• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.35 no.3
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• pp.125-132
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• 2017

Even after applying an image registration, Very High Resolution (VHR) multi-temporal images acquired from different optical satellite sensors such as IKONOS, QuickBird, and Kompsat-2 show a local misalignment due to dissimilarities in sensor properties and acquisition conditions. As the local misalignment, also referred to as Registration Noise (RN), is likely to have a negative impact on multi-temporal information extraction, detecting and reducing the RN can improve the multi-temporal image processing performance. In this paper, an approach to fine registration between VHR multi-temporal images by considering local distribution of RN is proposed. Since the dominant RN mainly exists along boundaries of objects, we use edge information in high frequency regions to identify it. In order to validate the proposed approach, datasets are built from VHR multi-temporal images acquired by optical satellite sensors. Both qualitative and quantitative assessments confirm the effectiveness of the proposed RN-based fine registration approach compared to the manual registration.

• Proceedings of the Korean Society of Surveying, Geodesy, Photogrammetry, and Cartography Conference
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• 2003.10a
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• pp.379-384
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• 2003

In this paper, we introduce a change detection system that can extract and analyze change elements from high-resolution satellite imagery as well as low- or middle-resolution satellite imagery. The developed system provides not only 7 pixel-based methods that can be used to detect change from low- or middle-resolution satellite images but also a float window concept that can be used in manual change detection from high-resolution satellite images. This system enables fast process of the very large image, because it is constituted by OGC grid coverage components. Also new change detection algorithms can be easily added into this system if once they are made into grid coverage components.

• Korean Journal of Remote Sensing
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• v.16 no.1
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• pp.87-98
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• 2000

Image merging techniques have been developed to integrate the advantage of different data type. The objective of this study is to present the optimal method for merging high spatial resolution panchromatic image, such as the latest commercial satellite data, and low spatial resolution mulitspectral images. For this study, a set of 2m resolution panchromatic and 8m resolution mulitspectral data were simulated by using airborne mulitspectral data. Five merging methods of MWD, IHS, PCA, HPF, and CN were applied to produce four bands of high spatial resolution mulitspectral data. Merging results were evaluated by visual interpretation, image statistics, semivariogram, and spectral characteristics. From the aspects of both spatial resolution and spectral information, the wavelet-based MWD merging method have shown very similar results compared with the original data used for the merging.

• Proceedings of the KSRS Conference
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• v.2
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• pp.631-634
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• 2006

Three main important sources for establishing GIS are the orthomap in scale 1:5 000 with Ground Sampling Distance of 0,5m; DEM/DTM data with height error of ${\pm}$1,0m and topographic map in scale 1: 10 000. The new era with Very High Resolution Satellite (VHRS) images as IKONOS, QuickBird, EROS, OrbView and other ones having Ground Sampling Distance (GSD) even lower than 1m has been in potential for producing orthomap in large scale 1:5 000, to update existing maps, to compile general-purpose or thematic maps and for GIS. The accuracy of orthomap generated from VHRS image affects strongly on GIS reliability. Nevertheless, orthomap accuracy taken from VHRS image is at first dependent on chosen sensor geometrical models. This paper presents, at fist, theoretical basic of the Rational Polynomial Coefficient (RPC) model installed in the commercial ImageStation Systems, realized for orthorectifying VHRS images. The RPC model of VHRS image is a replacement camera mode that represents the indirect relation between terrain and its image acquired on the flight orbit. At the end of this paper the practical accuracies of IKONOS and QuickBird image orthorectified by RPC model on Canadian PCI Geomatica System have been presented. They are important indication for practical application of producing digital orthomaps.

• Korean Journal of Remote Sensing
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• v.36 no.6_2
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• pp.1605-1613
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• 2020

The tidal channel is a coastal sedimentary terrain that plays the most important role in the formation and development of tidal flats, and is considered a very important index for understanding and distribution of tidal flat sedimentation/erosion terrain. The purpose of this study is to understand the changes in tidal channels by a period after the opening of the floodgate of the seawall in the reclaimed land of Sihwa Lake using KOMPSAT high-resolution multispectral satellite image data and to evaluate the applicability and efficiency of high-resolution satellite images. KOMPSAT 2 and 3 images were used for extraction of the tidal channels' lineaments in 2009, 2014, and 2019 and were applied to supervised classification method based on Principal Component Analysis (PCA), Artificial Neural Net (ANN), Matched Filtering (MF), and Spectral Angle Mapper (SAM) and band ratio techniques using Normalized Difference Water Index (NDWI) and MF/SAM. For verification, a numerical map of the National Geographic Information Service and Landsat 7 ETM+ image data were utilized. As a result, KOMPSAT data showed great agreement with the verification data compared to the Landsat 7 images for detecting a direction and distribution pattern of the tidal channels. However, it has been confirmed that there will be limitations in identifying the distribution of tidal channels' density and providing meaningful information related to the development of the sedimentary process. This research is expected to present the possibility of utilizing KOMPSAT image-based high-resolution remote exploration as a way of responding to domestic intertidal environmental issues, and to be used as basic research for providing multi-platform-image-based convergent thematic maps and topics.

• Journal of Korean Society for Geospatial Information Science
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• v.16 no.3
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• pp.15-20
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• 2008

High-spatial resolution remote sensing satellites (IKONOS-2, QuickBird and KOMPSAT-2) have provided low-spatial resolution multispectral images and high-spatial resolution panchromatic images. Image fusion or Pan-sharpening is a very important in that it aims at using a satellite image with various applications such as visualization and feature extraction through combining images that have a different spectral and spatial resolution. Many image fusion algorithms are proposed, most methods could not preserve the spectral information of original multispectral image after image fusion. In order to solve this problem, modified induction technique which reduce the spectral distortion of fused image is developed. The spectral distortion is adjusted by the comparison between the spatially degraded pan-sharpened image and original multispectral image and our algorithm is evaluated by QuickBird satellite imagery. In the experiment, pan-sharpened image by various methods can reduce spectral distortion when our algorithm is applied to the fused images.

• Journal of the Korean Geographical Society
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• v.47 no.2
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• pp.282-296
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• 2012

This study compared effects of spatial resolution ratio in image fusion by Korea Multi-Purpose SATellite 2 (KOMPSAT II), also known as Arirang-2. Image fusion techniques, also called pansharpening, are required to obtain color imagery with high spatial resolution imagery using panchromatic and multi-spectral images. The higher quality satellite images generated by an image fusion technique enable interpreters to produce better application results. Thus, image fusions categorized in 3 domains were applied to find out significantly improved fused images using KOMPSAT 2. In addition, all fused images were evaluated to satisfy both spectral and spatial quality to investigate an optimum fused image. Additionally, this research compared Pixel-Based Image Analysis (PBIA) with the GEOgraphic Object-Based Image Analysis (GEOBIA) to make better classification results. Specifically, a roof top of building was extracted by both image analysis approaches and was finally evaluated to obtain the best accurate result. This research, therefore, provides the effective use for very high resolution satellite imagery with image interpreter to be used for many applications such as coastal area, urban and regional planning.

• Proceedings of the KSRS Conference
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• 1999.11a
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• pp.43-47
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• 1999

In this paper, we describe the configuration of the KITSAT-3 image data receiving, archiving, processing and distribution system in operation. Following the low-cost and software-based design concept, the whole system is composed of three PCs : two for data receiving, archiving and processing which provide a full dual-redundant configuration and one for image catalog browsing which can be accessed by public users. Except that receiving and archiving PCs have serial data ingest boards plugged in, they are configured by general peripherals. This basic and simple hardware configuration made it possible to show that a very low cost system can support a full ground operation for the utilization of high-resolution satellite image data.

• Korean Journal of Remote Sensing
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• v.18 no.3
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• pp.163-169
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• 2002

The land uses of Korean peninsula are very complicated and high-density. Therefore, the image classification using coarse resolution satellite images may not provide good results for the land cover classification. The purpose of this paper is to compare the classification accuracy of visual interpretation with that of digital image classification of satellite remote sensing data such as 20m SPOT and 30m TM. In this study, hybrid classification was used. Classification accuracy was assessed by comparing each classification result with reference data obtained from KOMPSAT-1 EOC imagery, air photos, and field surveys.

• Korean Journal of Remote Sensing
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• v.35 no.6_2
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• pp.1095-1106
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• 2019

Damage of forest fire should be investigated quickly and accurately for recovery, compensation and prevention of secondary disaster. Using remotely sensed data, burn severity is investigated based on the difference of reflectance or spectral indices before and after forest fire. Recently, the use of high resolution satellite and UAV imagery is increasing, but it is not easy to obtain an image before forest fire that cannot be predicted where and when. This study tried to analyze availability of high-resolution images and supervised classifiers on the burn severity classification. Two supervised classifiers were applied to the KOMPSAT-3A image and the UAV multispectral image acquired after the forest fire. The maximum likelihood (MLH) classifier use absolute value of spectral reflectance and the spectral angle mapper (SAM) classifier use pattern of spectra. As a result, in terms of spatial resolution, the classification accuracy of the UAV image was higher than that of the satellite image. However, both images shown very high classification accuracy, which means that they can be used for classification of burn severity. In terms of the classifier, the maximum likelihood method showed higher classification accuracy than the spectral angle mapper because some classes have similar spectral pattern although they have different absolute reflectance. Therefore, burn severity can be classified using the high resolution multispectral images after the fire, but an appropriate classifier should be selected to get high accuracy.