• 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.

• Korean Journal of Remote Sensing
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• v.17 no.1
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• pp.33-44
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• 2001

The paper presents several satellite models and satellite image decomposition methods for inaccessible 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 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 inaccessible 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 1st 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.

• Aerospace Engineering and Technology
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• v.13 no.1
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• pp.174-183
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• 2014

The Value-Added Processing System(VAPS) was developed for post-processing the KOMPSAT imagery. Recently software version and hardware specification of VAPS were changed for improving the VAPS performance. The purpose of this study is to describe about the improvement of existing VAPS(ver.1.0) and systematically evaluate the performance of the improved VAPS(ver.2.0). To this end, test-bed areas in South and North Korea were selected and then image processing tests were conducted using KOMPSAT-2 and KOMPSAT-3 imagery in both areas. In conclusion, VAPS(ver.2.0) had an ability to generate the high level products like ortho images and mosaic images. Image processing time using the Graphic Processing Unit(GPU) on ver.2.0 was enhanced up to 10 times than ver.1.0.

• Journal of Environmental Impact Assessment
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• v.7 no.1
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• pp.81-91
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• 1998

The objective of this study was to use remotely sensed data, combined with in situ data, for the assessment of trophic state for Daecheong reservoir. Three Landsat TM(Thematic Mapper) imagery data were processed to portray trophic state conditions. The remotely sensed data and the measured data were obtained on 20 June 1995. Regression models have been developed between the chlorophyll-a concentration and reflectance which was converted to Landsat TM digital data. The regression model was determined based on the correlation coefficient which was higher than 0.7 and was applied to the entire study area to generate a distribution map of chlorophyll-a and trophic state. The equation, providing estimates of chlorophyll-a concentration, represented the year-to-year spatial variation of trophic zones in the reservoir. Satellite remote sensing data derived from Landsat TM had been successfully used for trophic slate mapping in Daecheong reservoir.

• Korean Journal of Remote Sensing
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• v.30 no.1
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• pp.37-45
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• 2014

This paper reports the geolocation accuracy of KOMPSAT-3 imagery. KOMPSAT-3 was launched successfully on May 18, 2012 and has been released last March. In this paper, we have checked the geolocation accuracy of initial sensor model, precise sensor model and stereo-and multi-image model using four KOMPSAT-3 images covering the same area. The KOMPSAT-3 images without GCPs provided the geolocation accuracy of about 30m and the geocorrected KOMPSAT-3 images provided the geolocation accuracy of about 1m or less. KOMPSAT-3 stereo- and multi-images models yield threedimensional points with sub-meter accuracy in horizontal and vertical direction. Overall, KOMPSAT-3 showed much improved performance in terms of the geolocation accuracy over KOMPSAT-2. KOMPSAT-3 is expected to be able to replace foreign satellite data with sub-meter accuracy level for achieving accurate geometric information.

• Proceedings of the KSRS Conference
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• 2004.10a
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• pp.626-629
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• 2004

Texture imaging, which means texture image creation by co-occurrence relation, has been known as one of useful image analysis methodologies. For this purpose, most commercial remote sensing software provides texture analysis function named GLCM (Grey Level Co-occurrence Matrix). In this study, texture-imaging program for GLCM algorithm is newly implemented in the MS Visual IDE environment. While, additional texture imaging modules based on GLDV (Grey Level Difference Vector) are contained in this program. As for GLCM/GLDV texture variables, it composed of six types of second order texture function in the several quantization levels of 2(binary image), 8, and 16: Homogeneity, Dissimilarity, Energy, Entropy, Angular Second Moment, and Contrast. As for co-occurrence directionality, four directions are provided as $E-W(0^{\circ}),\;N-E(45^{\circ}),\;S-W(135^{\circ}),\;and\;N-S(90^{\circ}),$ and W-E direction is also considered in the negative direction of E- W direction. While, two direction modes are provided in this program: Omni-mode and Circular mode. Omni-mode is to compute all direction to avoid directionality problem, and circular direction is to compute texture variables by circular direction surrounding target pixel. At the second phase of this study, some examples with artificial image and actual satellite imagery are carried out to demonstrate effectiveness of texture imaging or to help texture image interpretation. As the reference, most previous studies related to texture image analysis have been used for the classification purpose, but this study aims at the creation and general uses of texture image for urban remote sensing.

• Proceedings of the KSRS Conference
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• v.2
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• pp.981-984
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• 2006

Oil pollution of the ocean is a major environmental problem, especially in its coastal zones. Synthetic aperture radar (SAR) flown on satellites, such as ERS-2 and Envisat, has been proved to be a useful tool in oil spill monitoring due to its wide coverage, day and night, and all-weather capability. The total 120 SAR images containing oil spill over the East China Sea were collected and analyzed, ranging in date from July 23, 2002 to November 11, 2005. After preprocessed, SAR images were segmented by adaptive threshold method. The oil spill images were incorporated into GIS after distinguished from look-like phenomena, finally we presented the oil spills distribution map for the East China Sea. The wide-swath and quick-looks SAR imagery for mapping of oil spill distribution over large marine areas were proved to be useful when full resolution data are not available. After the temporal and spatial distribution of the oil spills were analyzed, we found that most of oil spills were distributed along the main ship routes, which means the illegal discharge by ships, and the occurrence of oil spill detected on SAR images acquired during morning and summer is much higher than during evening and winter.

• Korean Journal of Remote Sensing
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• v.27 no.5
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• pp.587-599
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• 2011

As one measure of image interpretability, NIIRS(National Imagery Interpretability Rating Scale) has been used. Unlike MTF(Modulation Transfer Function), SNR(Signal to Noise Ratio), and GSD(Ground Sampling Distance), NIIRS can describe the quality of overall image at user's perspective. NIIRS is observed with human observation directly or estimated by edge analysis. For edge analysis specially manufactured artificial target is used commonly. This target, formed with a tarp of black and white patterns, is deployed on the ground and imaged by the satellite. Due to this, the artificial target-based method needs a big expense and can not be performed often. In this paper, we propose a new edge analysis method that enables to estimate NIIRS accurately. In this method, natural targets available in the image are used and characteristics of the target are considered. For assessment of the algorithm, various experiments were carried out. The results showed that our algorithm can be used as an alternative to the artificial target-based method.

• Journal of Korean Society for Geospatial Information Science
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• v.16 no.4
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• pp.79-87
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• 2008

There has been a strong demand for low altitude UAV development in rapid mapping not only to acquire high resolution image with much more low cost and weather independent, compared to satellite surveying or traditional aerial surveying, but also to meet many needs of the aerial photogrammetry. Especially, efficient geocoding of UAV imagery is the key issue. Contrary to high UAV potential for civilian applications, the technology development in photogrammetry for example direct georeferencing is in the early stage and it requires further research and additional technical development. In this study, two approaches are supposed for automatic geocoding of UAV still images by simple affine transformation and block adjustment of affine transformation using minimal ground control points and also evaluated the applicability and quality of geometric model compared to geocoded images generated by commercial S/W.

• Journal of Korean Society for Geospatial Information Science
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• v.15 no.1 s.39
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• pp.23-29
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• 2007

Spatial outliers in remotely sensed imagery represent observed quantities showing unusual values compared to their neighbor pixel values. There have been various methods to detect the spatial outliers based on spatial autocorrelations in statistics and data mining. These methods may be applied in detecting forest fire pixels in the MODIS imageries from NASA's AQUA satellite. This is because the forest fire detection can be referred to as finding spatial outliers using spatial variation of brightness temperature. In this paper, we propose a new forest fire detection algorithm which is based on local spatial association analysis, and test the proposed algorithm to evaluate its applicability. In order to evaluate the proposed algorithm, the results were compared with the MODIS fire product provided by the NASA MODIS Science Team, which showed the possibility of the proposed algorithm in detecting the fire pixels.