POINT-TO-SURFACE MATCHING FOR DEM CORRECTION USING ICESAT DATA

KOMPSAT-2와 같은 고해상 위성영상은 대상영역의 3차원 위치결정을 위하여 RPC(Rational Polynomial Coefficient)가 포함된 자료를 제공한다. 그러나 RPC로 계산된 영상기하는 일정량의 편이(systematic errors)를 지니고 있는 상태이며, 이를 보정하기 위해서는 수 개 이상의 지상기준점(ground control point)이 필요하다. 이에 본 논문에서는 지상기준점 없이 입체영상(stereo pair)과 SRTM(Shuttle Radar Topography Mission) DEM(Digital Elevation Model) 사이의 대응점(tie point)만을 이용하여 자동으로 영상 기하를 보정하는 효과적인 방법을 제안하였다. 이러한 방법은 4가지 단계를 포함 한다: 1) 대응점 추출, 2) 대응점에 대한 지상좌표 결정, 3) SRTM DEM을 이용한 지상좌표의 보정, 4) RPC 보정 모델의 파라미터 결정. 우리는 KOMPSAT-2 입체영상을 이용하여 제안된 방법의 성과를 입증하였다. 검사점(check point)을 통해 계산된 RMSE(Root Mean Square Error)는 X와 Y, Z방향으로 각각 약 3.55 m, 9.70 m, 3.58 m를 나타냈다. 이는 SRTM DEM을 이용하여 RPC가 지닌 편이를 X, Y 및 Z 모든방향에 대하여 10 m이내의 정확도로 자동보정할 수 있다는 것을 의미한다. A high resolution satellite imagery such as KOMPSAT-2 includes a material containing rational polynomial coefficient (RPC) for three-dimensional geopositioning. However, image geometries which are calculated from the RPC must have inevitable systematic errors. Thus, it is necessary to correct systematic errors of the RPC using several ground control points (GCPs). In this paper, we propose an efficient method for automatic correction of image geometries using tie points of a stereo pair and the Shuttle Radar Topography Mission (SRTM) Digital Elevation Model (DEM) without GCPs. This method includes four steps: 1) tie points extraction, 2) determination of the ground coordinates of the tie points, 3) refinement of the ground coordinates using SRTM DEM, and 4) RPC adjustment model parameter estimation. We validates the performance of the proposed method using KOMPSAT-2 stereo pair. The root mean square errors (RMSE) achieved from check points (CPs) were about 3.55 m, 9.70 m and 3.58 m in X, Y;and Z directions. This means that we can automatically correct the systematic error of RPC using SRTM DEM.

Enhancing Large-Area DEM modeling of GF-7 stereo imagery: Integrating ICESat-2 data with Multi-characteristic constraint filtering and terrain matching correction

The correct representation of the topography of terrain is an important requirement to generate photogrammetric products such as orthoimages and maps from high-resolution (HR) or very high-resolution (VHR) satellite datasets. The refining of the digital elevation model (DEM) for the generation of an orthoimage is a vital step with a direct effect on the final accuracy achieved in the orthoimages. The refined DEM has potential applications in various domains of earth sciences such as geomorphological analysis, flood inundation mapping, hydrological analysis, large-scale mapping in an urban environment, etc., impacting the resulting output accuracy. Manual editing is done in the presented study for the automatically generated DEM from IKONOS data consequent to the satellite triangulation with a root mean square error (RMSE) of 0.46, using the rational function model (RFM) and an optimal number of ground control points (GCPs). The RFM includes the rational polynomial coefficients (RPCs) to build the relation between image space and ground space. The automatically generated DEM initially represents the digital surface model (DSM), which is used to generate a digital terrain model (DTM) in this study for improving orthoimages for an area of approximately 100 km2. DSM frequently has errors due to mass points in hanging (floating) or digging, which need correction while generating DTM. The DTM assists in the removal of the geometric effects (errors) of ground relief present in the DEM (i.e., DSM here) while generating the orthoimages and thus improves the quality of orthoimages, especially in areas such as Dehradun that have highly undulating terrain with a large number of natural drainages. The difference image of reference, i.e., edited IKONOS DEM (now representing DTM) and automatically generated IKONOS DEM, i.e., DSM, has a mean difference of 1.421 m. The difference DEM (dDEM) for the reference IKONOS DEM and generated Cartosat-1 DEM at a 10 m posting interval (referred to as Carto10 DEM) results in a mean difference of 8.74 m.

Along with the appearance of high resolution satellite images, image correction using Rational Polynomial Coefficients (RPCs) has become common. Location accuracy of Korea Multipurpose Satellite (KOMPSAT) standard images is still not adequate, so, in order to leverage the KOMPSAT images for applications such as mapping and change detection, it is necessary to orthorectify the images. In this study, using updated RPCs, we performed orthorectification of KOMPSAT-2, KOMPSAT-3, and KOMPSAT-3A images using various data. Through this study, we discovered that the orthorectification result using precise Ground Control Points (GCPs) and Digital Elevation Model (DEM) is the best, but it was found that the correction results through image matching are also excellent. In particular, it was confirmed that orthoimages with a planimetric accuracy around 3 m (Root Mean Square Error (RMSE)) can be generated by using well-known matching algorithms with open data such as OpenStreetMap (OSM) and Shuttle Radar Topography Mission (SRTM) DEM, which can be acquired by anyone. Although the accuracy was low in some mountainous terrain, it was confirmed that it could be used for generating KOMPSAT orthoimages using open data. This paper describes the results for orthorectifying high resolution KOMPSAT optical images using various reference data.

The new generation of satellite-borne laser radar Ice, Cloud, and land Elevation Satellite-2 (ICESat-2) data has been successfully used for ground information acquisition. However, when dealing with complex terrain and dense vegetation cover, the accuracy of the extracted understory Digital Terrain Model (DTM) is limited. Therefore, this paper proposes a photon correction data processing method based on ICESat-2 to improve the DTM inversion accuracy in complex terrain and high forest coverage areas. The correction value is first extracted based on the ALOS PALSAR DEM reference data to correct the cross-track photon data of ICESat-2. The slope filter threshold is then selected from the reference data, and the extracted possible ground photons are slope filtered to obtain accurate ground photons. Finally, the impacts of cross-track photon and slope filtering on fine ground extraction from the ICESat-2 data are discussed. The results show that the proposed photon correction and slope filtering algorithms help to improve the extraction accuracy of forest DTM in complex terrain areas. Compared with the forest DTM extracted without the photon correction and slope filtering methods, the MAE (Mean Absolute Error) and RMSE (Root Mean Square Error) are reduced by 51.90~57.82% and 49.37~53.55%, respectively. To the best of our knowledge, this is the first study demonstrating that photon correction can improve the terrain inversion ability of ICESat-2, while providing a novel method for ground extraction based on ICESat-2 data. It provides a theoretical basis for the accurate inversion of canopy parameters for ICESat-2.

The present study evaluates the accuracy of Triangulation, Digital Elevation Model (DEM) and orthoimage generated by Cartosat-1 data by using Rational Polynomial Coefficient’s (RPC’s) provided with the Cartosat-1 satellite stereo (PAN) data. The study area is taken as Jaipur city, Rajasthan. The data acquired over Jaipur, on 18 May’ 2005 was analysed. The stereopair comprises of terrain ranging from plain area, agriculture fields, urban area, waterbody and Aravalli hill range. A Differential Global Positioning System (DGPS) survey was conducted to collect ground control points (GCPs) for satellite triangulation, DEM generation, orthoimage generation and subsequent analysis for accuracy assessment. Eighteen GCPs were measured through DGPS survey. Twelve GCPs are used for triangulation and six GCPs were used as check points. The overall RMSE for satellite triangulation is 0.753 pixel. Horizontal accuracy of 2.08 m and 1.74 m is achieved in X and Y direction, respectively. The vertical accuracy of 3.72 m has been assessed separately also by using independent check point in highly varying terrain.

Satellite-derived Digital Elevation Model (DEM) selection, preparation and correction for hydrodynamic modelling in large, low-gradient and data-sparse catchments

GaoFen-4 (GF-4) imagery has very potential in terms of emergency response due to its gazing mode. However, only poor geometric accuracy can be obtained using the rational polynomial coefficient (RPC) parameters provided, making ground control points (GCPs) necessary for emergency response. However, selecting GCPs is traditionally time-consuming, labor-intensive, and not fully reliable. This is mainly due to the facts that (1) manual GCP selection is time-consuming and cumbersome because of too many human interventions, especially for the first few GCPs; (2) typically, GF-4 gives planar array imagery acquired at rather large tilt angles, and the distortion introduces problems in image matching; (3) reference data will not always be available, especially under emergency circumstances. This paper provides a novel emergency georeferencing framework for GF-4 Level 1 imagery. The key feature is GCP prediction based on dynamic RPC refinement, which is able to predict even the first GCP and the prediction will be dynamically refined as the selection goes on. This is done by two techniques: (1) GCP prediction using RPC parameters and (2) dynamic RPC refinement using as few as only one GCP. Besides, online map services are also adopted to automatically provide reference data. Experimental results show that (1) GCP predictions improve using dynamic RPC refinement; (2) GCP selection becomes more efficient with GCP prediction; (3) the integration of online map services constitutes a good example for emergency response.

This paper provides geometric accuracy improvements for very high resolution WorldView‐2 (WV2) Ortho Ready Standard Level‐2A (ORS2A) satellite images in the panchromatic (PAN) band. The entire process can be divided into two steps: (1) sensor orientation, which was conducted on 40 ground control points (GCPs) to determine which sensor model was best depending on various numbers of GCPs; and (2) orthorectification, whose main objective was to compare the accuracy of the process when using different freely available global digital elevation models (DEMs). The research showed that a sensor model based on rational polynomial coefficients (RPCs) with a shift or zero‐order bias correction attained the best geopositioning accuracy during the orientation step with the use of seven GCPs. The highest accuracy was achieved for images orthorectified with the ALOS World 3D‐30 m DEM (AW3D30).

Abstract. Accurate information of ice sheet surface slope is essential for estimating elevation change by satellite altimetry measurement. A study is carried out to recover surface slope of Antarctic ice sheet from Ice, Cloud and land Elevation Satellite (ICESat) elevation measurements based on repeat orbits. ICESat provides repeat ground tracks within 200 meters in cross-track direction and 170 meters in along-track direction for most areas of Antarctic ice sheet. Both cross-track and along-track surface slopes could be obtained by adjacent repeat ground tracks. Combining those measurements yields a surface slope model with resolution of approximately 200 meters. An algorithm considering elevation change is developed to estimate the surface slope of Antarctic ice sheet. Three Antarctic Digital Elevation Models (DEMs) were used to calculate surface slopes. The surface slopes from DEMs are compared with estimates by using in situ GPS data in Dome A, the summit of Antarctic ice sheet. Our results reveal an average surface slope difference of 0.02 degree in Dome A. High resolution remote sensing images are also used in comparing the results derived from other DEMs and this paper. The comparison implies that our results have a slightly better coherence with GPS observation than results from DEMs, but our results provide more details and perform higher accuracy in coastal areas because of the higher resolution for ICESat measurements. Ice divides are estimated based on the aspect, and are weakly consistent with ice divides from other method in coastal regions.

Satellite images can be rectified and adapted to map representation also without information about viewing geometry of the sensor. Polynomial Functions (PFs) or Rational Polynomial Functions (RPFs) can be applied for this purpose, both requiring Ground Control Points (GCPs), of which the positions in the image as well as in the real world must be known. Typically for PFs only planimetric (X, Y) positions of GCPs are used while for RPFs 3D coordinates (X, Y, Z) of them as well as a DEM (Digital Elevation Model) of the entire scene are required. Check Points (CPs) with the same characteristics of GCPs (but not coincident with them) are used to better verify the accuracy of the product. Not only topographic survey, but also maps or ortho-photos with adequate resolution supply the coordinates of GCPs as well as CPs. This paper is aimed to compare methods to rectify IKONOS images based on PFs or RPFs applications, considering the positional accuracy of the results as index for performance evaluation. Tests were executed on IKONOS panchromatic image of an area of the Cilento and Vallo di Diano National Park, in Campania Region (Italy): ortho-photos (scale 1:10,000) were used for GCPs and CPs planimetric position in the real world while for RPFs applications also DEM (cell size 20 m) was considered as source of 3D information. To compare the selected methods, differences (residuals) between the X, Y coordinates of GCPs (but also of the CPs) on the ortho-photos and corresponding values in the rectified image were calculated and evaluated. The positional accuracy of the resulting products in relation to the method as well as to the number of GCPs was analyzed; also the implications of the calculation of Rational Polynomial Coefficients (RPCs) in alternative to the use of the values supplied for them by the image provider were investigated.

Satellite optical images and altimetry data are two major data sources used in Antarctic research. The integration use of these two datasets is expected to provide more accurate and higher quality products, during which data registration is the first issue that needs to be solved. This paper presents an alternative approach for the registration of high-resolution satellite optical images and ICESat (Ice, Cloud, and land Elevation Satellite) laser altimetry data. Due to the sparse distribution characteristic of the ICESat laser point data, it is difficult and even impossible to find same-type conjugate features between ICESat data and satellite optical images. The method is implemented in a direct way to correct the point-to-line inconsistency in image space through 2D transformation between the projected terrain feature points and the corresponding 2D image lines, which is simpler than discrepancy correction in object space that requires stereo images for 3D model construction, and easier than the indirect way of image orientation correction via photogrammetric bundle adjustment. The correction parameters are further incorporated into imaging model through RPCs (Rational Polynomial Coefficients) generation/regeneration for the convenience of photogrammetric applications. The experimental results by using the ASTER (Advanced Spaceborne Thermal Emission and Reflection Radiometer) images and ZY-3 (Ziyuan-3 satellite) images for registration with ICESat data showed that sub-pixel level registration accuracies were achieved after registration, which have validated the feasibility and effectiveness of the presented approach.

The number of high and very high resolution (VHR) optical satellite sensors, as well as the number of medium resolution satellites is continuously growing. However, not all high-resolution optical satellite imaging cameras have a sufficient and stable calibration in time. Due to their high agility in rotation, a quick change in viewing direction can lead to satellite attitude oscillation, causing image distortions and thus affecting image geometry and geo-positioning accuracy. This paper presents an approach based on re-projection of regularly distributed 3D ground points from object in image space, to detect and estimate the periodic distortions of Pléiades tri-stereo imagery caused by satellite attitude oscillations. For this, a hilly region was selected as a test site. Consequently, we describe a complete processing pipeline for computing the systematic height errors (deformations, waves) of the satellite-based digital elevation model by using a Lidar high resolution terrain model. Ground points with fixed positions, but with two elevations (actual and corrected) are then re-projected to the satellite images with the aid of the Rational Polynomial Coefficients (RPCs) provided with the imagery. Therefore, image corrections (displacements) are determined by computing the differences between the distinct positions of corresponding points in image space. Our experimental results in Allentsteig (Lower Austria) show that the systematic height errors of satellite-based elevation models cannot be compensated with an usual or even high number of Ground Control Points (GCPs) for RPC bias correction, due to insufficiently known image orientations. In comparison to a reference Lidar Digital Terrain Model (DTM), the computed elevation models show undulation effects with a maximum height difference of 0.88 m in along-track direction. With the proposed method, image distortions in-track direction with amplitudes of less than 0.15 pixels were detected. After applying the periodic distortion compensation to all three images, the systematic elevation discrepancies from the derived elevation models were successfully removed and the overall accuracy in open areas improved by 33% in the RMSE. Additionally, we show that a coarser resolution reference elevation model (AW3D30) is not feasible for improving the geometry of the Pléiades tri-stereo satellite imagery.

<p>After launching on 15 September 2018, the Ice, Cloud, and Land Elevation Satellite – 2 (ICESat-2) Mission began collecting data on 14 October 2018.  The mission uses green laser light emitted by the Advanced Topographic Laser Altimetry System (ATLAS) to detect individual photons that are reflected by the Earth’s surface and returned to ATLAS.  These photons, when combined with information on the pointing direction, and position of the observatory in space, provide a geolocation and elevation for every measurement that spans the globe from 88 degrees north latitude to 88 degrees south.  The Global Geolocated Photon data product provides a latitude, longitude, elevation, and measurement time for each photon event telemetered to Earth for each of the instrument’s six beams. This product also delineates between high, medium, and low signal confidence levels and those measurements associated with background noise. The higher level, along-track products each use different strategies for photon aggregation to optimize the precision and accuracy of the surface retrievals over specific surface types. These types include land ice, sea ice, vegetation/land, ocean, and inland water. There is a separate channel dedicated to atmospheric returns to measure cloud and aerosols over a vertical window of 15 km. Calibration efforts utilized well designed on-orbit maneuvers to identify both pointing and range biases attributed to orbital variations on the satellite. Once corrected, the science-quality data products were released to the public in May 2019.</p><p> </p><p>In this presentation, we will present our ongoing work to evaluate and validate the geolocation and elevation accuracy and precision of measurements provided by the ICESat-2 mission.  The approaches are diverse in both location and methodology to ensure that we have a comprehensive assessment of the ATLAS performance variations throughout the orbital cycles. These strategies include comparisons with ground-based and airborne elevation measurements over the ice sheets, detailed analysis of returns from well-surveyed corner cube retro-reflectors, comparison of sea ice freeboard measured by airborne lidars, evaluation of global-scale ocean elevation through comparison with radar altimeters, and comparison of vegetation canopy height metrics measured by airborne lidar.  Our work to date demonstrates that individual photon elevations are accurate to approximately 30 cm vertically, and 6 m radially.  Aggregating many photons together reduces the elevation uncertainty to less than 5 cm for relatively flat and smooth ice sheet interiors.</p>

The Shuttle Radar Topography Mission (SRTM) provided scientists with digital elevation data on a nearly global scale and with highly consistent accuracy. This paper compares elevation values of the C-band SRTM 30-m digital elevation model (DEM) with pointwise elevations from the Ice, Cloud, and land-Elevation Satellite (ICESat) laser altimetry for Otter Tail County, Minnesota. The accuracy of SRTM DEM is measured as a function of land covers and geomorphologic characteristics. The typical mean vertical difference between the SRTM DEM and ICESat elevations in this paper was determined in each classified land-use type and is approximately 1.5 m over bare ground, with the SRTM measuring lower elevations. Significant changes in the SRTM DEM uncertainties have been identified over different surface types classified from Landsat-7 imagery, e.g., bare ground, urban, and forested areas. Based on this result, the difference of the SRTM 30-m DEM and ICESat elevations has been removed from the DEM and made available for improved hydrological applications