• Proceedings of the KSRS Conference
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• 1999.11a
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• pp.355-362
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• 1999

With the ready access to the high resolution satellite image data, users of and areas covered by satellite image data are constantly on the rise world-wide. Korea will also be able to take full advantage of the satellite data once the KOMPSAT is successfully launched. Harmonizing satellite data production and application technology and users' needs, along with the guiding policy is essential for promoting satellite data use. Up to now, the Korean government has mainly concentrated on developing production technology for the satellite units. However, the imminent task of independent satellite data production demands a promotion policy for satellite data use. In this context, the policy is defined as an important medium for identifying the role and status of satellite image information at the national level and also Preparing the legal as well as systematic foundation for producing, building, distributing, and packaging satellite data. For example, in the countries with the advanced satellite technology, such as the United States, the United Kingdom, and Australia, digital ortho image and digital elevation model (DEM) are mandatorily included in the National Geographic Framework Data through policy measures. In addition, in order for the efficient provision of the satellite data, separate organization or agency is being in operation for the exclusive production and distribution of the satellite data. The present paper aims to examine the role and status of the satellite data as well as their current status and problems in Korea in reference to the National Spatial Data Infrastructure, and finally to provide the policy directions to promote the satellite data use in public sector on the basis of the preceding analyses.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.24 no.3
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• pp.289-297
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• 2006

The Land Cover/Land Use Map have been constructed from 1998, which has hierarchical structure according to land cover/land use system. Level 1 classification Map have done using Landsat satellite image over whole Korean peninsula. Level II classification Map have been digitized using IRS-1C, 1D, KOMPSAT and SPOT5 satellite images resolution-merged with low resolution color images. Level II Land Cover/Land Use Map construction by digitizing method, however, is consuming enormous expense for satellite image acquisition, image process and Land Cover/Land Use Map construction. In this paper, the possibility of constructing Level II Land Cover/Land Use Map using hyperspectral satellite image of EO-1 Hyperion, which is studied a lot recently, is studied. The comparison of classifications using Hyperion satellite image offering more spectral information and Landsat-7 ETM+ image is performed to evaluate the availability of Hyperion satellite image. Also, the algorithm of the optimal band selection is presented for effective application of hyperspectral satellite image.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.458-460
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• 2003

This paper proposes new algorithms for cloudy area detection by GHA (Generalized Hebbian Algorithm) and SOFM (Self-Organized Feature Map). SOFM and GHA are unsupervised neural networks and are used for pattern classification and shape detection of satellite image. Proposed algorithm is based on block based image processing that size is 16${\times}$16. Results of proposed algorithm shows good performance of cloudy area detection except blur cloudy area.

• Proceedings of the KSRS Conference
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• 1998.09a
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• pp.252-256
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• 1998

Accurate mapping of satellite images is one of the most important Parts in many remote sensing applications. Since the position and the attitude of a satellite during image acquisition cannot be determined accurately enough, it is normal to have several hundred meters' ground-mapping errors in the systematically corrected images. The users which require a pixel-level or a sub-pixel level mapping accuracy for high-resolution satellite images must use a number of Ground Control Points (GCPs). In this paper, the performance of two geometric correction algorithms is tested and compared. One is the polynomial warping algorithm which is simple and popular enough to be implemented in most of the commercial satellite image processing software. The other is full camera modelling algorithm using Physical orbit-sensor-Earth geometry which is used in satellite image data receiving, pre-processing and distribution stations. Several criteria were considered for the performance analysis : ultimate correction accuracy, GCP representatibility, number of GCPs required, convergence speed, sensitiveness to inaccurate GCPs, usefulness of the correction results. This paper focuses on the usefulness of the precision correction algorithm for regular image pre-processing operations. This means that not only final correction accuracy but also the number of GCPs and their spatial distribution required for an image correction are important factors. Both correction algorithms were implemented and will be used for the precision correction of KITSAT-3 images.

• Aerospace Engineering and Technology
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• v.1 no.2
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• pp.66-74
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• 2002

The characteristics of the satellite image quality parameters driven by satellite imaging instrument are investigated. Since the satellite image is directly produced by the satellite imaging instrument, the satellite image quality depends on the imager performance highly. This is why the imager performance parameters are considered as an important part of the satellite image quality parameters. The imager performance parameters consist of spectral band parameters, ground sample distance(GSD) parameters, swath width parameters, imager Modulation Transfer Function (MTF), imager Signal to Noise Ratio(SNR), radiometric response characteristics parameters, pixel registration, and imager calibration.

• Korean Journal of Remote Sensing
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• v.24 no.6
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• pp.591-603
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• 2008

Satellite performance can be evaluated by image quality. MTF(Modulation Transfer Fuction), SNR(Signal To Noise Ratio), GSD(Ground Sample Distance) etc. are important bias parameters to analyze image quality. It is necessary to estimate quality of satellite image in design stage. In this paper, the simulating method of satellite image quality, considering design, manufacturing, and operation, is proposed. The proposed method shall be used to estimate and restore quality of satellite image.

• Korean Journal of Remote Sensing
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• v.19 no.3
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• pp.247-253
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• 2003

The mainly used technique to correct satellite images with geometric distortion is to develop a mathematical relationship between pixels on the image and corresponding points on the ground. Polynomial models with various transformations have been designed for defining the relationship between two coordinate systems. GCP based geometric correction has peformed overall plane to plane mapping. In the overall plane mapping, overall structure of a scene is considered, but local variation is discarded. The Region with highly variant height is rectified with distortion on overall plane mapping. To consider locally variable region in satellite image, TIN-based rectification on a satellite image is proposed in this paper. This paper describes the relationship between GCP distribution and rectification model through experimental result and analysis about each rectification model. We can choose a geometric correction model as the structural characteristic of a satellite image and the acquired GCP distribution.

• Journal of Astronomy and Space Sciences
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• v.30 no.4
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• pp.321-326
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• 2013

The modulation transfer function (MTF) is a widely used indicator in assessments of remote-sensing image quality. This MTF method is also used to restore information to a standard value to compensate for image degradation caused by atmospheric or satellite jitter effects. In this study, we evaluated MTF values as an image quality indicator for the Geostationary Ocean Color Imager (GOCI). GOCI was launched in 2010 to monitor the ocean and coastal areas of the Korean peninsula. We evaluated in-orbit MTF value based on the GOCI image having a 500-m spatial resolution in the first time. The pulse method was selected to estimate a point spread function (PSF) with an optimal natural target such as a Seamangeum Seawall. Finally, image restoration was performed with a Wiener filter (WF) to calculate the PSF value required for the optimal regularization parameter. After application of the WF to the target image, MTF value is improved 35.06%, and the compensated image shows more sharpness comparing with the original image.

• Proceedings of the KSRS Conference
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• 2002.10a
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• pp.780-785
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• 2002

As the KOMPSAT-1 satellite can roll tilt up to $\pm$45$^{\circ}$, we have analyzed some EOC images taken at different tilt angles fur this study. The required ground coordinates for bundle adjustment and geometric accuracy, are read from the digital map produced by the National Geography Institution, at a scale of 1:5, 000. These are the steps taken for the tilting angle of KOMPSAT-1 satellite to be present in the evaluation of the accuracy of the geometric of each different stereo image data: Firstly, as the tilting angle is different in each image, the satellite dynamic characteristic must be determined by the sensor modeling. Then the best sensor modeling equation is determined. The result of this research, the difference between the RMSE values of individual stereo images is due more the quality of image and ground coordinates than to the tilt angle. The bundle adjustment using three KOMPSAT-1 stereo pairs, first degree of polynomials for modeling the satellite position were sufficient.

• Journal of KIISE:Databases
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• v.35 no.5
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• pp.379-390
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• 2008

Due to the increase of the amount of image data, the demand for searching similar images is continuously increasing. Therefore, many researches about the content-based image retrieval (CBIR) are conducted to search similar images effectively. In CBIR, it uses image contents such as color, shape, and texture for more effective retrieval. However, when we apply CBIR to satellite images which are complex and pose the difficulty in using color information, we can have trouble to get a good retrieval result. Since it is difficult to use color information of satellite images, we need image segmentation to use shape information by separating the shape of an object in a satellite image. However, because satellite images are complex, image segmentation is hard and poor image segmentation results in poor retrieval results. In this paper, we propose a new approach to search similar images without image segmentation for satellite images. To do a similarity search without image segmentation, we define a similarity of an image by considering SIFT keypoint descriptors which doesn't require image segmentation. Experimental results show that the proposed approach more effectively searches similar satellite images which are complex and pose the difficulty in using color information.