The Selection of Ground Control Points in a Remote Sensing Image Correction Based on Weighted Voronoi Diagram

2009 ◽  
Author(s):  
Zhang Wangfei ◽  
Gao Jianguo ◽  
Xu Tianshu ◽  
Huang Yanru
Keyword(s):  
Remote Sensing ◽  
Voronoi Diagram ◽  
Remote Sensing Image ◽  
Ground Control ◽  
Control Points ◽  
Image Correction ◽  
Ground Control Points ◽  
Selection Of

scholarly journals Semi-Automatic Selection of Ground Control Points for High Resolution Remote Sensing Data in Urban Areas

2016 ◽  
Vol 20 (1) ◽  
pp. 21-26
Author(s):  
Linda Gulbe ◽  
Gundars Korāts
Keyword(s):  
Remote Sensing ◽  
Urban Areas ◽  
Remote Sensing Data ◽  
Ground Control ◽  
Test Case ◽  
Control Points ◽  
Sensing Data ◽  
Ground Control Points ◽  
Automatic Selection ◽  
Selection Of

Abstract Geometrical accuracy of remote sensing data often is ensured by geometrical transforms based on Ground Control Points (GCPs). Manual selection of GCP is a time-consuming process, which requires some sort of automation. Therefore, the aim of this study is to present and evaluate methodology for easier, semi-automatic selection of ground control points for urban areas. Custom line scanning algorithm was implemented and applied to data in order to extract potential GCPs for an image analyst. The proposed method was tested for classical orthorectification and special object polygon transform. Results are convincing and show that in the test case semi-automatic methodology is able to correct locations of 70 % (thermal data) – 80 % (orthophoto images) of buildings. Geometrical transform for subimages of approximately 3 hectares with approximately 12 automatically found GCPs resulted in RSME approximately 1 meter with standard deviation of 1.2 meters.

Control Point Extraction in the Remote Sensing Image via Adaptive Filter

2013 ◽  
Vol 411-414 ◽  
pp. 1267-1276
Author(s):  
Lei Lei Geng ◽  
De Shen Xia ◽  
Quan Sen Sun ◽  
Kai Yuan
Keyword(s):  
Remote Sensing ◽  
Nearest Neighbor ◽  
Rapid Development ◽  
Control Point ◽  
Remote Sensing Image ◽  
Ground Control ◽  
Reference Image ◽  
Control Points ◽  
Ground Control Points ◽  
Geometric Precision

With the rapid development of the remote sensing satellite, the size and resolution of remote sensing image grow increasingly. The evaluation of image quality requires precise information of ground control points extracted from remote sensing image and reference image. Therefore, we propose an adaptive Wallis enhancement based on radiation-parameters to increase the number of ground control points and to improve the matching precision. First, feature vectors of sub-region are constructed by computing image radiation-parameters, and then the sub-region terrain in the remote sensing image can be recognized using nearest neighbor classifier. Second, according to specific type of sub-region terrain, we enhance images using adaptive Wallis filter with local parameters. Finally, two-level matching method is used to extract and match the control points. The experiments show that compared with existing Wallis filter which are based on global parameters, our method gets better results in the detail enhancement on ZY-3 image so that more and higher accurate ground control points can be effectively extracted to achieve the evaluation of geometric precision automatically and accurately.

scholarly journals Geometric Accuracy Study of Orthorectification Based on Sensor Model Refinement in Imagery Subset Using ORFEO Toolbox (OTB)

2019 ◽  
Vol 2 (1) ◽  
Author(s):  
Andri Suprayogi ◽  
Nurhadi Bashit
Keyword(s):  
Remote Sensing ◽  
Satellite Imagery ◽  
Ground Control ◽  
Surface Model ◽  
Control Points ◽  
Geometric Accuracy ◽  
Model Refinement ◽  
Full Extent ◽  
Sensor Model ◽  
Ground Control Points

Large scale base map can be obtained by various methods, one of them is orthorectification process of remote sensing satellite imagery to eliminate the relief displacement caused by height variation of earth surface. To obtain a  map images with good quality,  it requires additional data such as sensor model in the form of rational polynomial coefficients (RPC), surface model data, and ground control points Satellite imageries with high resolution  file size are relatively large.  In order to process them,  high specification of hardwares were required. To overcome this by cutting only a portion of the images, based on certain study areas were suffer from of georeference lost so it would not be able to orthorectified. On the other hand,  in several remote sensing software such as ESA SNAP and Orfeo Toolbox (OTB)  subset or pixel extraction from satellite imagery,  preserve the imagery geometric sensor models. This research aimed at geometric accuracy of orthorectification carried out in a single scene of Pleiades Imagery within the Kepahiang Subdistrict, located at Kepahiang Regency, Bengkulu Province, by using DEMNAS and the imagery refined sensor mode, and ground control points taken using GPS Survey. Related with the raw imagery condition which consists of Panchromatic and multispectral bands, this study were separated to assembly, pan sharpening , and sensor model refinement stages prior to orthorectification carried out both in the original or full extent imagery and the result of subset extent imagery. After  these processses taken place, we measure the accuracy of each full and subset imagery.These procedures were carried out using Orfeo toolbox 6.6.0 in the Linux Mint 19 Operating system. From the process log, running time in total  were 7814.518  second for the full extent and 4321.95 seconds for the subset processess. And as a big data process, the total of full extent imageries was 83.15 GB  while the subset size  was  only 30.73 GB.  The relative accuracy of the full extent and its subset imagery were 0.431 meters. Accuracy of the  sensor model refinement process are  1.217 meters and 1.550 meters with GCP added, while the accuracu of  the orthorectifications results were  0.416 meters and 0.751 meters by using ICP.  Variation of execution time may caused by the data input size and complexity of the mathematical process carried out in each stages. Meanwhile,  the variation of accuracy may  caused by the check or control points placements above satellite Imagery which suffer from uncertainty when dealing with  the sub-pixel position or under 0.5 meters.

Application of Postprocessing Kinematic Methods with UAS Remote Sensing in Forest Ecosystems

2021 ◽  
Author(s):  
Zachary M Miller ◽  
Joseph Hupy ◽  
Aishwarya Chandrasekaran ◽  
Guofan Shao ◽  
Songlin Fei
Keyword(s):  
Remote Sensing ◽  
Data Collection ◽  
Ground Control ◽  
Unmanned Aerial Systems ◽  
Control Points ◽  
Gps Technology ◽  
Ground Control Points ◽  
Forested Areas ◽  
Rapid Deployment ◽  
Aerial Systems

Abstract Unmanned Aerial Systems (UAS) serve as an excellent remote-sensing platform to fulfill an aerial imagery data collection niche previously unattainable in forestry by satellites and manned aircraft. However, for UAS-derived data to be spatially representative, a precise network of ground control points (GCP) is often required, which can be tedious and limit the logistical benefits of UAS rapid deployment capabilities, especially in densely forested areas. Therefore, methods for efficient data collection without GCPs are highly desired in UAS remote sensing. Here, we demonstrate the use of postprocessing kinematic (PPK) technology to obtain subcentimeter precision in datasets of forested areas without the need for placing GCPs. We evaluated two key measures, positional variability and time efficiency, of the PPK technology by comparing them to traditional GCP methods. Results show that PPK displays consistently higher positional precision than traditional GCP approaches. Moreover, PPK surveys and processing take less time to complete than traditional GCP methods and require fewer logistical steps, especially in image acquisition. The time and resource savings with PPK as compared to GCP processing are undeniable. We conclude that PPK technology provides a practical means to produce precise aerial forest surveys. Study Implications Unmanned Aerial Systems (UAS) have enormous potential for lowering costs and streamlining practices in the forestry management and research community. Despite this potential, however, UAS forestry applications have been limited in scope and precision because of a reliance on using ground-based GPS technology to survey ground control points (GCP), which are time intensive and require an open view of the sky. Such a need for a ground-based GCP survey, along with forest canopy serving to limit and scatter incoming GPS signals, diminishes the potential for rapid deployment and precision mapping offered by UAS. Fortunately, Postprocessing-Kinematic (PPK) GPS technology lowers these barriers by providing the means to seamlessly gather highly precise UAS imagery without needing to conduct time-intensive ground-based surveys. This study compares the precision and time-effectiveness between traditional GCP marker surveys and PPK correction methods.

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