scholarly journals A Rational Function Model Based Geo-Positioning Method for Satellite Images without Using Ground Control Points

2018 ◽  
Vol 10 (2) ◽  
pp. 182 ◽  
Author(s):  
Zhenling Ma ◽  
Wei Song ◽  
Junping Deng ◽  
Jiali Wang ◽  
Cancan Cui
Keyword(s):  
Rational Function ◽  
Satellite Images ◽  
Ground Control ◽  
Control Points ◽  
Function Model ◽  
Model Based ◽  
Ground Control Points ◽  
Positioning Method

scholarly journals Correction of Very High-Resolution Satellite Images using Control Points Captured by Web Map Service (WMS) server: Google Earth

2020 ◽  
Vol 9 (8) ◽  
pp. 188-194
Keyword(s):  
Large Scale ◽  
Satellite Images ◽  
Google Earth ◽  
Ground Control ◽  
Control Points ◽  
Web Map ◽  
Ground Control Points ◽  
High Resolution Satellite Images ◽  
Map Scale ◽  
Map Service

The recent progress for spatial resolution of remote sensing imagery led to generate many types of Very HighResolution (VHR) satellite images, consequently, general speaking, it is possible to prepare accurate base map larger than 1:10,000 scale. One of these VHR satellite image is WorldView-3 sensor that launched in August 2014. The resolution of 0.31m makes WorldView-3 the highest resolution commercial satellite in the world. In the current research, a pan-sharpen image from that type, covering an area at Giza Governorate in Egypt, used to determine the suitable large-scale map that could be produced from that image. To reach this objective, two different sources for acquiring Ground Control Points (GCPs). Firstly, very accurate field measurements using GPS and secondly, Web Map Service (WMS) server (in the current research is Google Earth) which is considered a good alternative when GCPs are not available, are used. Accordingly, three scenarios are tested, using the same set of both 16 Ground Control Points (GCPs) as well as 14 Check Points (CHKs), used for evaluation the accuracy of geometric correction of that type of images. First approach using both GCPs and CHKs coordinates acquired by GPS. Second approach using GCPs coordinates acquired by Google Earth and CHKs acquired by GPS. Third approach using GCPs and CHKs coordinates by Google Earth. Results showed that, first approach gives Root Mean Square Error (RMSE) planimeteric discrepancy for GCPs of 0.45m and RMSE planimeteric discrepancy for CHKs of 0.69m. Second approach gives RMSE for GCPs of 1.10m and RMSE for CHKs of 1.75m. Third approach gives RMSE for GCPs of 1.10m and RMSE for CHKs of 1.40m. Taking map accuracy specification of 0.5mm of map scale, the worst values for CHKs points (1.75m&1,4m) resulted from using Google Earth as a source, gives the possibility of producing 1:5000 large-scale map compared with the best value of (0.69m) (map scale 1:2500). This means, for the given parameters of the current research, large scale maps could be produced using Google Earth, in case of GCPs are not available accurately from the field surveying, which is very useful for many users.

scholarly journals DESARROLLO DE UNA METODOLOGÍA SENCILLA PARA LA GEORREFERENCIACIÓN Y MEDICIÓN DE DISTANCIAS A PARTIR DE IMÁGENES DE SATÉLITE SISTEMÁTICAMENTE GEORREFERENCIADAS

2016 ◽  
Vol 39 (1) ◽  
Author(s):  
Leonardo Gónima ◽  
Libardo E. Ruiz ◽  
Marcos E. González
Keyword(s):  
Satellite Images ◽  
Control Point ◽  
Ground Control ◽  
Gps Data ◽  
Control Points ◽  
Statistical Validation ◽  
Distance Measurements ◽  
The North ◽  
Ground Control Points ◽  
Representative Points

One of the main problems for a precise georeferencing and distance measurements from satellite images, especially in geographical zones with strong morphologic and environmental dynamics, lies not only in the difficulty for identifying ground control points (GCPs), but also in real limitations for accessing such places. In this work a relatively simple methodology is proposed for georeferencing and distance measuring from satellite images, based on the utilization of previously calculated reflectance images from the surface and then oriented toward the north (systematic georeferencing). From these images and setting a basic control point (pixel) P, measured with GPS, the other GCPs were obtained by measurements of distances defined from the P point to representative points (pixels) on the image, selected for its georeferencing. The statistical validation of the obtained results, using a different sample of GCPs measured with GPS, shows that the precision of the georeferencing and distance measurement utilizing the developed methodology is similar to that obtained by conventional procedures, such as image georeferencing from GPS data.

scholarly journals A Novel Method for Fast Positioning of Non-Standardized Ground Control Points in Drone Images

2021 ◽  
Vol 13 (15) ◽  
pp. 2849
Author(s):  
Zheng Zhu ◽  
Tengfei Bao ◽  
Yuhan Hu ◽  
Jian Gong
Keyword(s):  
Road Traffic ◽  
Regions Of Interest ◽  
Ground Control ◽  
Control Points ◽  
Accuracy Rate ◽  
Ground Control Points ◽  
Positioning Method ◽  
Urban Scene ◽  
Novel Method

Positioning the pixels of ground control points (GCPs) in drone images is an issue of great concern in the field of drone photogrammetry. The current mainstream automatic approaches are based on standardized markers, such as circular coded targets and point coded targets. There is no denying that introducing standardized markers improves the efficiency of positioning GCP pixels. However, the low flexibility leads to some drawbacks, such as the heavy logistical input in placing and maintaining GCP markers. Especially as drone photogrammetry steps into the era of large scenes, the logistical input in maintaining GCP markers becomes much more costly. This paper proposes a novel positioning method applicable for non-standardized GCPs. Firstly, regions of interest (ROIs) are extracted from drone images with stereovision technologies. Secondly, the quality of ROIs is evaluated using image entropy, and then the outliers are filtered by an adjusted boxplot. Thirdly, pixels of interest are searched with a corner detector, and the precise imagery coordinates are obtained by subpixel optimization. Finally, the verification was carried out in an urban scene, and the results show that this method has good applicability to the GCPs on road traffic signs, and the accuracy rate is over 95%.

scholarly journals Improving HySpex Sensor Co-registration Accuracy using BRISK and Sensor-model based RANSAC

2014 ◽  
Vol XL-1 ◽  
pp. 371-376 ◽  
Author(s):  
P. Schwind ◽  
M. Schneider ◽  
R. Müller
Keyword(s):  
Test Data ◽  
Feature Detection ◽  
Ground Control ◽  
Control Points ◽  
Feature Detector ◽  
Registration Accuracy ◽  
Data Set ◽  
Model Based ◽  
Sensor Model ◽  
Ground Control Points

In this paper a method to improve the co-registration accuracy of two separate HySpex SWIR and VNIR cameras is proposed. The first step of the presented approach deals with the detection of point features from both scenes using the BRISK feature detector. After matching these features, the match coordinates in the VNIR scene are orthorectified and the resulting ground control points in the SWIR scene are filtered using a sensor-model based RANSAC. This implementation of RANSAC estimates the boresight angles of a scene by iteratively fitting the sensor-model to a subset of the matches. The boresight angles which can be applied to most of the remaining matches are then used to orthorectify the scene. Compared to previously used methods, the main advantages of this approach are the high robustness against outliers and the reduced runtime. The proposed methodology was evaluated using a test data set and it is shown in this work that the use of BRISK for feature detection followed by sensor-model based RANSAC significantly improves the co-registration accuracy of the imagery produced by the two HySpex sensors.

scholarly journals A New Method for Positional Accuracy Analysis in Georeferenced Satellite Images without Independent Ground Control Points

2020 ◽  
Vol 12 (24) ◽  
pp. 4132
Author(s):  
Miguel Sánchez ◽  
Aurora Cuartero ◽  
Manuel Barrena ◽  
Antonio Plaza
Keyword(s):  
Spatial Data ◽  
Traditional Method ◽  
Satellite Images ◽  
New Method ◽  
Ground Control ◽  
Landsat 8 ◽  
Control Points ◽  
Positional Accuracy ◽  
National Geographic ◽  
Ground Control Points

This paper introduces a new method to analyze the positional accuracy of georeferenced satellite images without the use of ground control points. Compared to the traditional method used to carry out this kind of analysis, our approach provides a semiautomatic way to obtain a larger number of control points that satisfy the requirements of current standards regarding the size of the set of sample points, the positional accuracy of such points, the distance between points, and the distribution of points in the sample. Our methodology exploits high quality orthoimages, such as those provided by the Aerial Orthography National Plan (PNOA)—developed by the Spanish National Geographic Institute—and has been tested on spatial data from Landsat 8. Our method works under the current international standard (ASPRS 2014) and exhibits similar performance than other well-known methods to analyze the positional accuracy of georeferenced images based on the use of independent ground control points. More specifically, the positional accuracy achieved for a Landsat 8 dataset evaluated by the traditional method is 5.22 ± 1.95 m, and when evaluated with the proposed method, it exhibits a typical accuracy of 5.76 ± 0.50 m. Our experimental results confirm that the method is equally effective and less expensive than other available methods to analyze the positional accuracy of satellite images.

Sign in / Sign up

Export Citation Format

Share Document