• Proceedings of the KSRS Conference
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• 2001.03a
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• pp.140-148
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• 2001

The satellite sensor model is typically established using ground control points acquired by ground survey Of existing topographic maps. In some cases where the targeted area can't be accessed and the topographic maps are not available, it is difficult to obtain ground control points so that geospatial information could not be obtained from satellite image. The paper presents several satellite sensor models and satellite image decomposition methods for non-accessible area where ground control points can hardly acquired in conventional ways. First, 10 different satellite sensor models, which were extended from collinearity condition equations, were developed and then the behavior of each sensor model was investigated. Secondly, satellite images were decomposed and also pseudo images were generated. The satellite sensor model extended from collinearity equations was represented by the six exterior orientation parameters in 1$^{st}$, 2$^{nd}$ and 3$^{rd}$ order function of satellite image row. Among them, the rotational angle parameters such as $\omega$(omega) and $\phi$(phi) correlated highly with positional parameters could be assigned to constant values. For non-accessible area, satellite images were decomposed, which means that two consecutive images were combined as one image. The combined image consists of one satellite image with ground control points and the other without ground control points. In addition, a pseudo image which is an imaginary image, was prepared from one satellite image with ground control points and the other without ground control points. In other words, the pseudo image is an arbitrary image bridging two consecutive images. For the experiments, SPOT satellite images exposed to the similar area in different pass were used. Conclusively, it was found that 10 different satellite sensor models and 5 different decomposed methods delivered different levels of accuracy. Among them, the satellite camera model with 1$^{st}$ order function of image row for positional orientation parameters and rotational angle parameter of kappa, and constant rotational angle parameter omega and phi provided the best 60m maximum error at check point with pseudo images arrangement.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.32 no.1
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• pp.39-47
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• 2014

In this study, the epipolar images were created by both methods of resolution adjustment and piecewise approach using RPC(Rational Polynomial coefficients) and ancillary data of IKONOS-2 and SPOT-5 satellite images whose resolutions are different from each other. The stereo geometry of these two satellite images was analyzed and the RPC block modelling was accomplished for generating epipolar images. In order to evaluate the accuracy of created epipolar images, the y-parallaxes were analyzed for the specific points which were apparently identified in mountainous, plain and urban area. Also the RMSEs of the specific points were calculated using the coordinates from the epipolar stereo images and the coordinates from the block triangulation. Y-parallaxes were within one pixel and the RMSEs were within two meters for X, Y and Z each.

• Journal of Wetlands Research
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• v.16 no.3
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• pp.393-401
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• 2014

The purpose of this study was to investigate the objective impact in accuracy and reliability with tendency depend on training samples by using the high-resolution images. Supervised classification was performed based on multi-spectral images which made by each satellite and aerial images for considering all of bands' characteristics. The highest accuracy was 84.7% with satellite image(3*3) and 83% with aerial image(5*5) at the accuracy verification phase. Also, the overall accuracy with the consideration of Kappa coefficient were 0.84 for satellite images and 0.82 for aerial images. In all of the images, the smaller training sample was, the higher accuracy showed. Therefore, tree species classification accuracy was tended to rely on training sample size.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.34 no.5
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• pp.471-478
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• 2016

This paper proposes the results of analysis of correlation between satellite geometry elements for an effective use of satellite images. To achieve accurate positional information, stereo images have normal range of convergence and BIE (BIsector Elevation) angles which are greatly influenced by azimuth and elevation angle of individual image. In this paper, the variations of convergence and BIE angles are estimated according to azimuth angle differences between two images and each elevation angle. The analysis provided strong support for predicting stereo geometry without complex analysis of epiploar geometry or mathematics. The experiment results showed that more than 150°, 130°, and 100° azimuth angle differences need to be constructed when elevation angle of two images is 50°, 60°, and 70°, respectively, in order to make the convergence and BIE angle within normal range. The results are expected to be fully used for various application using stereo images.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.22 no.1
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• pp.1-9
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• 2004

Until comparatively lately the use of vector data is the main current in GIS fields. Vector data have many advantages such as easy of scale change, small data volume and so on. But it also has some weak point that can't apply real world. The other hand the satellite images have strong point that can apply real world actually. Recently rapid progress of computer capacity and processing velocity is the current trend of the times. Therefore in this study I intend to develop GIS application based on satellite images with vector data and raster data altogether. This system is developed by using Visual C＋＋ in the personal computer and MS Access's MDB far database management. The LANDSAT, KOMPSAT and IKONOS satellite image database were used in this study. Also, we are studied to set up scale for screen display according to spatial resolution.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.29 no.5
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• pp.519-525
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• 2011

High-resolution earth-observing satellites acquire substantial amount of geospatial images. In addition to high image quality, high-resolution satellite images (HRSI) provide unprecedented direct georegistration accuracy, which have been enabled by accurate orbit determination technology. Direct georegistration is carried out by relating the determined position and attitude of camera to the ground target, i.e., projecting an image point to the earth ellipsoid using the collinearity equation. However, the apparent position of ground target is displaced due to the atmosphere and satellite velocity causing significant georegistration bias. In other words, optic ray from the earth surface to satellite cameras at 400~900km altitude refracts due to the thick atmosphere which is called atmospheric refraction. Velocity aberration is caused by high traveling speed of earth-observing satellites, approximately 7.7 km/s, relative to the earth surface. These effects should be compensated for accurate direct georegistration of HRSI. Therefore, this study presents the equation and the compensation procedure of atmospheric refraction and velocity aberration. Then, the effects are simulated at different image acquisition geometry to present how much bias is introduced. Finally, these effects are evaluated for Quickbird and WorldView-1 based on the physical sensor model.

• Journal of Korean Society for Geospatial Information Science
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• v.11 no.2 s.25
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• pp.61-66
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• 2003

The change detection using the satellite imagery and airphotos has been carried out in the application of terrain mapping, environment, forestry, facility detection, etc. The low-spatial resolution data such as Landsat, NOAA satellite images is generally used for automatic change detection, while on the other hand the high-spatial resolution data is used for change detection by image interpretation. The research to integrate automatic method with manual change detection through the high-spatial resolution satellite image is performed. but the problem such as shadow, building 'lean' due to perspective geometry and precision geocorrection was found. In this paper we performed change detection using the IKONOS satellite images, and present the concerning problem.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.1162-1164
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• 2003

The methods of the geometric reconstruction and sensor calibration of satellite linear pushbroom images are investigated. The model of the sensor used is based on the SPOT model that is developed by Kraiky. The satellite trajectory is a Keplerian trajectory in the approximation. Four orbit parameters, longitude of the ascending node(${\omega}$), inclination of the orbit plan(I), latitude argument of the satellite(W) and distance between earth center and satellite, are used for the camera modeling. Time-dependent orbit parameters are expressed by quadratic polynomials. SPOT-5 images have been used for validation tests. The results are that the RMSE acquired from 20 GCPs is 1.763m and the RMSE of 5 checking points 2.470m. Because the ground resolution of SPOT-5 is 2.5m, the result obtained in this study has a good accuracy. It demonstrates that the sensor model developed by this study can be used to reconstruct the geometry of satellite image using pushbroom camera.

• Advances in aircraft and spacecraft science
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• v.10 no.1
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• pp.67-82
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• 2023

Linear array imaging sensors are widely used in remote sensing satellites. The final products of an imaging sensor can only be used when they are geometrically, radiometrically, and spectrally calibrated. Therefore, at the first stages of sensor design, a detailed calibration procedure must be carefully planned based on the accuracy requirements. In this paper, focusing on inherent optical distortion, a step-by-step procedure for laboratory geometric calibration of a typical push-broom satellite imaging sensor is simulated. The basis of this work is the simulation of a laboratory procedure in which a linear imager mounted on a rotary table captures images of a pin-hole pattern at different angles. By these images and their corresponding pinhole approximation, the correction function is extracted and applied to the raw images to give the corrected ones. The simulation results illustrate that using this approach, the nonlinear effects of distortion can be minimized and therefore the accuracy of the geometric position of this method on the image screen can be improved to better than the order of sub-pixel. On the other hand, the analyses can be used to proper laboratory facility selection based on the imaging sensor specifications and the accuracy.

• Korean Journal of Remote Sensing
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• v.38 no.5_1
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• pp.523-533
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• 2022

This paper presents a two-stage spatio-spectral fusion method (2SSFM) based on area-to-point regression kriging (ATPRK) to enhance spatial and spectral resolutions using multi-sensor satellite images with complementary spatial and spectral resolutions. 2SSFM combines ATPRK and random forest regression to predict spectral bands at high spatial resolution from multi-sensor satellite images. In the first stage, ATPRK-based spatial down scaling is performed to reduce the differences in spatial resolution between multi-sensor satellite images. In the second stage, regression modeling using random forest is then applied to quantify the relationship of spectral bands between multi-sensor satellite images. The prediction performance of 2SSFM was evaluated through a case study of the generation of red-edge and short-wave infrared bands. The red-edge and short-wave infrared bands of PlanetScope images were predicted from Sentinel-2 images using 2SSFM. From the case study, 2SSFM could generate red-edge and short-wave infrared bands with improved spatial resolution and similar spectral patterns to the actual spectral bands, which confirms the feasibility of 2SSFM for the generation of spectral bands not provided in high spatial resolution satellite images. Thus, 2SSFM can be applied to generate various spectral indices using the predicted spectral bands that are actually unavailable but effective for environmental monitoring.