Quality Assessment Of Digital Surface Models Generated From IKONOS Imagery (original) (raw)
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ISPRS International Journal of Geo-Information, 2017
Very high spatial resolution (VHSR) stereo-imagery-derived digital surface models (DSM) can be used to generate digital elevation models (DEM). Filtering algorithms and triangular irregular network (TIN) densification are the most common approaches. Most filter-based techniques focus on image-smoothing. We propose a new approach which makes use of integrated object-based image analysis (OBIA) techniques. An initial land cover classification is followed by stratified land cover ground point sample detection, using object-specific features to enhance the sampling quality. The detected ground point samples serve as the basis for the interpolation of the DEM. A regional uncertainty index (RUI) is calculated to express the quality of the generated DEM in regard to the DSM, based on the number of samples per land cover object. The results of our approach are compared to a high resolution Light Detection and Ranging (LiDAR)-DEM, and a high level of agreement is observed-especially for non-vegetated and scarcely-vegetated areas. Results show that the accuracy of the DEM is highly dependent on the quality of the initial DSM and-in accordance with the RUI-differs between the different land cover classes.
GIScience & Remote Sensing, 2010
The launch of the Very High Resolution (VHR) sensor satellites has paved the way for further exploitation of the capabilities of satellite stereo imaging for many applications. The objective of this paper is to evaluate the level of accuracy that can be achieved by using stereo satellite images for different applications involving significantly different types of terrain. Three mathematical models for satellite sensor modeling are used: Rational Function Model (RFM), 3D polynomial model, and 3D affine model. Three stereo pairs of image datasets are tested from different satellites for different areas: (a) Indian Remote Sensing (IRS)-1D stereo images for topographic mapping and digital terrain elevation modeling for an area in Egypt; (b) IKONOS stereo images for highway alignments extraction in Toronto, Canada; and (c) IKONOS stereo images for topographic mapping and geometric parameter extraction for highway alignments in Hong Kong, China. The accuracy was evaluated by comparing the results of the data extracted using stereo satellite images and those extracted from conventional techniques, including Global Positioning System, field measurements, and aerial photogrammetry. The accuracy of the extracted features was found to be within a pixel-level. The results of this paper should be of interest to professionals from different disciplines exploring the use and accuracy of satellite stereo images for topographic and transportation applications.
3D Geometric Modelling of IKONOS Geo Images
2001
Digital elevation model (DEM) extracted from IKONOS along-track stereo images with photogrammetric method is evaluated. As few as 12 GCPs are enough for the stereo photogrammetric bundle adjustment, which also filters the errors of the input data. With an area-based image matching users may produce high resolution DEMs with LE68 errors of 1 m to 4 m depending on the land covers. The best results (1.1 m-2.6 m) are obtained in bare soils, lakes, residential areas and sparse forests. The surface elevation of some of the areas (residential/ forests) did not affect too much the errors because the 1-2-storey houses in residential areas are sparse or because the images were acquired when there is no leave in the deciduous forests. An error evaluation as a function of the slope azimuths shows that the DEM error in sun-facing slopes is 1-m smaller than the DEM error in slopes away from the sun. 5-10 m contour lines could thus be derived with the highest topographic standard.
Quality assessment of high density digital surface model over different land cover classes
Periodicum Biologorum, 2016
Background and Purpose: Recent research on generation of digital surface models (DSMs) using image matching methods revealed a great potential of DSM application in forestry, especially in forest inventory. However, research dealing with DSM generation from digital aerial images are still lacking in Croatia. Therefore, the main objective of this study was to present the workflow for generating high density DSM from colour infrared (CIR) digital stereo aerial images using area-based image matching algorithm. Materials and Methods: The high density DSM was generated from colour infrared digital aerial stereo images using Dense DTM algorithm of PHOTOMOD software-an area-based image matching algorithm which operates on the principle of cross-correlation approach. To evaluate the quality of the generated DSM, an agreement assessment with manual stereo measurements was conducted over three different land cover classes (forests, shrubs, grasslands) using the same images as for DSM generation. Results: The good vertical agreement between the generated DSM and stereo measurement was achieved for all three land cover classes present at the research area. The highest vertical agreement was obtained for the grassland land cover class (RMSE=0.36), slightly lower for forest (RMSE=0.62), whereas the lowest vertical agreement was obtained for shrub land cover class (RMSE=0.83). Conclusions: The results of this research are very promising and suggest that the high density DSM generated from digital aerial stereo images and by using the proposed methodology has the potential to be used in forestry, primarily in forest inventory. Therefore, further research should be focused on generation of CHM by subtracting available DTM from the high density DSM and on the examination of its potential for deriving various forest attributes.
2011
To measure the accuracy of Digital Surface Models (DSMs) generated by high resolution satellite images (HRSI) using semi-global matching algorithm in comparison with LIDAR DSMs, two different test areas with different properties and corresponding attributes and magnitudes of errors are considered. Error characteristics are classified as systematic and gross errors and significance of them to measure the accuracy of DSMs are evaluated. In this manner and to avoid the influence of outliers in accuracy assessment robust statistical methods are proposed. According to final values obtained for two test areas it can be concluded that the performance of DSMs generated by stereo matching for mountainous wooden areas in respect to the accuracy of LIDAR DSM are poor. In contrast, in case of residential urban areas the quality of the DSM generated by HRSI is able to follow the accuracy of LIDAR data.
Sensors, 2012
Digital surface models (DSMs) are widely used in forest science to model the forest canopy. Stereo pairs of very high resolution satellite and digital aerial images are relatively new and their absolute accuracy for DSM generation is largely unknown. For an assessment of these input data two DSMs based on a WorldView-2 stereo pair and a ADS80 DSM were generated with photogrammetric instruments. Rational polynomial coefficients (RPCs) are defining the orientation of the WorldView-2 satellite images, which can be enhanced with ground control points (GCPs). Thus two WorldView-2 DSMs were distinguished: a WorldView-2 RPCs-only DSM and a WorldView-2 GCP-enhanced RPCs DSM. The accuracy of the three DSMs was estimated with GPS measurements, manual stereo-measurements, and airborne laser scanning data (ALS). With GCP-enhanced RPCs the WorldView-2 image orientation could be optimised to a root mean square error (RMSE) of 0.56 m in planimetry and 0.32 m in height. This improvement in orientation allowed for a vertical median error of −0.24 m for the WorldView-2 GCP-enhanced RPCs DSM in flat terrain. Overall, the DSM based on ADS80 images showed the highest accuracy of the three models with a median error of 0.08 m over bare ground. As the accuracy of a DSM varies with land cover three classes were distinguished: herb and grass, forests, and artificial areas. The study suggested the ADS80 DSM to best model actual surface height in all three land cover classes, with median errors <1.1 m. The WorldView-2 GCP-enhanced RPCs model achieved good accuracy, too, with median errors of −0.43 m for the herb and grass vegetation and −0.26 m for artificial areas. Forested areas emerged
SPOT stereo matching for Digital Terrain Model generation
1993
This paper presents a matching algorithm for automatic Digital Terrain Model (DTM) generation from SPOT satellite images that provides dense, accurate and reliable results and attacks the problem of radiometric differences between the images. The proposed algorithm is based on a modified version of the Multiphoto Geometrically Constrained Matching (MPGC). It is the first algorithm that explicitly uses the SPOT geometry in matching, restricting thus the search space in one dimension, and simultaneously providing pixel and object coordinates. This leads to an increase in reliability, and to reduction and easier detection of blunders. The sensor modelling is based on Kratky's polynomial mapping functions to transform between the image spaces of stereopairs. With their help epipolar lines that are practically straight can be determined and the search is constrained along these lines. The polynomial functions can also provide approximate values, which are further refined by the use of an image pyramid. Radiometric differences are strongly reduced by performing matching not in the grey level but in gradient magnitude images. Thus, practically only the information in stripes along the edges is used for matching. Edges that exist in only one image can be detected by subtracting quasi registered images in the upper levels of an image pyramid. The points to be matched are selected by an interest operator. Gross errors can be detected by statistical analysis of criteria that are provided by the algorithm and by a robust analysis of the heights within local neighbourhoods. The results of an extensive test using a stereo SPOT model over Switzerland will be reported. Matching with different options and the qualitative comparison of the results based on thirty thousand check points will be presented.
ASSESSMENT OF DEMS PRODUCED BY MEDIUM RESOLUTION OPTICAL SENSORS CONSIDERING LAND COVER CLASSES
A digital elevation model (DEM) presents immense data proving three dimensional terrain structure of any part of the Earth. DEMs are obtained by two main methods in space-borne remote sensing as stereoscopy using optical or radar imagery and interferometric synthetic aperture radar (InSAR) technology. In fact, the primary product of space-borne remote sensing techniques is a digital surface model (DSM) that contains points located on the top of ground objects. By removing these points that do not belong to the bare ground, the DEM is obtained. In optical imagery, DSMs are generated based on stereo matching using ground control points and co-located clear tie points at stereo image-pair with high correlation. In this case, correlation comes into prominence and affects the success of DSM acquired by stereoscopy. This investigation aims to assess the quality of DEMs produced by medium resolution spatial data derived from optical imagery depending upon the effect of correlation in stereoscopy correspondingly the land cover types. Towards this purpose, land cover classes have been generated such as open, forest, built-up, road network and rocky regions, DSM-DEM conversion was applied by optimal filtering methods and DEM accuracies have been achieved separately. The analyses were realized using actual ASTER GDEM Version 2 with 30m original grid spacing in Zonguldak, Turkey including rugged topography and suitable land cover classes. For the verification, a reference DEM derived from 1/1000 scaled aerial photos was employed.
Proceedings of the 6th International Conference on Geographical Information Systems Theory, Applications and Management, 2020
Worldview-3 stereo-extracted DSMs represent state-of-the-art products in the domain of satellite-based digital surface modelling. Main goal of our research was to evaluate the vertical accuracy of WV-3 derived DSMs over olive groves. Creation of high-accuracy WV-3 derived DSMs would allow efficient large scale management and protection of this valuable agricultural resource. Vertical accuracy of WV-3 derived DSM was evaluated at two test sites within Olive Gardens of Lun (Pag Island, Croatia), through the comparison with reference UAV photogrammetry derived VHR DSM. Two test sites were selected by object-based approach, established on spectral (NDVI, VARI) and height information (digital olive models (DOMs)). While first test site covers one single, individual oldest olive tree (45 m²), second test site covers larger area (2500 m²) with dense, unattended olive trees. Although vertical accuracy of individual olive trees still significantly deviates from reference model (RMSE = 3.604 m; MAE = 3.203 m), accuracy within larger test was much better (RMSE = 1.462 m; MAE = 1.127 m). This demonstrated that WV-3 stereo imagery has great potential for application in creation of DSMs over large scale forested areas, that would be hard to cover with field geospatial techniques (e.g. LiDAR or UAV photogrammetry).
Topographic and elevation data are essential in the development of supporting infrastructure around mining sites. The de facto standard for acquiring elevation data is through light detection and ranging (lidar). The high labour and monetary cost of acquiring lidar has fostered more cost-effective approaches for creating elevation models that use stereo photogrammetry. To assess the accuracy of stereo-photogrammetry-derived elevation models and their potential application, we benchmark satellite (Worldview-2) and aircraft (South Central Ontario Orthoimagery Project; SCOOP) stereo-derived digital surface models (DSMs) against a lidarderived DSM. Our results show that both stereo-derived DSMs have strong monotonic correlations with lidar across a range of land-cover types and slopes. The overall vertical accuracy of Worldview-2 and SCOOP DSMs are similar and do not meet the United States National Digital Elevation Program (NDEP) standards. However, accuracy assessment across land-cover types and slope categories show that specific land cover types (i.e. grass, row crops/pasture, sparse vegetation and marsh) on gently sloping terrain compare well to lidar data and meet NDEP accuracy standards. We situate the presented research in the context of northern resource development and discuss opportunities to improve the vertical accuracy of stereo-derived DSMs, for example, through unmanned aerial systems.