• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.216-218
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• 2003

QuickBird satellite is quickly becoming the best choice for high-resolution mapping using satellite images. In this paper, we will describe the followings: (1) how to correct QuickBird data using different geometric correction methods, (2) data fusion using QuickBird panchromatic and multispectral data, and (3) automatic DEM extraction using QuickBird stereo data.

• Journal of the Korean Association of Geographic Information Studies
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• v.6 no.1
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• pp.98-106
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• 2003

Because satellite images have the advantage of high resolution, multi-spectral, revisit and wide swath characteristics, it is increased to utilize satellite image and get information little by little in nowadays. In order to utilize remote sensed images effectively, it is necessary to process satellite images through many processing steps. Among them, geometric correction is essential step for satellite image processing. In this study, we constructed geometric correction data using LANDSAT satellite images. First, we extracted GCPs from maps and constructed database over the Korean peninsular. Second, LANDSAT satellite images, 165 scenes were corrected geometrically using GCP database. Finally, we made 7 mosaic images by means of geometric correction images over Korean peninsular. We think that constructed geometric correction data will be used for many application fields as basic data.

• Korean Journal of Remote Sensing
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• v.37 no.3
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• pp.431-447
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• 2021

Recently, the use of high-resolution satellites is increasing in many areas. In order to supply useful satellite images stably, it is necessary to establish automatic precision geometric correction technic. Geometric correction is the process that corrected geometric errors of satellite imagery based on the GCP (Ground Control Point), which is correspondence point between accurate ground coordinates and image coordinates. Therefore, in the automatic geometric correction process, it is the key to acquire high-quality GCPs automatically. In this paper, we proposed iterative precision geometry correction method. we constructed an image pyramid and repeatedly performed GCP chip matching, outlier detection, and precision sensor modeling in each layer of the image pyramid. Through this method, we were able to acquire high-quality GCPs automatically. we then improved the performance of geometric correction of high-resolution satellite images. To analyze the performance of the proposed method, we used KOMPSAT-3 and 3A Level 1R 8 scenes. As a result of the experiment, the proposed method showed the geometric correction accuracy of 1.5 pixels on average and a maximum of 2 pixels.

• Proceedings of the KSRS Conference
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• 1999.11a
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• pp.285-289
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• 1999

A processing system to produce cloud-free composite image data set was developed. In the process, a fine geometric correction based on orbit parameters and ground control points and radiometric correction based on 6S code are applied. Presently, by using AVHRR image data received at Tokyo, Okinawa, Ulaanbaatar and Bangkok, data set of 10 days composite images covering almost whole Asian region.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.37 no.3
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• pp.209-219
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• 2019

Satellite imagery occurs geometric and radiometric errors due to external environmental factors at the acquired time, which in turn causes false-alarm in change detection. These errors should be eliminated by geometric and radiometric corrections. In this study, we propose a methodology that automatically and simultaneously performs geometric and radiometric corrections by using the SURF (Speeded-Up Robust Feature) algorithm and the mask filter. The MPs (Matching Points), which show invariant properties between multi-temporal imagery, extracted through the SURF algorithm are used for automatic geometric correction. Using the properties of the extracted MPs, PIFs (Pseudo Invariant Features) used for relative radiometric correction are selected. Subsequently, secondary PIFs are extracted by generated mask filters around the selected PIFs. After performing automatic using the extracted MPs, we could confirm that geometric and radiometric errors are eliminated as the result of performing the relative radiometric correction using PIFs in geo-rectified images.

• ETRI Journal
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• v.35 no.6
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• pp.1115-1125
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• 2013

This paper presents a scheme for geometric correction of projected content for planar and quadratic projection surfaces. The scheme does not require the projection surface to be perfectly quadratic or planar and is therefore suitable for uneven low-cost commercial and home projection surfaces. An approach based on the recursive subdivision of second-order B$\acute{e}$zier patches is proposed for the estimation of projection distortion owing to surface imperfections. Unlike existing schemes, the proposed scheme is completely automatic, requires no prior knowledge of the projection surface, and uses a single uncalibrated camera without requiring any physical markers on the projection surface. Furthermore, the scheme is scalable for geometric calibration of multi-projector setups. The efficacy of the proposed scheme is demonstrated using simulations and via practical experiments on various surfaces. A relative distortion error metric is also introduced that provides a quantitative measure of the suppression of geometric distortions, which occurs as the result of an imperfect projection surface.

• Current Optics and Photonics
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• v.5 no.1
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• pp.77-77
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• 2021

• 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.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.60 no.5
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• pp.1023-1029
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• 2011

In the industrial vision systems, image correction has great influence on the overall performance of measurement or inspection. The overall area of distorted image is usually splitted into small control areas, and each area is corrected by its control points. The performance of correction methods using control points can be improved by reduction of control areas because the computational time and memory highly depend on the number of control areas. We develop a merging algorithm that reduces control areas and preserves the correction accuracy. The algorithm merges the splitted control areas by use of quad tree method. Experimental results are presented to verify the usefulness of the proposed method.

• Proceedings of the KSRS Conference
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• 2004.10a
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• pp.216-219
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• 2004

In this study, the concept and techniques to generate the level lA, lB and 2A image products have been reviewed. In particular, radiometric and geometric corrections and bands registration used to generate level lA, lB and 2A products have been focused in this study. Radiometric correction is performed to take into account radiometric gain and offset calculated by compensating the detector response non-uniformity. And, in order to compensate satellite altitude, attitude, skew effects, earth rotation and earth curvature, some geometric parameters for geometric corrections are computed and applied. Bands registration process using the matching function between a geometry, which is called 'reference geometry', and another one which is corresponds to the image to be registered is applied to images in case of multi-spectral imaging mode. In order to generate level-lA image products, a simple radiometric processing is applied to a level-0 image. Level-lB image has the same radiometry correction as a level-lA image, but is also issued from some geometric corrections in order to compensate skew effects, Earth rotation effects and spectral misregistration. Level-2A image is generated using some geo-referencing parameters computed by ephemeris data, orbit attitudes and sensor angles. Level lA image is tested by visual analysis. The difference between distances calculated level 1 B image and distances of real coordinate is tested. Level 2A image is tested Using checking points.