scholarly journals Ground Control Point Generation from Simulated SAR Image Derived from Digital Terrain Model and its Application to Texture Feature Extraction

Author(s):  
Kohei Arai
2021 ◽  
Vol 17 (1) ◽  
pp. 39-48
Author(s):  
Hariady Indra Mantong

Utilization of The Unmanned Aerial Vehicle (UAV) or Drone has brought revolution in digital photogrammetry. The feature matching on surface reconstruction or Digital Surface Model (DSM) are quickly finished. However, DSM doesn’t represent itself as a part of topography, that is why DSM should be converted into Digital Terrain Model (DTM). This research is to investigate the accuracy of UAV photogrammetry’s DTM  for hydraulic modeling purpose. This study has produced 4 sets of DTMs; 2 sets of DTMs with different grid resolution which are 2 cm & 40 cm, also the 2 other sets of DTM with extra fine nature algorithm and set of filtering parameters adjustment; bulge, offset, spike and standard deviation. Every DTM are validated by Ground Control Point (GCP) from Real Time Kinematic-Different Global Positioning System (RTK-DGPS) measurement. According to the validation, the adjustment of filtering parameters is the most accurate method with Root Mean Square Error (RMSE) of 6,17 cm for 2 cm resolution; and 5,22 cm for 40 cm resolution. Next, DTM UAV is used to estimate the flood water level from Synthetic Aperture Radar (SAR) Image detection with 46 flood images on Glane and Losser area, east part of Overijssel, The Netherlands, since October 2014 to December 2017, then validated with the insitu water level measurement and resulted RMSE 6,72 cm for set of UAV DTM’s 40 cm resolution with the filtering parameters adjustment. Therefore, this DTM UAV can be used as a topography parameter in hydraulic modeling, especially at the similar flat-surface terrain where this research have been conducted.  Keywords: UAV photogrammetry, SAR detection, DTM production


2020 ◽  
Author(s):  
Sebastian Flöry ◽  
Camillo Ressl ◽  
Gerhard Puercher ◽  
Norbert Pfeifer ◽  
Markus Hollaus ◽  
...  

<p>Mountain regions are disproportionately affected by global warming and changing precipitation conditions. Especially the strong variations within high mountain ranges at the local scale require additional sources in order to quantify changes within this challenging environment. With the emergence of alpine tourism, terrestrial photographs became available by the end of 1800, predating aerial imagery for the selected study areas by 50 years. Due to the earlier availability and oblique acquisition geometry these images are a promising source for quantifying changes within mountainous regions at the local scale. Within the research project SEHAG, methods to process these images and to analyse their potential to quantify and describe environmental changes are developed and applied to study areas in Austria and Italy.</p><p>One of the prerequisites for the estimation of changes based on terrestrial imagery is the calculation of the corresponding object point for each pixel in a global coordinate system resulting in a georeferenced orthorectified image. This can be achieved by intersecting the ray defined by the projection center of the camera and each pixel with a digital terrain model, a process known as monoplotting.</p><p>So far 1000 terrestrial images with unknown interior and exterior orientation have been collected from various archives for the selected study areas Kaunertal, Horlachtal (both Tyrol, Austria) and Martelltal (South Tyrol, Italy). In order to estimate all camera parameters a 3D viewer for the selection of ground control points has been developed and implemented. The estimation of the exterior and interior orientation is done in OrientAL. </p><p>Preliminary results for selected images show, that especially the developed 3D viewer is an important improvement for the selection of well distributed ground control points and the accurate estimation of the exterior and interior orientation. Monoplotting depends on a digital terrain model, which cannot be computed from the terrestrial images alone due to missing overlap and different acquisitions times. Hence, the combination with historical digital terrain models derived from aerial imagery is necessary to minimize errors introduced due to changes in topography until today. While the large amount of terrestrial images with their oblique acquisition geometries can be exploited to fill occluded areas by combining the results from multiple images, the partly missing or inaccurate temporal information poses another limitation.</p><p>With this large image collection, for the first time, we are able to evaluate the use of historical oblique terrestrial photographs for change detection in a systematic manner. This will promote knowledge about challenges, limitations and the achievable accuracy of monoplotting within mountainous regions. The work is part of the SEHAG project (project number I 4062) funded by the Austrian Science Fund (FWF).</p>


2019 ◽  
Vol 632 ◽  
pp. L4 ◽  
Author(s):  
F. Preusker ◽  
F. Scholten ◽  
S. Elgner ◽  
K.-D. Matz ◽  
S. Kameda ◽  
...  

A high-resolution 3D surface model, map-projected to a digital terrain model (DTM), and precisely ortho-rectified context images (orthoimages) of MASCOT landing site area are important data sets for the scientific analysis of relevant data that have been acquired with MASCOT’s image camera system MASCam and other instruments (e.g., the radiometer MARA and the magnetometer MASMag). We performed a stereo-photogrammetric (SPG) analysis of 1050 images acquired from the Hayabusa2 Optical Navigation Camera system (ONC) during the asteroid characterization phase and the MASCOT release phase in early October 2018 to construct a photogrammetric control point network of asteroid (162173) Ryugu. We validated existing rotational parameters for Ryugu and improved the camera orientation (position and pointing) of the ONC images to decimeter accuracy using SPG bundle block adjustment. We produced a high-resolution DTM of the entire MASCOT landing site area. Finally, based on this DTM, a set of orthoimages from the highest-resolution ONC images around MASCOT’s final rest position complements the results of this analysis.


2018 ◽  
Vol 2 ◽  
pp. 535
Author(s):  
Maundri Prihanggo

<p>Saat ini, citra satelit resolusi sangat tinggi digunakan dalam berbagai macam aplikasi, terutama pemetaan skala besar. Sebelum dapat digunakan, citra satelit tersebut harus diorthorektifikasi terlebih dahulu. Data <em>Digital Surface Model </em>(DSM) dan <em>Ground Control Point</em> (GCP) adalah dua data utama yang diperlukan saat melakukan orthorektifikasi. Perbedaan data DSM yang digunakan akan menghasilkan perbedaan nilai ketelitian horizontal pada kedua citra tegak hasil orthorektifikasi. Pada penelitian ini digunakan dua jenis DSM yaitu SRTM dan Terrasar-X. Ketelitian vertikal dari SRTM adalah 90 m sedangkan ketelitian vertikal dari Terrasar-X adalah 12,5 m. Penelitian ini berlokasi di Wilayah Buli, Kabupaten Halmahera Timur, Provinsi Maluku. Terdapat tiga sensor citra satelit yang digunakan yaitu Pleiades, Quickbird dan Worldview-2 yang digunakan pada lokasi penelitian. Total GCP yang digunakan adalah 33 titik, tiap titiknya diukur dengan melakukan pengamatan geodetik dan memiliki ketelitian horizontal ≤15 cm dan ketelitian vertikal ≤30 cm. Ketelitian horizontal dari citra tegak satelit resolusi sangat tinggi diperoleh dengan melakukan uji terhadap Independent Check Point (ICP). Total ICP yang digunakan adalah 12 titik, tiap titik ICP diukur dengan metode dan standar yang sama dengan titik GCP. Ketelitian horizontal dengan Circular Error (CE 90) dari citra tegak satelit menggunakan data SRTM adalah 18,856 m sedangkan ketelitian horizontal dengan Circular Error (CE 90) dari citra tegak satelit menggunakan data Terrasar-X adalah 2.168 m . Hasil dari penelitian ini membuktikan bahwa ketelitian vertikal data DSM yang digunakan memberikan pengaruh pada citra tegak satelit hasil orthorektifikasi tersebut. Mengacu pada Peraturan Kepala BIG nomor 15 tahun 2014, citra tegak satelit hasil orthorektifikasi menggunakan data Terrasar-X sebagai DSM memenuhi ketelitian horizontal peta dasar kelas 3 skala 1:5.000 sedangkan citra tegak satelit hasil orthorektifikasi menggunakan data SRTM sebagai DSM tidak dapat memenuhi ketelitian horizontal peta dasar skala besar.</p><p><strong>Kata kunci:</strong> orthorektifikasi, DSM, ketelitian horizontal</p>


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