• Korean Journal of Remote Sensing
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• v.37 no.5_1
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• pp.847-859
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• 2021

Recently, as the number of high-resolution satellite SAR images increases, the demand for precise matching of SAR imagesin change detection and image fusion is consistently increasing. RMSE (Root Mean Square Error) values using GCPs (Ground Control Points) selected by analysts have been widely used for quantitative evaluation of image registration results, while it is difficult to find an approach for automatically measuring the registration accuracy. In this study, a feasibility analysis was conducted on using the FSIM (Feature Similarity) index as a measure to evaluate the registration accuracy. TerraSAR-X (TSX) staring spotlight data collected from various incidence angles and orbit directions were used for the analysis. FSIM was almost independent on the spatial resolution of the SAR image. Using a single SAR image, the FSIM with respect to registration errors was analyzed, then use it to compare with the value estimated from TSX data with different imaging geometry. FSIM index slightly decreased due to the differencesin imaging geometry such as different look angles, different orbit tracks. As the result of analyzing the FSIM value by land cover type, the change in the FSIM index according to the co-registration error was most evident in the urban area. Therefore, the FSIM index calculated in the urban was mostsuitable for determining the accuracy of image registration. It islikely that the FSIM index has sufficient potential to be used as an index for the co-registration accuracy of SAR image.

• Korean Journal of Remote Sensing
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• v.38 no.5_1
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• pp.597-608
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• 2022

Remotely sensed data have been used in various fields, such as disasters, agriculture, urban planning, and the military. Recently, the demand for the multitemporal dataset with the high-spatial-resolution has increased. This manuscript proposed an automatic image matching algorithm using a deep learning technique to utilize a multitemporal remotely sensed dataset. The proposed deep learning model was based on High Resolution Net (HRNet), widely used in image segmentation. In this manuscript, denseblock was added to calculate the correlation map between images effectively and to increase learning efficiency. The training of the proposed model was performed using the multitemporal orthophotos of the National Geographic Information Institute (NGII). In order to evaluate the performance of image matching using a deep learning model, a comparative evaluation was performed. As a result of the experiment, the average horizontal error of the proposed algorithm based on 80% of the image matching rate was 3 pixels. At the same time, that of the Zero Normalized Cross-Correlation (ZNCC) was 25 pixels. In particular, it was confirmed that the proposed method is effective even in mountainous and farmland areas where the image changes according to vegetation growth. Therefore, it is expected that the proposed deep learning algorithm can perform relative image registration and image matching of a multitemporal remote sensed dataset.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2013.05a
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• pp.345-348
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• 2013

This paper provides a method for compression and transmission of on-chip bus data. As the data traffic on on-chip buses is rapidly increasing with enlarged video resolutions, many video processor chips suffer from a lack of bus bandwidth and their IP cores have to wait for a longer time to get a bus grant. In multimedia data such as images and video, the adjacent data signals very often have little or no difference between them. Taking advantage of this point, this paper develops a simple bus data compression method to improve the chip performance and presents its hardware implementation. The method is applied to a Video Codec - 1 (VC-1) decoder chip and reduces the processing time of one macro-block by 13.6% and 10.3% for SD and HD videos, respectively.

• Geophysics and Geophysical Exploration
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• v.1 no.3
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• pp.176-182
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• 1998

The borehole radar reflection survey can image the underground structure with high resolution, however, we cannot get any information on the orientation of the reflectors with dipole antenna alone. The direction-finding antenna system is commonly used to give the solution to the problem. However, the interpretation of the data from direction- finding antenna may be time-consuming, and sometimes have ambiguities in the sense of precise determination of the azimuth. To solve the problem, we developed the automatic azimuth finding scheme of reflectors in borehole radar reflection data using direction-finding antenna. The algorithm is based on finding the azimuthal angle possibly showing the maximum reflection amplitude in the least-squared error sense. The developed algorithm was applied to the field data acquired in quarry mine. It was possible to locate nearly all of the reflectors in three dimensional fashion, which coincide with the known geological structures and man-made discontinuities.

• KIPS Transactions on Software and Data Engineering
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• v.7 no.1
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• pp.9-18
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• 2018

Recently, the use of image stitching technology has been increasing as the number of contents based on virtual reality increases. Image Stitching is a method for matching multiple images to produce a high resolution image and a wide field of view image. The image stitching is used in various fields beyond the limitation of images generated from one camera. Image Stitching detects feature points and corresponding points to match multiple images, and calculates the homography among images using the RANSAC algorithm. Generally, corresponding points are needed for calculating conversion relation. However, the corresponding points include various types of noise that can be caused by false assumptions or errors about the conversion relationship. This noise is an obstacle to accurately predict the conversion relation. Therefore, RANSAC algorithm is used to construct an accurate conversion relationship from the outliers that interfere with the prediction of the model parameters because matching methods can usually occur incorrect correspondence points. In this paper, we propose an algorithm that extracts more accurate inliers and computes accurate transformation relations by using correspondence point relation information used in RANSAC algorithm. The correspondence point relation information uses distance ratio between corresponding points used in image matching. This paper aims to reduce the processing time while maintaining the same performance as RANSAC.

• Journal of the Korean Association of Geographic Information Studies
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• v.12 no.2
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• pp.1-10
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• 2009

Recently the large scale of forest disaster such as landslide and forest fire gives a very bad impact on not only forest ecosystem but also farm business so that it has became the main issue of environmental problems. In this study, the landslide hazard area forecast method was developed by considering not only the topographic thematic maps based on GIS and satellite images but also amount of rainfall data, which are very important factors of landslide. Uljin-gun was selected as the study area and the GIS weight score and overlay analysis were applied to topographical map and meteorological observation map. Finally the landslide area distribution map was constructed by considering the evaluation criteria. Also, the accuracy could be acquired by comparing the landslide hazard area forecast map and real damaged area extracted from satellite image.

• Journal of Korean Society of Forest Science
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• v.100 no.4
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• pp.540-547
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• 2011

This study was conducted to compare classification accuracy by tree species using QuickBird imagery for pixel- and segment-based classifications that have been mostly applied to classify land covers. A total of 398 points was used as training and reference data. Based on this points, the points were classified into fourteen land cover classes: four coniferous and seven deciduous tree species in forest classes, and three non-forested classes. In pixel-based classification, three images obtained by using raw spectral values, three tasseled indices, and three components from principal component analysis were produced. For the both classification processes, the maximum likelihood method was applied. In the pixel-based classification, it was resulted that the classification accuracy with raw spectral values was better than those by the other band combinations. As resulted that, the segment-based classification with a scale factor of 50% provided the most accurate classification (overall accuracy:76% and ${\hat{k}}$ value:0.74) compared to the other scale factors and pixel-based classification.

• Korean Journal of Remote Sensing
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• v.24 no.2
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• pp.205-211
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• 2008

The river management in Korea had been focused on line based 2D spatial data for the developing river management application system. In this study, the polygon based 3D spatial data such as aerial photos and satellite images were selected and used through comparing their resolution levels for the river environment management. In addition, 1m detailed DEM (Digital Elevation Model) was constructed to implement the real topography information around river so that the damage area scale could be extracted for flood disaster. Also, the social environment thematic maps such as a cadastral map or land cover map could be used to verify the real damage area scale by overlay analysis on aerial photos or satellite images. The construction of these spatial data makes possible to present the real surface information and extract quantitative analysis to support the scientific decision making for establishing the river management policy. For the further study, the lidar surveying data will be considered as the very useful data by offering the real height information of riverbed as the depth of river so that flood simulation can give more reality.

• Proceedings of the Korean Association of Geographic Inforamtion Studies Conference
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• 2007.10a
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• pp.221-225
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• 2007

산림은 탄소저장능력이 있어 대표적인 온실가스인 이산화탄소를 저감시킨다. 따라서 산림의 탄소저장능력 특성을 규명하고 그것을 산림경영에 반영함으로써 온실가스 저감이라는 국제적 노력에 동참하는 수단으로 활용할 수 있다. 일반적으로 임분에서의 탄소저장능력(Carbon Storage, CS)은 식생탄소저장능력(Vegetation Carbon Storage, VCS)과 토양탄소저장능력(Soil Carbon Storage, SCS)의 합으로 볼 수 있다. 본 연구에서는 우리나라 대표적인 자생수종인 소나무림 VCS의 공간분포를 지엽적 범위(spot level)에서 광역적 범위(regional level)로 확대하여 그 특성을 규명하는 방법을 제시하고자 한다. 지엽적 범위의 조사 및 연구에서 VCS는 임목의 흉고직경(Diameter at Breast Height)과 밀접한 관계가 있는 것으로 확인되었다. 이러한 관계와 Quickbird 고해상도 위성영상에서 추출한 소나무림 공간분포도를 이용해 경관범위(landscape level)에서 소나무림 탄소저장능력의 공간분포를 추정할 수 있었으며,그 결과를GIS 및 RS를 통해 광역적 범위로 확대하였다.

• Korean Journal of Remote Sensing
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• v.36 no.2_2
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• pp.309-323
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• 2020

Estimation of chlorophyll a concentration (CHL) on coastal areas using remote sensing has been mostly performed through multi-spectral satellite image analysis. Recently, various studies using hyperspectral imagery have been attempted. In particular, airborne hyperspectral imagery is composed of hundreds of bands with a narrow band width and high spatial resolution, and thus may be more effective in coastal areas than estimation of CHL through conventional satellite image. In this study, comparative analysis of hyperspectral and satellite-based CHL images was performed to estimate CHL in coastal areas. As a result of analyzing CHL and seawater spectrum data obtained by field survey conducted on the south coast of Korea, the seawater spectrum with high CHL peaked near the wavelength bands of 570 and 680 nm. Using this spectral feature, a new band ratio of 570 / 490 nm for estimating CHL was proposed. Through regression analysis between band ratio and the measured CHL were generated new CHL empirical formula. Validation of new empirical formula using the measured CHL showed valid results, with R² of 0.70, RMSE of 2.43 mg m^-3, and mean bias of 3.46 mg m^-3. As a result of applying the new empirical formula to hyperspectral and satellite images, the average RMSE between hyperspectral imagery and the measured CHL was 0.12 mg m^-3, making it possible to estimate CHL with higher accuracy than multi-spectral satellite images. Through these results, it is expected that it is possible to provide more accurate and precise spatial distribution information of CHL in coastal areas by utilizing hyperspectral imagery.