DEM Generation Research Articles

Development and application of space-borne DEM generation

人工衛星などを用いた宇宙からの地形測量は，近年まで様々な手法で行われてきた．得られた地形のデータはDEM（Digital Elevation Model）と呼ばれ，商用利用も拡大しており，氷河研究においても重要な基盤データの一つである．この解説では，光学ステレオ立体視および合成開口レーダを用いたDEM作成技術の発展を総括する．そして無償公開されている画素サイズ30mのDEM を日本国内の各種地形およびヒマラヤ氷河域で比較し，生じる差異を検証する．国内の平地から山岳地域まで3種類の地形において精度検証したところSRTM1とASTER GDEMに対して，ALOS World 3Dがすべての地形で最も高精度であることが分かった．またヒマラヤ氷河域で生じるデータの欠損は，SRTM1では急斜面に多く存在するのに対して，ALOS World 3Dでは積雪の可能性が高く，平坦で標高の高い地表面に多く存在する傾向が明らかになった．山岳氷河の研究においては，これらの精度や特性の差異を考慮したうえで，適切なDEMを選択することが望まれる．

On the errors involved in ice-thickness estimates II: errors in digital elevation models of ice thickness

ABSTRACTThis paper is the second (Paper II) in a set of studies concerning the errors involved in the estimate of ice thickness and ice volume. Here we present a detailed analysis of the errors involved in the generation of ice-thickness DEMs constructed, most often, from GPR data, complemented by boundary data and sometimes, additional synthetic data arising from estimates based on theoretical considerations supported by independent data. We describe a complete methodology of error analysis that, starting from the errors in the data, propagates them to the grid nodes. In turn, the interpolation error at the grid nodes is calculated using a novel procedure that also provides an estimate of the bias introduced by the interpolation process. Finally, both errors are combined at the grid nodes to produce a gridpoint-dependent error estimate, which is complemented by an overall error estimate providing an assessment of the quality of the DEM. This methodology is illustrated with the case study of Werenskioldbreen, a land-terminating polythermal glacier in Svalbard.

OBPCA 기반의 수직단면 이용 차량 추출 기법

점 클라우드로부터 차량을 추출하는 다양한 방식 중 OBPCA 방식은 세그먼트 단위의 평가-분류로 정확도가 높고, 단순한 직사각형 평면도에서 특성 값들을 추출하므로 분류가 빠르다. 그러나 이 OBPCA 방식은 차량과 크기가 비슷한 직육면체 모양의 물체를 차량과 구별하지 못하는 문제를 가지므로 이를 극복하고 차량 추출의 정확도를 높이는 방안에 대한 연구가 필요하다. 본 논문에서는 이 문제를 해결하기 위해 수평 단면과 함께 수직 단면을 이용하는 확장 OBPCA 방식을 제안한다. 제안 방법은 수평 단면을 통해 차량 후보를 1차로 선별하고, 각 차량 후보에서 가장 특징적인 수직 단면을 찾아서 그 단면의 특성 값들을 임계값들과 비교하여 차량 여부를 판단한다. 비교실험에서는 본 제안방식이 기존 OBPCA 방식에 비해 정밀도가 6.61% 향상되고 위양성률이 13.96% 감소됨을 확인했으며, 이를 통해 제안 방식이 기존 OBPCA 분류오류 문제에 대해 효과적인 해결방안임을 보였다. Among various vehicle extraction techniques, OBPCA (Object-Based Point Cloud Analysis) calculates features quickly by coarse-grained rectangles from top-view of the vehicle candidates. However, it uses only a top-view rectangle to detect a vehicle. Thus, it is hard to extract rectangular objects with similar size. For this reason, accuracy issue has raised on the OBPCA method which influences on DEM generation and traffic monitoring tasks. In this paper, we propose a novel method which uses the most distinguishing vertical elevations to calculate additional features. Our proposed method uses same features with top-view, determines new thresholds, and decides whether the candidate is vehicle or not. We compared the accuracy and execution time between original OBPCA and the proposed one. The experiment result shows that our method produces 6.61% increase of precision and 13.96% decrease of false positive rate despite with marginal increase of execution time. We can see that the proposed method can reduce misclassification.

An algorithm for generation of DEMs from contour lines considering geomorphic features

Geomorphic information is omitted from many existing methods of generating gridded digital elevation models (DEMs) from contour lines, resulting in significant errors during interpolation. Here, we present an advanced schema for improvement of the comprehensive regionalized method of linear interpolation. This approach uses a moving fitting method for an interpolated point and selects elevation points that are representative of geomorphic features as a whole to improve interpolation quality. A total of 16 points are selected, according to certain criteria, in eight directions surrounding the interpolated point; thus, there are two points in each direction, which is sufficient to provide an accurate representation of the geomorphic features of the DEM. Our method introduces virtual control points to prevent sudden changes in the interpolation results, which helps to overcome problems related to the distortion of the local geospatial distribution in areas where feature geomorphic information is inadequate. We construct the spline interpolation function using intersection points and virtual control points, all of which are applied to compute the point elevation. Moreover, we index all elevation values and spatial points of linear features using the R-tree method to ensure that points related to an interpolated position can be retrieved as quickly as possible. Finally, we test our method using a coal mine elevation dataset. The results confirm that our proposed method can generate DEMs smoothly and, in particular, avoid problems related to local distortion. ResumenLa información geomórfica se omite en muchos de los métodos de generación de Modelos Digitales de Elevación (DEM, en inglés) que se elaboran a partir de líneas de contorno, lo que resulta en errores significativos durante la interpolación. En este trabajo se presenta un esquema avanzado para el mejoramiento del método comprensivo regionalizado de interpolación lineal. Esta aproximación utiliza un método de ajuste móvil para un punto interpolado y selecciona puntos de elevación representativos o características geomórficas como un todo para mejorar la calidad de la interpolación. Se seleccionaron 16 puntos de acuerdo con ciertos criterios, en ocho direcciones alrededor del punto interpolado; por lo tanto, hay dos puntos en cada dirección, lo que es suficiente para proveer una representación precisa de las características geomórficas del DEM. El método propuesto consta de puntos virtuales de control para prevenir cambios repentinos en los resultados de la interpolación, lo que ayuda a vencer los problemas relacionados a la distorsión de la distribución geoespacial local en áreas donde la información de las características geomórficas es inadecuada. Se utilizó una función de interpolación spline con puntos de intersección y puntos de control virtual, que fueron utilizados para calcular el punto de elevación. Además, se indexaron todos los valores de elevación y los puntos espaciales de las características lineales con el método de árbol-R para asegurar que los puntos relacionados a una posición interpolada pueden ser recuperados tan rápido como sea posible. Finalmente, el método fue evaluado con la configuración de elevación de una mina de carbón. Los resultados confirman que el método propuesto puede generar modelos sin problemas y, en particular, evitar complicaciones relacionadas a distorsión local.

GLACIER VOLUME CHANGE ESTIMATION USING TIME SERIES OF IMPROVED ASTER DEMS

Abstract. Volume change data is critical to the understanding of glacier response to climate change. The Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) system embarked on the Terra (EOS AM-1) satellite has been a unique source of systematic stereoscopic images covering the whole globe at 15m resolution and at a consistent quality for over 15 years. While satellite stereo sensors with significantly improved radiometric and spatial resolution are available to date, the potential of ASTER data lies in its long consistent time series that is unrivaled, though not fully exploited for change analysis due to lack of data accuracy and precision. Here, we developed an improved method for ASTER DEM generation and implemented it in the open source photogrammetric library and software suite MicMac. The method relies on the computation of a rational polynomial coefficients (RPC) model and the detection and correction of cross-track sensor jitter in order to compute DEMs. ASTER data are strongly affected by attitude jitter, mainly of approximately 4 km and 30 km wavelength, and improving the generation of ASTER DEMs requires removal of this effect. Our sensor modeling does not require ground control points and allows thus potentially for the automatic processing of large data volumes. As a proof of concept, we chose a set of glaciers with reference DEMs available to assess the quality of our measurements. We use time series of ASTER scenes from which we extracted DEMs with a ground sampling distance of 15m. Our method directly measures and accounts for the cross-track component of jitter so that the resulting DEMs are not contaminated by this process. Since the along-track component of jitter has the same direction as the stereo parallaxes, the two cannot be separated and the elevations extracted are thus contaminated by along-track jitter. Initial tests reveal no clear relation between the cross-track and along-track components so that the latter seems not to be easily modeled analytically from the first one. We thus remove the remaining along-track jitter effects in the DEMs statistically through temporal DEM stacks to finally compute the glacier volume changes over time. Our method yields cleaner and spatially more complete elevation data, which also proved to be more in accordance to reference DEMs, compared to NASA’s AST14DMO DEM standard products. The quality of the demonstrated measurements promises to further unlock the underused potential of ASTER DEMs for glacier volume change time series on a global scale. The data produced by our method will help to better understand the response of glaciers to climate change and their influence on runoff and sea level.

GLACIER VOLUME CHANGE ESTIMATION USING TIME SERIES OF IMPROVED ASTER DEMS

Volume change data is critical to the understanding of glacier response to climate change. The Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) system embarked on the Terra (EOS AM-1) satellite has been a unique source of systematic stereoscopic images covering the whole globe at 15m resolution and at a consistent quality for over 15 years. While satellite stereo sensors with significantly improved radiometric and spatial resolution are available to date, the potential of ASTER data lies in its long consistent time series that is unrivaled, though not fully exploited for change analysis due to lack of data accuracy and precision. Here, we developed an improved method for ASTER DEM generation and implemented it in the open source photogrammetric library and software suite MicMac. The method relies on the computation of a rational polynomial coefficients (RPC) model and the detection and correction of cross-track sensor jitter in order to compute DEMs. ASTER data are strongly affected by attitude jitter, mainly of approximately 4 km and 30 km wavelength, and improving the generation of ASTER DEMs requires removal of this effect. Our sensor modeling does not require ground control points and allows thus potentially for the automatic processing of large data volumes. <br><br> As a proof of concept, we chose a set of glaciers with reference DEMs available to assess the quality of our measurements. We use time series of ASTER scenes from which we extracted DEMs with a ground sampling distance of 15m. Our method directly measures and accounts for the cross-track component of jitter so that the resulting DEMs are not contaminated by this process. Since the along-track component of jitter has the same direction as the stereo parallaxes, the two cannot be separated and the elevations extracted are thus contaminated by along-track jitter. Initial tests reveal no clear relation between the cross-track and along-track components so that the latter seems not to be easily modeled analytically from the first one. We thus remove the remaining along-track jitter effects in the DEMs statistically through temporal DEM stacks to finally compute the glacier volume changes over time. Our method yields cleaner and spatially more complete elevation data, which also proved to be more in accordance to reference DEMs, compared to NASA’s AST14DMO DEM standard products. <br><br> The quality of the demonstrated measurements promises to further unlock the underused potential of ASTER DEMs for glacier volume change time series on a global scale. The data produced by our method will help to better understand the response of glaciers to climate change and their influence on runoff and sea level.

ANALYSIS OF INFLUENCE OF TERRAIN RELIEF ROUGHNESS ON DEM ACCURACY GENERATED FROM LIDAR IN THE CZECH REPUBLIC TERRITORY

Abstract. Digital elevation models are today a common part of geographic information systems and derived applications. The way of their creation is varied. It depends on the extent of area, required accuracy, delivery time, financial resources and technologies available. The first model covering the whole territory of the Czech Republic was created already in the early 1980's. Currently, the 5th DEM generation is being finished. Data collection for this model was realized using the airborne laser scanning which allowed creating the DEM of a new generation having the precision up to a decimetre. Model of such a precision expands the possibilities of employing the DEM and it also offers new opportunities for the use of elevation data especially in a domain of modelling the phenomena dependent on highly accurate data. The examples are precise modelling of hydrological phenomena, studying micro-relief objects, modelling the vehicle movement, detecting and describing historical changes of a landscape, designing constructions etc. Due to a nature of the technology used for collecting data and generating DEM, it is assumed that the resulting model achieves lower accuracy in areas covered by vegetation and in built-up areas. Therefore the verification of model accuracy was carried out in five selected areas in Moravia. The network of check points was established using a total station in each area. To determine the reference heights of check points, the known geodetic points whose heights were defined using levelling were used. Up to several thousands of points were surveyed in each area. Individual points were selected according to a different configuration of relief, different surface types, and different vegetation coverage. The sets of deviations were obtained by comparing the DEM 5G heights with reference heights which was followed by verification of tested elevation model. Results of the analysis showed that the model reaches generally higher precision than the declared one in majority of areas. This applies in particular to areas covered by vegetation. By contrast, the larger deviations occurred in relation to the slope of the terrain, in particular in the micro-relief objects. The results are presented in this article.

ANALYSIS OF INFLUENCE OF TERRAIN RELIEF ROUGHNESS ON DEM ACCURACY GENERATED FROM LIDAR IN THE CZECH REPUBLIC TERRITORY

Digital elevation models are today a common part of geographic information systems and derived applications. The way of their creation is varied. It depends on the extent of area, required accuracy, delivery time, financial resources and technologies available. The first model covering the whole territory of the Czech Republic was created already in the early 1980's. Currently, the 5th DEM generation is being finished. Data collection for this model was realized using the airborne laser scanning which allowed creating the DEM of a new generation having the precision up to a decimetre. Model of such a precision expands the possibilities of employing the DEM and it also offers new opportunities for the use of elevation data especially in a domain of modelling the phenomena dependent on highly accurate data. The examples are precise modelling of hydrological phenomena, studying micro-relief objects, modelling the vehicle movement, detecting and describing historical changes of a landscape, designing constructions etc. <br><br> Due to a nature of the technology used for collecting data and generating DEM, it is assumed that the resulting model achieves lower accuracy in areas covered by vegetation and in built-up areas. Therefore the verification of model accuracy was carried out in five selected areas in Moravia. The network of check points was established using a total station in each area. To determine the reference heights of check points, the known geodetic points whose heights were defined using levelling were used. Up to several thousands of points were surveyed in each area. Individual points were selected according to a different configuration of relief, different surface types, and different vegetation coverage. The sets of deviations were obtained by comparing the DEM 5G heights with reference heights which was followed by verification of tested elevation model. Results of the analysis showed that the model reaches generally higher precision than the declared one in majority of areas. This applies in particular to areas covered by vegetation. By contrast, the larger deviations occurred in relation to the slope of the terrain, in particular in the micro-relief objects. The results are presented in this article.

CLASSIFICATION OF LiDAR DATA WITH POINT BASED CLASSIFICATION METHODS

Abstract. LiDAR is one of the most effective systems for 3 dimensional (3D) data collection in wide areas. Nowadays, airborne LiDAR data is used frequently in various applications such as object extraction, 3D modelling, change detection and revision of maps with increasing point density and accuracy. The classification of the LiDAR points is the first step of LiDAR data processing chain and should be handled in proper way since the 3D city modelling, building extraction, DEM generation, etc. applications directly use the classified point clouds. The different classification methods can be seen in recent researches and most of researches work with the gridded LiDAR point cloud. In grid based data processing of the LiDAR data, the characteristic point loss in the LiDAR point cloud especially vegetation and buildings or losing height accuracy during the interpolation stage are inevitable. In this case, the possible solution is the use of the raw point cloud data for classification to avoid data and accuracy loss in gridding process. In this study, the point based classification possibilities of the LiDAR point cloud is investigated to obtain more accurate classes. The automatic point based approaches, which are based on hierarchical rules, have been proposed to achieve ground, building and vegetation classes using the raw LiDAR point cloud data. In proposed approaches, every single LiDAR point is analyzed according to their features such as height, multi-return, etc. then automatically assigned to the class which they belong to. The use of un-gridded point cloud in proposed point based classification process helped the determination of more realistic rule sets. The detailed parameter analyses have been performed to obtain the most appropriate parameters in the rule sets to achieve accurate classes. The hierarchical rule sets were created for proposed Approach 1 (using selected spatial-based and echo-based features) and Approach 2 (using only selected spatial-based features) and have been tested in the study area in Zekeriyaköy, Istanbul which includes the partly open areas, forest areas and many types of the buildings. The data set used in this research obtained from Istanbul Metropolitan Municipality which was collected with ‘Riegl LSM-Q680i’ full-waveform laser scanner with the density of 16 points/m2. The proposed automatic point based Approach 1 and Approach 2 classifications successfully produced the ground, building and vegetation classes which were very similar although different features were used.

CLASSIFICATION OF LiDAR DATA WITH POINT BASED CLASSIFICATION METHODS

LiDAR is one of the most effective systems for 3 dimensional (3D) data collection in wide areas. Nowadays, airborne LiDAR data is used frequently in various applications such as object extraction, 3D modelling, change detection and revision of maps with increasing point density and accuracy. The classification of the LiDAR points is the first step of LiDAR data processing chain and should be handled in proper way since the 3D city modelling, building extraction, DEM generation, etc. applications directly use the classified point clouds. The different classification methods can be seen in recent researches and most of researches work with the gridded LiDAR point cloud. In grid based data processing of the LiDAR data, the characteristic point loss in the LiDAR point cloud especially vegetation and buildings or losing height accuracy during the interpolation stage are inevitable. In this case, the possible solution is the use of the raw point cloud data for classification to avoid data and accuracy loss in gridding process. In this study, the point based classification possibilities of the LiDAR point cloud is investigated to obtain more accurate classes. The automatic point based approaches, which are based on hierarchical rules, have been proposed to achieve ground, building and vegetation classes using the raw LiDAR point cloud data. In proposed approaches, every single LiDAR point is analyzed according to their features such as height, multi-return, etc. then automatically assigned to the class which they belong to. The use of un-gridded point cloud in proposed point based classification process helped the determination of more realistic rule sets. The detailed parameter analyses have been performed to obtain the most appropriate parameters in the rule sets to achieve accurate classes. The hierarchical rule sets were created for proposed Approach 1 (using selected spatial-based and echo-based features) and Approach 2 (using only selected spatial-based features) and have been tested in the study area in Zekeriyaköy, Istanbul which includes the partly open areas, forest areas and many types of the buildings. The data set used in this research obtained from Istanbul Metropolitan Municipality which was collected with ‘Riegl LSM-Q680i’ full-waveform laser scanner with the density of 16 points/m2. The proposed automatic point based Approach 1 and Approach 2 classifications successfully produced the ground, building and vegetation classes which were very similar although different features were used.

POINT CLOUD ORIENTED SHOULDER LINE EXTRACTION IN LOESS HILLY AREA

Abstract. Shoulder line is the significant line in hilly area of Loess Plateau in China, dividing the surface into positive and negative terrain (P-N terrains). Due to the point cloud vegetation removal methods of P-N terrains are different, there is an imperative need for shoulder line extraction. In this paper, we proposed an automatic shoulder line extraction method based on point cloud. The workflow is as below: (i) ground points were selected by using a grid filter in order to remove most of noisy points. (ii) Based on DEM interpolated by those ground points, slope was mapped and classified into two classes (P-N terrains), using Natural Break Classified method. (iii) The common boundary between two slopes is extracted as shoulder line candidate. (iv) Adjust the filter gird size and repeat step i-iii until the shoulder line candidate matches its real location. (v) Generate shoulder line of the whole area. Test area locates in Madigou, Jingbian County of Shaanxi Province, China. A total of 600 million points are acquired in the test area of 0.23km2, using Riegl VZ400 3D Laser Scanner in August 2014. Due to the limit Granted computing performance, the test area is divided into 60 blocks and 13 of them around the shoulder line were selected for filter grid size optimizing. The experiment result shows that the optimal filter grid size varies in diverse sample area, and a power function relation exists between filter grid size and point density. The optimal grid size was determined by above relation and shoulder lines of 60 blocks were then extracted. Comparing with the manual interpretation results, the accuracy of the whole result reaches 85%. This method can be applied to shoulder line extraction in hilly area, which is crucial for point cloud denoising and high accuracy DEM generation.

POINT CLOUD ORIENTED SHOULDER LINE EXTRACTION IN LOESS HILLY AREA

Shoulder line is the significant line in hilly area of Loess Plateau in China, dividing the surface into positive and negative terrain (P-N terrains). Due to the point cloud vegetation removal methods of P-N terrains are different, there is an imperative need for shoulder line extraction. In this paper, we proposed an automatic shoulder line extraction method based on point cloud. The workflow is as below: (i) ground points were selected by using a grid filter in order to remove most of noisy points. (ii) Based on DEM interpolated by those ground points, slope was mapped and classified into two classes (P-N terrains), using Natural Break Classified method. (iii) The common boundary between two slopes is extracted as shoulder line candidate. (iv) Adjust the filter gird size and repeat step i-iii until the shoulder line candidate matches its real location. (v) Generate shoulder line of the whole area. Test area locates in Madigou, Jingbian County of Shaanxi Province, China. A total of 600 million points are acquired in the test area of 0.23km2, using Riegl VZ400 3D Laser Scanner in August 2014. Due to the limit Granted computing performance, the test area is divided into 60 blocks and 13 of them around the shoulder line were selected for filter grid size optimizing. The experiment result shows that the optimal filter grid size varies in diverse sample area, and a power function relation exists between filter grid size and point density. The optimal grid size was determined by above relation and shoulder lines of 60 blocks were then extracted. Comparing with the manual interpretation results, the accuracy of the whole result reaches 85%. This method can be applied to shoulder line extraction in hilly area, which is crucial for point cloud denoising and high accuracy DEM generation.

GEOSPATIAL ANALYSIS USING REMOTE SENSING IMAGES: CASE STUDIES OF ZONGULDAK TEST FIELD

Abstract. Inclined topographies are one of the most challenging problems for geospatial analysis of air-borne and space-borne imageries. However, flat areas are mostly misleading to exhibit the real performance. For this reason, researchers generally require a study area which includes mountainous topography and various land cover and land use types. Zonguldak and its vicinity is a very suitable test site for performance investigation of remote sensing systems due to the fact that it contains different land use types such as dense forest, river, sea, urban area; different structures such as open pit mining operations, thermal power plant; and its mountainous structure. In this paper, we reviewed more than 120 proceeding papers and journal articles about geospatial analysis that are performed on the test field of Zonguldak and its surroundings. Geospatial analysis performed with imageries include elimination of systematic geometric errors, 2/3D georeferencing accuracy assessment, DEM and DSM generation and validation, ortho-image production, evaluation of information content, image classification, automatic feature extraction and object recognition, pan-sharpening, land use and land cover change analysis and deformation monitoring. In these applications many optical satellite images are used i.e. ASTER, Bilsat-1, IKONOS, IRS-1C, KOMPSAT-1, KVR-1000, Landsat-3-5-7, Orbview-3, QuickBird, Pleiades, SPOT-5, TK-350, RADARSAT-1, WorldView-1-2; as well as radar data i.e. JERS-1, Envisat ASAR, TerraSAR-X, ALOS PALSAR and SRTM. These studies are performed by Departments of Geomatics Engineering at Bülent Ecevit University, at İstanbul Technical University, at Yıldız Technical University, and Institute of Photogrammetry and GeoInformation at Leibniz University Hannover. These studies are financially supported by TÜBİTAK (Turkey), the Universities, ESA, Airbus DS, ERSDAC (Japan) and Jülich Research Centre (Germany).

GEOSPATIAL ANALYSIS USING REMOTE SENSING IMAGES: CASE STUDIES OF ZONGULDAK TEST FIELD

Inclined topographies are one of the most challenging problems for geospatial analysis of air-borne and space-borne imageries. However, flat areas are mostly misleading to exhibit the real performance. For this reason, researchers generally require a study area which includes mountainous topography and various land cover and land use types. Zonguldak and its vicinity is a very suitable test site for performance investigation of remote sensing systems due to the fact that it contains different land use types such as dense forest, river, sea, urban area; different structures such as open pit mining operations, thermal power plant; and its mountainous structure. In this paper, we reviewed more than 120 proceeding papers and journal articles about geospatial analysis that are performed on the test field of Zonguldak and its surroundings. Geospatial analysis performed with imageries include elimination of systematic geometric errors, 2/3D georeferencing accuracy assessment, DEM and DSM generation and validation, ortho-image production, evaluation of information content, image classification, automatic feature extraction and object recognition, pan-sharpening, land use and land cover change analysis and deformation monitoring. In these applications many optical satellite images are used i.e. ASTER, Bilsat-1, IKONOS, IRS-1C, KOMPSAT-1, KVR-1000, Landsat-3-5-7, Orbview-3, QuickBird, Pleiades, SPOT-5, TK-350, RADARSAT-1, WorldView-1-2; as well as radar data i.e. JERS-1, Envisat ASAR, TerraSAR-X, ALOS PALSAR and SRTM. These studies are performed by Departments of Geomatics Engineering at Bülent Ecevit University, at İstanbul Technical University, at Yıldız Technical University, and Institute of Photogrammetry and GeoInformation at Leibniz University Hannover. These studies are financially supported by TÜBİTAK (Turkey), the Universities, ESA, Airbus DS, ERSDAC (Japan) and Jülich Research Centre (Germany).

MEDIUM FORMAT CAMERA EVALUATION BASED ON THE LATEST PHASE ONE TECHNOLOGY

In early 2016, Phase One Industrial launched a new high resolution camera with a 100 MP CMOS sensor. CCD sensors excel at ISOs up to 200, but in lower light conditions, exposure time must be increased and Forward Motion Compensation (FMC) has to be employed to avoid smearing the images. The CMOS sensor has an ISO range of up to 6400, which enables short exposures instead of using FMC. <br><br> This paper aims to evaluate the strengths of each of the sensor types based on real missions over a test field in Speyer, Germany, used for airborne camera calibration. The test field area has about 30 Ground Control Points (GCPs), which enable a perfect scenario for a proper geometric evaluation of the cameras. The test field includes both a Siemen star and scale bars to show any blurring caused by forward motion. <br><br> The result of the comparison showed that both cameras offer high accuracy photogrammetric results with post processing, including triangulation, calibration, orthophoto and DEM generation. The forward motion effect can be compensated by a fast shutter speed and a higher ISO range of the CMOS-based camera. The results showed no significant differences between cameras.

MEDIUM FORMAT CAMERA EVALUATION BASED ON THE LATEST PHASE ONE TECHNOLOGY

Abstract. In early 2016, Phase One Industrial launched a new high resolution camera with a 100 MP CMOS sensor. CCD sensors excel at ISOs up to 200, but in lower light conditions, exposure time must be increased and Forward Motion Compensation (FMC) has to be employed to avoid smearing the images. The CMOS sensor has an ISO range of up to 6400, which enables short exposures instead of using FMC. This paper aims to evaluate the strengths of each of the sensor types based on real missions over a test field in Speyer, Germany, used for airborne camera calibration. The test field area has about 30 Ground Control Points (GCPs), which enable a perfect scenario for a proper geometric evaluation of the cameras. The test field includes both a Siemen star and scale bars to show any blurring caused by forward motion. The result of the comparison showed that both cameras offer high accuracy photogrammetric results with post processing, including triangulation, calibration, orthophoto and DEM generation. The forward motion effect can be compensated by a fast shutter speed and a higher ISO range of the CMOS-based camera. The results showed no significant differences between cameras.

Potential of RADARSAT-2 stereo radargrammetry for the generation of glacier DEMs

ABSTRACTThe study of glaciers and ice caps in remote and cloudy regions remains difficult using current remote sensing tools. Here the potential of stereo radargrammetry (SRG) with RADARSAT-2 Wide Ultra-Fine images is explored for DEM extraction, elevation changes and mass-balance calculations on Barnes Ice Cap (Nunavut, Canada). Over low-relief terrain surrounding Barnes, a vertical precision of ~7 m (1σ confidence level) is measured, as well as an average vertical bias of ~4 m. Moreover, we show that the C-band penetration depth over the ice cap is insignificant at this time of the year (i.e. late ablation season). This is likely due to a wet surface and the presence of superimposed ice that leads to a surface radar response. Comparing the SRG DEMs with other datasets, an historical glacier-wide mass balance of −0.52 ± 0.19 m w.e. a−1is estimated for 1960–2013, whereas it decreases to −1.06 ± 0.84 m w.e. a−1between 2005 and 2013. This clear acceleration of mass loss is in agreement with other recent studies. Given its all-weather functionality and its possible use without ground control points, the RADARSAT-2 SRG technology represents an appropriate alternative for glacier monitoring in cloudy and remote regions.

DEM Generation of Geological Boundary Surface based on Spline Interpolation Considering Distribution of Observed Data

DEM Generation of Geological Boundary Surface based on Spline Interpolation Considering Distribution of Observed Data

Flood-prone area delimitation using UAV technology, in the areas hard-to-reach for classic aircrafts: case study in the north-east of Apuseni Mountains, Transylvania

In the delimitation of flood-prone areas, particularly in areas that are difficult to access using traditional flight equipment, the most critical problem is the generation of a high-quality DEM of the major and minor riverbed, in order to obtain several hydraulic parameters (e.g., the surface of the drainage section, average water velocity) with equivalent accuracy. The chosen area for the case study represents one with the greatest history of hydrological risk factors in the Somesul Mic basin, given the extreme previous hydrological events that have taken place (e.g., in the year 1995) as well as the limited capacity of the river for upstream river flow, and to handle the volume of the flash floods. In this study, the use of UAV technology coupled with Leica MultiStation for validation made it possible to achieve this performance. Another advantage was the existence of the hydrometric station situated in the middle of the study area, the information from which was used for statistics, calibration of the measurements and validation of the results obtained (roughness of the riverbed, the debit with an overflow possibility). The hydraulic calculations performed on the 11 transverse profiles, conducted using the GIS terrain model, led to the high accuracy defining of the flood-prone areas corresponding to the flows in the major riverbed with probabilities of 1 and 5 %. Also highlighted on the 2D and 3D images were several civilian areas at risk in the case of extreme hydrological events, related to maximum flow.

Extraction of digital elevation model using stereo matching with slope-adaptive patch transformation

Traditional automated DEM generation from high-resolution satellite imagery relies on area-based correlation matching methods. However, these methods often fail, especially where the slope of the terrain is very high; this results in large geometric difference in the stereo pair. To address this issue, we introduced an adaptive stereo matching scheme that incorporates image transformation for the slope in the epipolar image space. The matching patch is designed to adaptively transform the target image according to the terrain slope determined in the lower scale of the image pyramid. This transformation reduces the geometric difference between the stereoscopic images due to the image acquisition angle and the terrain slope. Experimental results with Kompsat-2 images showed that the proposed method increased the overall matching success rate by 5% and the vertical accuracy of the generated DEM improved by about 2 meters (17%).