• Journal of Korean Society for Geospatial Information Science
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• v.21 no.2
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• pp.63-69
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• 2013

Change detection technique is essential to various applications of Very High-Resolution(VHR) satellite imagery and land monitoring. However, change detection accuracy of VHR satellite imagery can be decreased due to various geometrical dissimilarity. In this paper, the existing fusion evaluation indexes were revised and applied to improve VHR imagery based change detection accuracy between multi-temporal images. In addition, appropriate change detection methodology of VHR images are proposed through comparison of general change detection algorithm with cross-sharpened image based change detection algorithm. For these purpose, ERGAS, UIQI and SAM, which were representative fusion evaluation index, were applied to unsupervised change detection, and then, these were compared with CVA based change detection result. Methodologies for minimizing the geometrical error of change detection algorithm are analyzed through evaluation of change detection accuracy corresponding to image fusion method, also. The experimental results are shown that change detection accuracy based on ERGAS index by using cross-sharpened images is higher than these based on other estimation index by using general fused image.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.36 no.6
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• pp.581-588
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• 2018

The aim of this study is to propose a fine co-registration approach for multi-temporal satellite images acquired from RapidEye, which has an advantage of availability for time-series analysis. To this end, we generate multitemporal ortho-rectified images using RPCs (Rational Polynomial Coefficients) provided with RapidEye images and then perform fine co-registration between the ortho-rectified images. A DEM (Digital Elevation Model) extracted from the digital map was used to generate the ortho-rectified images, and the RNCC (Registration Noise Cross Correlation) was applied to conduct the fine co-registration. Experiments were carried out using 4 RapidEye 1B images obtained from May 2015 to November 2016 over the Yeonggwang area. All 5 bands (blue, green, red, red edge, and near-infrared) that RapidEye provided were used to carry out the fine co-registration to show their possibility of being applicable for the co-registration. Experimental results showed that all the bands of RapidEye images could be co-registered with each other and the geometric alignment between images was qualitatively/quantitatively improved. Especially, it was confirmed that stable registration results were obtained by using the red and red edge bands, irrespective of the seasonal differences in the image acquisition.

• Journal of the Korean Association of Geographic Information Studies
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• v.10 no.3
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• pp.31-39
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• 2007

In this study, the hydrosphere change of the Daecheong dam basin was detected qualitatively and quantitatively using Landsat satellite images until recentness since the construction of Daecheong dam. The hydrosphere change of the basin was analyzed by applying supervised classification about Landsat satellite images which were classified according to the hydrosphere, vegetation, road and etc. for four distinct years which are 1981, 1987, 1993, and 2002 year. Landsat satellite images of each year were achieved overlay analysis with extracting only the hydrosphere, and though these results, the hydrosphere change of the Daecheong dam basin was monitored efficiently.

• Korean Journal of Remote Sensing
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• v.37 no.5_1
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• pp.1083-1094
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• 2021

In order to monitor nuclear activity in inaccessible areas, it is necessary to establish a methodology to analyze changesin nuclear activity-related objects using high-resolution satellite images. However, traditional object detection and change detection techniques using satellite images have difficulties in applying detection results to various fields because effects of seasons and weather at the time of image acquisition. Therefore, in this paper, an object of interest was detected in a satellite image using a deep learning model, and object changes in the satellite image were analyzed based on object detection results. An initial training of the deep learning model was performed using an open dataset for object detection, and additional training dataset for the region of interest were generated and applied to transfer learning. After detecting objects by multitemporal and multisensory satellite images, we tried to detect changes in objects in the images by using them. In the experiments, it was confirmed that the object detection results of various satellite images can be directly used for change detection for nuclear activity-related monitoring in inaccessible areas.

• Korean Journal of Remote Sensing
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• v.33 no.6_3
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• pp.1179-1185
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• 2017

The KOMPSAT-3A satellite acquires high-resolution MWIR images twice a day compared to conventional Earth observing satellites. New radiometric information of Earth's surface can be provided due to different characteristics from existing SWIR images or TIR images. In this study, the difference image of multitemporal images was generated and compared with existing infrared images to find the characteristics of KOMPSAT-3A MWIR satellite images. A co-registration was performed and the difference between pixel values was minimized by using PIFs (Pseudo Invariant Features) pixel-based relative normalization. The experiment using Sentinel-2 SWIR image, Landsat 8 TIR image, and KOMPSAT-3A MWIR image showed that the distinction between artifacts in the difference image of KOMPSAT-3A is prominent. It is believed that the utilization of KOMPSAT-3A MWIR images can be improved by using the characteristics of IR image.

• 한국지형공간정보학회:학술대회논문집
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• 2002.11a
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• pp.153-158
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• 2002

• Proceedings of the Korean Institute of Landscape Architecture Conference
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• 2001.10a
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• pp.90-92
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• 2001

• Journal of Korean Society for Geospatial Information Science
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• v.20 no.2
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• pp.47-54
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• 2012

Additional high resolution satellite images, other period or site, are essential for efficient city modeling and analysis. However, the same ground objects have a radiometric inconsistency in different satellite images and it debase the quality of image processing and analysis. Moreover, in an urban area, buildings, trees, bridges, and other artificial objects cause shadow effects, which lower the performance of relative radiometric normalization. Therefore, in this study, we exclude shadow areas and suggest the selective histogram matching methods for image based application without supplementary digital elevation model or geometric informations of sun and sensor. We extract the shadow objects first using adjacency informations with the building edge buffer and spatial and spectral attributes derived from the image segmentation. And, Outlier objects like a asphalt roads are removed. Finally, selective histogram matching is performed from the shadow masked multi-temporal Quickbird-2 images.

• Journal of the Korean Association of Geographic Information Studies
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• v.5 no.2
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• pp.81-90
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• 2002

Remote Sensing has been very useful tool in monitoring of cities and updating of GIS database compare to traditional methods due to its benefit; wide range covering on low cost and advanced data collection. However it had come to a limited method in limited researches because of its relatively poor spatial resolution in scanning. Recently launched satellites are able to produce improved imageries, and new commercial services have been commenced for the use of general public with higher spatial resolution up to $1m{\times}1m$. This study tackled a potential use of these improved satellite imageries in urban planning based on the Multi-temporal satellite imagery with particular reference to monitoring on urban areas, for example urbanization and its expanding. i) Portion of individual features and elements in each pixel of satellite imagery was computed based on 'Endmember' of targeted elements. ii) Urbanized areas were categorized based on the 'Fraction imagery' derived from the 'SMA algorithm'. iii) Alterations and expanding of urban areas were identified based on the Multi-temporal satellite imageries. Tested method showed a strong potential to produce more advanced monitoring skills of urban areas.

• Proceedings of the KAIS Fall Conference
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• 2012.05b
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• pp.845-847
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• 2012

본 논문에서는 다수의 Landsat 영상을 이용하여 구름지역을 보정한 영상을 제작하였다. 비슷한 시기에 취득된 다수의 영상에서 구름을 제거하고, 구름이 제거된 부분을 다른 영상의 온전한 화소값을 기준으로 복원함으로써 효과적으로 구름지역 보정 영상을 제작할 수 있었다. 제작된 영상은 구름 때문에 식별이 불가능한 지역을 크게 감소시켰으며, 주기적인 위성영상의 취득이 어려운 여건을 개선하는 한편, 대규모 지역의 변화탐지 및 영상분류 등 다양한 분야에 활용될 것이다.