• Proceedings of the KIEE Conference
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• 1991.07a
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• pp.837-840
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• 1991

This paper describes an implementation of road network extraction algorithms for satellite images. We propose a new road network extraction algorithm which uses magnitude and direction information of edges. The results of applying the proposed algorithm to satellite images are presented and compared with those of other algorithms.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.33 no.4
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• pp.325-331
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• 2015

This paper suggests an intersection method to improve the accuracy of three-dimensional position from heterogeneous satellite stereo images, and addresses validation of the suggested method following the experimental results. The three-dimensional position is achieved by determining an intersection point of two rays that have been precisely adjusted through the sensor orientation. In case of conventional homogeneous satellite stereo images, the intersection point is generally determined as a mid-point of the shortest line that links two rays in at least square fashion. In this paper, a refined method, which determines the intersection point upon the ray adjusted at the higher resolution image, was used to improve the positioning accuracy of heterogeneous satellite images. Those heterogeneous satellite stereo pairs were constituted using two KOMPSAT-2 and QuickBird images of covering the same area. Also, the positioning results were visually compared in between the conventional intersection and the refined intersection, while the quantitative analysis was performed. The results demonstrated that the potential of refined intersection improved the positioning accuracy of heterogeneous satellite stereo pairs; especially, with a weak geometry of the heterogeneous satellite stereo, the greater effects on the accuracy improvement.

• Korean Journal of Remote Sensing
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• v.37 no.5_1
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• pp.999-1011
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• 2021

In spatio-temporal fusion aiming at predicting images with both high spatial and temporal resolutionsfrom multi-sensor images, the radiometric inconsistency between input multi-sensor images may affect prediction performance. This study investigates the effect of radiometric correction, which compensate different spectral responses of multi-sensor satellite images, on the spatio-temporal fusion results. The effect of relative radiometric correction of input images was quantitatively analyzed through the case studies using Sentinel-2, PlanetScope, and RapidEye images obtained from two croplands. Prediction performance was improved when radiometrically corrected multi-sensor images were used asinput. In particular, the improvement in prediction performance wassubstantial when the correlation between input images was relatively low. Prediction performance could be improved by transforming multi-sensor images with different spectral responses into images with similar spectral responses and high correlation. These results indicate that radiometric correction is required to improve prediction performance in spatio-temporal fusion of multi-sensor satellite images with low correlation.

• Korean Journal of Remote Sensing
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• v.37 no.6_3
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• pp.2027-2034
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• 2021

Using multi-temporal KOMPSAT-3/3A high-resolution satellite images, the Normalized Difference Vegetation Index (NDVI) for the area around the Fukushima daiichi nuclear power plant was determined, and the pattern of vegetation changes was analyzed. To calculate the NDVI, surface reflectance from the KOMPSAT-3/3A satellite image was used. Satellite images from four years were used, and the zones where the images overlap was designated as the area of interest (AOI) for the study, and by setting a profile passing through highly vegetated area as a data analysis method, the changes by year were examined. In addition, random points were extracted within the AOI and displayed as a box plot to quantitatively indicate change of NDVI distribution pattern. The main results of this study showed that the NDVI in 2014 was low within AOI in the vicinity of the nuclear power plant, but vegetated area continued to expand until 2021. These results were also confirmed in the change monitoring results shown in a profile or box plot. In disaster areas where access is restricted, such as the Fukushima nuclear power plant area, where it is difficult to collect field data, obtaining land cover classification products with high accuracy using satellite images is challenging, so it is appropriate to analyze them using primary outputs such as vegetation indices obtained from high-resolution satellite imagery. It is necessary to establish an international cooperation system for jointly utilizing satellite images. Meanwhile, to periodically monitor environmental changes in neighboring countries that may affect the Korean peninsula, it is necessary to establish utilization models and systems using high-resolution satellite images.

• Proceedings of the KSRS Conference
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• 2002.10a
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• pp.543-547
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• 2002

In this paper, we introduce a more improved camera modeling method for linear pushbroom images than the method proposed by Orun and Natarajan(ON). ON model shows an accuracy of within 1 pixel if more than 10 ground control points(GCPs) are provided. In general, there is high correlation between platform position and attitude parameters but ON model ignores attitude variation in order to overcome such correlation. We propose a new method that obtains an optimal solution set of parameters without ignoring the attitude variation. We first assume that attitude parameters are constant and estimate platform position's. Then we estimate platform attitude parameters using the values of estimated position parameters. As a result, we can set up an accurate camera model for a linear pushbroom satellite scene. In particular, we can apply the camera model to its surrounding scenes because our model provide sufficient information on satellite's position and attitude not only for a single scene but also for a whole imaging segment. We tested on two images: one with a pixel size 6.6m$\times$6.6m acquired from EOC(Electro Optical Camera), and the other with a pixel size 10m$\times$l0m acquired from SPOT. Our camera model procedures were applied to the images and gave satisfying results. We had obtained the root mean square errors of 0.5 pixel and 0.3 pixel with 25 GCPs and 23 GCPs, respectively.

• Journal of KIISE:Computing Practices and Letters
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• v.9 no.3
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• pp.301-310
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• 2003

In this paper, we design and Implement the supervised classification systems for high resolution satellite images. The systems support various interfaces and statistical data of training samples so that we can select the m()st effective training data. In addition, the efficient extension of new classification algorithms and satellite image formats are applied easily through the modularized systems. The classifiers are considered the characteristics of spectral bands from the selected training data. They provide various supervised classification algorithms which include Parallelepiped, Minimum distance, Mahalanobis distance, Maximum likelihood and Fuzzy theory. We used IKONOS images for the input and verified the systems for the classification of high resolution satellite images.

• Journal of IKEEE
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• v.18 no.4
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• pp.536-543
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• 2014

Terrain classification is still a challenging issue in image processing, especially with high resolution satellite images. The well-known obstacles include low accuracy in the detection of targets, especially for the case of man-made structures, such as buildings and roads. In this paper, we present an efficient approach to classify and detect building footprints, foliage, grass and road from high resolution grayscale satellite images. Our contribution is to build a strong classifier using AdaBoost based on a combination of co-occurrence and Haar-like features. We expect that the inclusion of Harr-like feature improves the classification performance of the man-made structures, since Haar-like feature is extracted from corner features and rectangle features. Also, the AdaBoost algorithm selects only critical features and generates an extremely efficient classifier. Experimental result indicates that the classification accuracy of AdaBoost classifier is much higher than that of the conventional classifier using back propagation algorithm. Also, the inclusion of Harr-like feature significantly improves the classification accuracy. The accuracy of the proposed method is 98.4% for the target detection and 92.8% for the classification on high resolution satellite images.

• 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.

• Journal of Korean Society for Geospatial Information Science
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• v.20 no.1
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• pp.117-123
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• 2012

Many nations have been launched their own earth observation satellites due to much interest in space exploration. Because of this, the number of high resolution satellite are increasing day by day. With the development of web environment, general users have easy access to satellite images. The research conducted a basic study about web-based satellite photogrammetry in order to determine three dimensional coordinates easily without having profound knowledge of satellite sensor modeling. Various element technologies were analyzed to decide three dimensional coordinates by using high resolution satellite images in web environment, and case studies were conducted by using IKONOS satellite images. By applying parallel projection model, which is one of sensor modeling methods, epipolar image was created and prototype form of program that can decide three dimensional location in web environment was implemented. Through this, general users could decide three dimensional location easily in web environment and knew that already existed spatial information can be renewed and used in service area.

• Journal of Satellite, Information and Communications
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• v.9 no.2
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• pp.1-6
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• 2014

The increasing demands and utilization of the multi-purpose satellites have led to diverse research activities with regards to satellite image processing and applications. In this paper, the research activities of satellite images is investigated relevant to domestic agricultural activities in past 5 years and the system characteristics of the related spaceborne payload is analyzed. For this purpose, a broad range of research materials has been collected published in past years and a statistical analysis is performed to classify the use of satellite images. Overall the current work is aimed to carry out a comprehensive analysis on the current status of satellite imaging in agricultural sectors. Furthermore, this paper can be utilized to identify and support the incoming satellite development plan utilizing medium imaging capabilities specially in the field of agricultural uses.