address them are discussed. Potential improvements for automatic DSM extraction in urban areas are identified.

Analysis of Cartosat‐1 images regarding image quality, 3D point measurement and DSM generation

AbstractThe Institute of Geodesy and Photogrammetry (IGP) at ETH Zurich is participating in the Cartosat‐1 evaluation programme, a common initiative of ISRO (India) and ISPRS. Within this programme various test sites with reference data have been established and Cartosat‐1 images have been acquired over these sites. Here, investigations at the Rome (Italy) and Maussane‐les‐Alpilles (France) test sites are reported. First, radiometric problems encountered with the images and pre‐processing for their improvement are reported. Examples are shown, together with pre‐processing methods that can be employed in order to improve image quality, aimed especially at automatic generation of a digital surface model (DSM) with fewer blunders and more matched points. Then, georeferencing is discussed and the 3D point measurement accuracy that can be achieved is introduced, as well as problems encountered with the rational polynomial coefficients (RPCs). The georeferencing results were produced using various options regarding image pre‐processing, the mathematical model used for georeferencing, the number and distribution of ground control points (GCPs) and the GCP image mensuration methods. The best results led to a planimetric and height accuracy (RMSE) of about 1·3 m. Use of an affine transformation after the RPCs, with about six well‐distributed GCPs transferred with matching to the second image, is suggested. Finally, the results of automatic DSM generation using the SAT‐PP program package developed at the IGP are presented. Various DSMs were generated with 10 m grid spacing. The results were checked visually and were also compared to the reference data provided. In the best case, the accuracy achieved is about 2·7 m without any manual editing, in spite of a 3‐year difference between the matching and reference DSMs. Although some aspects regarding image quality and RPC generation could be improved, Cartosat‐1 is a useful sensor for mapping and especially for the generation of DSMs. However, owing to its poor absolute geolocation accuracy, Cartosat‐1 cannot be used for the generation of a global DSM without GCPs.