• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.387-389
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• 2003

Rational function model (RFM) is a universal sensor model for remote sensing image restitution. It is able to substitute for models of all known sensors. In this paper, RFM generation by CCD linear image models is described in detail. A principle of RFM-based 3D reconstruction and its implementation in JX4 DPW is also described. Experiments using IKONOS and SPOT5 images are carried out on JX4 DPW. Results show that RFM generated is feasible for photogrammetric restitution of CCD linear images.

• Korean Journal of Remote Sensing
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• v.23 no.5
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• pp.347-353
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• 2007

Recently, $Gonz\acute{a}lez-Aud\acute{i}cana$ et al. proposed the substitute wavelet intensity(SWI) method which provided a solution based on the intensity-hue-saturation(IHS) method for the fusing of panchromatic(PAN) and multispectral(MS) images. Although the spectral quality of the fused MS images is enhanced, this method is not efficient enough to quickly merge massive volumes of data from satellite. To overcome this problem, we introduce a new SWI method based on a fast IHS transform to implement efficiently as an alternative procedure. In addition, we show that the method is well applicable for fusing IKONOS PAN with MS images.

• The Journal of the Korea Contents Association
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• v.7 no.1
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• pp.215-224
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• 2007

The classification of the satellite images is basic part in Remote sensing. In classification of the satellite images, class separability feature is very effective accuracy of the images classified. For improving classification accuracy, It is necessary to study classification methode than analysis of class separability feature deciding classification probability. In this study, IKONOS, SPOT 5, Landsat TM, were resampled to sizes 1m grid. Above images were calculated the class separability prior to the step for classification of pixels. This Study concludes, each image was measured by the rate of class separability, values classified were showed highly about $1,600{\sim}2,000$.

• Korean Journal of Remote Sensing
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• v.31 no.4
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• pp.321-330
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• 2015

Atmospheric correction is an essential part in time-series analysis on biophysical parameters of surface features. In this study, we tried to examine possible problems in atmospheric correction of multitemporal High Spatial Resolution (HSR) images obtained from two different sensor systems. Three KOMPSAT-2 and two IKONOS-2 multispectral images were used. Three atmospheric correction methods were applied to derive surface reflectance: (1) Radiative Transfer (RT) - based absolute atmospheric correction method, (2) the Dark Object Subtraction (DOS) method, and (3) the Cosine Of the Uun zeniTh angle (COST) method. Atmospheric correction results were evaluated by comparing spectral reflectance values extracted from invariant targets and vegetation cover types. In overall, multi-temporal reflectance from five images obtained from January to December did not show consistent pattern in invariant targets and did not follow a typical profile of vegetation growth in forests and rice field. The multi-temporal reflectance values were different by sensor type and atmospheric correction methods. The inconsistent atmospheric correction results from these multi-temporal HSR images may be explained by several factors including unstable radiometric calibration coefficients for each sensor and wide range of sun and sensor geometry with the off-nadir viewing HSR images.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.35 no.4
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• pp.241-248
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• 2017

GCPs (Ground Control Points) are needed to correct the DEM (Digital Elevation Model) produced from high-resolution satellite images and the RPC (Rational Polynomial Coefficient). It is difficult to acquire the GCPs through field surveys such as GPS surveys and to read the image coordinates corresponding to the GCPs. In addition, GCPs cannot cover the entire image of the test site, and the RPC correction results may be influenced by the arrangement and distribution of the GCPs in the image. Therefore, a new method for the RPC correction is needed. In this study, an LHD (Least-squares Height Difference) DEM matching method was applied using previous DEMs: SRTM DEM, digital map DEM, and corrected IKONOS DEM. This was carried out to correct the DEM produced from KOMPSAT-3 satellite images and the provided RPC without GCPs. The IKONOS DEM had the highest accuracy, and the height accuracy was about ${\pm}3m$ RMSE in a mountainous area and about ${\pm}2m$ RMSE in an area with only low heights.

• Proceedings of the Korean Society of Surveying, Geodesy, Photogrammetry, and Cartography Conference
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• 2006.04a
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• pp.287-291
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• 2006

Currently application of high-resolution satellite imagery is expanding with development of high tech optical and space aviation technology. Although using 3 dimensional modeling technology in order to attain accurate terrain information using existing ground control points is the most dependable reference data, such means are unapplicable for certain area because of it's limited access. In this study, we have researched into ways to utilizing high resolution satellite images from IKONOS and Quickbird, and sub-meter class satellites images that will be utilized In the future such as Arirang images and PLEIADES images for unaccessible areas. For that purpose we have created accuracy verification and GCP files for existing ortho-imagery and digital elevation model. The results showed that accuracy of ortho-Imagery and digital elevation model was RMSE X:3.043m, Y:2.921m, Z:6.139m. Also, after ortho-rectifying IKONOS images using ground control points extracted from ortho imagery and digital elevation model the accuracy of the imagery was RMSE X:3.243m, Y:2.067m, Z:1.872m.

• ETRI Journal
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• v.33 no.4
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• pp.537-546
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• 2011

This paper introduces an automated method for building height recovery through the integration of high-resolution satellite images and digital vector maps. A cross-correlation matching method along the vertical line locus on the Ikonos images was deployed to recover building heights. The rational function models composed of rational polynomial coefficients were utilized to create a stereopair of the epipolar resampled Ikonos images. Building footprints from the digital maps were used for locating the vertical guideline along the building edges. The digital terrain model (DTM) was generated from the contour layer in the digital maps. The terrain height derived from the DTM at each foot of the buildings was used as the starting location for image matching. At a preset incremental value of height along the vertical guidelines derived from vertical line loci, an evaluation process that is based on the cross-correlation matching of the images was carried out to test if the top of the building has reached where maximum correlation occurs. The accuracy of the reconstructed buildings was evaluated by the comparison with manually digitized 3D building data derived from aerial photographs.

• Korean Journal of Remote Sensing
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• v.28 no.2
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• pp.181-189
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• 2012

Recently, interest in potential for estimating water quality using high resolution satellite images is increasing. However, low SNR(Signal to Noise Ratio) over inland water and radiometric errors such as non-linearity of brightness value of high resolution satellite images often lead to accuracy degradation in water quality estimation. Therefore radiometric correction should be carried out to estimate water quality for high resolution satellite images. For KOMPSAT-2 images parameters for brightness value-radiance conversion are not available and precise radiometric correction is difficult. To exploit KOMPSAT-2 images for water quality monitoring, it is necessary to investigate non-linearity of brightness value and noise over inland water. In this paper, we performed brightness value comparison between KOMPSAT-2 images and IKONOS/GeoEye-1, which are known to show the linearity. We used the images obtained over the same area and on the same date for comparison. As a result, we showed that although KOMPSAT-2 images are more noisy;the trend of brightness value and pattern of noise are almost similar to reference images. The results showed that appropriate target area to minimize the impact of noise was $5{\times}5$. Non-linearity of brightness value between KOMPSAT-2 and reference images was not observed. Therefore we could conclude that KOMPSAT-2 may be used for estimation of water quality parameters such as concentration of chlorophyll.

• Journal of Korean Society for Geospatial Information Science
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• v.17 no.1
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• pp.61-69
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• 2009

By the advent of the high resolution satellite imagery, a ground-coverage included by a single satellite image is decreased. By the reason, there are increasing needs in image mosaicking technology to use images to various GIS fields. This paper describes an edge-based seamline extraction algorithm using edge information such as rivers, roads, buildings for image mosaicking. For this, we developed a method to track and link discontinuous edges extracted by edge detection operator. To estimate the effectiveness of the proposed algorithm, we applied the algorithm to IKONOS, KOMPSAT-1 and SPOT-5 satellite images. The experimental results showed that the algorithm successfully dealts with discontinuities caused by geometric differences in two images.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.408-410
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• 2003

In this paper, an improved gradient direction assisted linking algorithm is proposed. This algorithm begins with initial seeds satisfying some local criteria. Then it will search along the direction provided by the initial point. A window will be generated in the gradient direction of the current point. Instead of the conventional method which only considers the value of the local salient structure, an improved mathematical model is proposed to describe the desired linear features. This model not only considers the value of the salient structure but also the direction of it. Furthermore, the linking problem under this model can be efficiently solved by dynamic programming method. This algorithm is tested for linear features detection in IKONOS images. The result demonstrates this algorithm is quite promising.