• Proceedings of the KSRS Conference
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• 2002.10a
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• pp.553-558
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• 2002

Physical sensor model in pushbroom satellite images can be made from sensor modeling by rotation parameters and attitude parameters on the satellite track. These parameters are determined by the information obtained from GPS, INS, or star tracker. Provided from satellite image, an auxiliary data error is connected directly with an error of rotation parameters and attitude parameters. This paper analyzed how obtaining satellite images influenced errors of rotation parameters and attitude parameters. furthermore, for detailed analysis, this paper generated simulated satellite image, which was changed variously by rotation parameters and attitude parameters of satellite sensor model. Simulated satellite image is generated by using high-resolution digital aerial image and DEM (Digital Elevation Model) data. Moreover, this paper determined correlation of rotation parameter and attitude parameters through error analysis of simulated satellite image that was generated by various rotation parameters and attitude parameters.

• Korean Journal of Remote Sensing
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• v.18 no.6
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• pp.327-336
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• 2002

Ever since high resolution satellites were launched, high-resolution satellite images have been utilized in many areas. This paper proposed methods of generating simulated satellite image using DEM(Digital Elevation Model) and digital image such as aerial photograph. There are two methods proposed in the paper: one is Direct-Indirect method and the other Indirect-Indirect, method. It is assumed that satellite attitude is not changing and perspective center is moving in the direction of flight while image is captured. The proposed methods were implemented with aerial photograph, DEM data, arbitrary orbit parameters and attitude parameters of high resolution satellite image under generation. Furthermore, for the stereo viewing, different orientation parameters and perspective center were tested for generating simulated satellite image. In addition, the quality and accuracy of the simulated satellite image generated by the proposed methods were analyzed.

• Aerospace Engineering and Technology
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• v.6 no.1
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• pp.97-102
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• 2007

Satellite performance can be evaluated by satellite product. When satellite development technology is in developing, most of efforts focus on success of satellite operation and safety. But, more and more efforts are focused on satellite performance and mission success. Especially quality of the image which is delivered to user is very important factor. In this paper, generating method for simulated image in the point of MTF is described.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.34 no.2
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• pp.121-132
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• 2016

This study proposes an approach for simulating high spatial resolution satellite images acquired under arbitrary sun-sensor geometry using existing images and 3D (three-dimensional) data. First, satellite images, having significant differences in spectral regions compared with those in the simulated image were transformed to the same spectral regions as those in simulated image by using the UPDM (Universal Pattern Decomposition Method). Simultaneously, shadows cast by buildings or high features under the new sun position were modeled. Then, pixels that changed from shadow into non-shadow areas and vice versa were simulated on the basis of existing images. Finally, buildings that were viewed under the new sensor position were modeled on the basis of open library-based 3D reconstruction program. An experiment was conducted to simulate WV-3 (WorldView-3) images acquired under two different sun-sensor geometries based on a Pleiades 1A image, an additional WV-3 image, a Landsat image, and 3D building models. The results show that the shapes of the buildings were modeled effectively, although some problems were noted in the simulation of pixels changing from shadows cast by buildings into non-shadow. Additionally, the mean reflectance of the simulated image was quite similar to that of actual images in vegetation and water areas. However, significant gaps between the mean reflectance of simulated and actual images in soil and road areas were noted, which could be attributed to differences in the moisture content.

• Korean Journal of Remote Sensing
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• v.38 no.1
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• pp.33-43
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• 2022

This study presents a method to restore an optical satellite image with distortion and occlusion due to fog, haze, and clouds to one that minimizes degradation factors by referring to the same type of peripheral image. Specifically, the time and cost of re-photographing were reduced by partially occluding a region. To maintain the original image's pixel value as much as possible and to maintain restored and unrestored area continuity, a simulation restoration technique modified with the Cycle Generative Adversarial Network (CycleGAN) method was developed. The accuracy of the simulated image was analyzed by comparing CycleGAN and histogram matching, as well as the pixel value distribution, with the original image. The results show that for Site 1 (out of three sites), the root mean square error and R² of CycleGAN were 169.36 and 0.9917, respectively, showing lower errors than those for histogram matching (170.43 and 0.9896, respectively). Further, comparison of the mean and standard deviation values of images simulated by CycleGAN and histogram matching with the ground truth pixel values confirmed the CycleGAN methodology as being closer to the ground truth value. Even for the histogram distribution of the simulated images, CycleGAN was closer to the ground truth than histogram matching.

• Proceedings of the KSRS Conference
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• 1999.11a
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• pp.349-354
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• 1999

Electro-Optical Camera (EOC) is the main payload of the KOMPSAT-1 satellite to perform the mission of cartography that builds up a digital map of Korean territory including a digital terrain elevation map. This paper discusses the issues of the digital image simulation of EOC for the generation of EOC simulated scene as taken by EOC at 685km altitude on orbit. For the purpose, simulation work has been performed with the sensor models of EOC and the satellite platform motions models through image chain analysis from the illumination source (Sun) to a simulated image output in digital number. MODTRAN fur radiance calculation, MTF models of optics, detector and motions of EOC for system point spread function (PSF), and signal chain equations for digital number output are described. Several noise models of EOC are also considered. The final output is the EOC simulated image in digital number. The simulation technique can be used in several phase of a spaceborne electro-optical system development project, feasibility study phase, design, manufacturing, test phases, ground image processing phases, and so on.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.32 no.3
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• pp.217-223
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• 2014

With precise sensor position, attitude element, and imaging resolution, a simulated geospatial image can be generated. In this study, a satellite image is simulated using SPOT ortho-image and global elevation data, and the geometric similarity between original and simulated images is analyzed. Using a SPOT panchromatic image and high-density elevation data from a 1/5K digital topographic map data an ortho-image with 10-meter resolution was produced. The simulated image was then generated by exterior orientation parameters and global elevation data (SRTM1, GDEM2). Experimental results showed that (1) the agreement of the image simulation between pixel location from the SRTM1/GDEM2 and high-resolution elevation data is above 99% within one pixel; (2) SRTM1 is closer than GDEM2 to high-resolution elevation data; (3) the location of error occurrence is caused by the elevation difference of topographical objects between high-density elevation data generated from the Digital Terrain Model (DTM) and Digital Surface Model (DSM)-based global elevation data. Error occurrences were typically found at river boundaries, in urban areas, and in forests. In conclusion, this study showed that global elevation data are of practical use in generating simulated images with 10-meter resolution.

• Proceedings of the KSRS Conference
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• 1999.11a
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• pp.331-336
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• 1999

The optical sensors of Electro-Optical Camera (EOC) and Ocean Scanning Multi-spectral Imager (OSMI) aboard the Korea Multi-Purpose SATellite (KOMPSAT) will be placed in a sun synchronous orbit in 1999. The EOC and OSMI sensors are expected to produce the land mapping imagery of Korean territory and the ocean color imagery of world oceans, respectively. Utilization of the EOC and OSMI data would encompass the various fields of science and technology such as land mapping, land use and development, flood monitoring, biological oceanography, fishery, and environmental monitoring. Readiness of data support for user community is thus essential to the success of the KOMPSAT program. As part of testing such readiness prior to the KOMPSAT launch, we have performed the preliminary acceptance test for the KOMPSAT data processing system using the simulated EOC and OSMI data sets. The purpose of this paper is to demonstrate the readiness of the KOMPSAT data processing system, and to help data users understand how the KOMPSAT EOC and OSMI data are processed and archived. Test results demonstrate that all requirements described in the data processing specification have been met, and that the image integrity is maintained for all products. It is however noted that since the product accuracy is limited by the simulated sensor data, any quantitative assessment of image products can not be made until actual KOMPSAT images will be acquired.

• Korean Journal of Remote Sensing
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• v.15 no.4
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• pp.357-365
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• 1999

The optical sensors of Electro-Optical Camera (EOC) and Ocean Scanning Multi-spectral Imager (OSMI) aboard the KOrea Multi-Purpose SATellite (KOMPSAT) will be placed in a sun synchronous orbit in late 1999. The EOC and OSMI sensors are expected to produce the land mapping imagery of Korean territory and the ocean color imagery of world oceans, respectively. Utilization of the EOC and OSMI data would encompass the various fields of science and technology such as land mapping, land use and development, flood monitoring, biological oceanography, fishery, and environmental monitoring. Readiness of data support for user community is thus essential to the success of the KOMPSAT program. As a part of testing such readiness prior to the KOMPSAT launch, we have performed the preliminary acceptance test for the KOMPSAT data processing system using the simulated EOC and OSMI data sets. The purpose of this paper is to demonstrate the readiness of the KOMPSAT data processing system, and to help data users understand how the KOMPSAT EOC and OSMI data are processed, archived, and provided. Test results demonstrate that all requirements described in the data processing specification have been met, and that the image integrity is maintained for all products. It is however noted that since the product accuracy is limited by the simulated sensor data, any quantitative assessment of image products can not be made until actual KOMPSAT images will be acquired.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.44 no.1
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• pp.125-132
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• 2024

Optical satellite images are being used for national security and collection of information, and their utilization is increasing. However, it acquires low-quality images that are not suitable for the user's requirement due to weather conditions and time constraints. In this paper, a deep learning-based conversion of image and colorization model referring to high-resolution SAR images was created to simulate the occluded area with clouds of optical satellite images. The model was experimented according to the type of algorithm applied and input data, and each simulated images was compared and analyzed. In particular, the amount of pixel value information between the input black-and-white image and the SAR image was similarly constructed to overcome the problem caused by the relatively lack of color information. As a result of the experiment, the histogram distribution of the simulated image learned with the Gray-scale image and the high-resolution SAR image was relatively similar to the original image. In addition, the RMSE value was about 6.9827 and the PSNR value was about 31.3960 calculated for quantitative analysis.