• 한국정보과학회논문지:컴퓨팅의 실제 및 레터
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• 제9권3호
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• pp.301-310
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• 2003

본 논문에서는 고해상도 위성영상의 효과적인 분류를 위한 감독분류 시스템을 설계하고 구현하였다. 구현된 시스템은 분류의 정확도 향상을 위한 훈련데이타의 효율적인 선택을 위해서 다양한 인터페이스와 통계자료를 제공한다. 또한, 다양한 위성영상 포맷의 지원과 새로운 감독분류 알고리즘의 확장을 용이하게 하기 위하여 시스템을 모듈화 하였으며, 분광 특성을 고려한 분류의 적용이 가능하다. 분류 알고리즘으로는 평행육면체 분류, 최소거리 분류, 마하라노비스 거리 분류, 최대우도 분류, 퍼지 분류의 감독분류기법을 이용하여 고해상도 위성영상의 처리를 지원한다. 본 시스템의 적용은 고해상도 IKONOS 위성영상을 입력으로 하고, 그 결과를 분석하여 봄으로써 시스템의 응용 가능성을 보여준다.

• 전기전자학회논문지
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• 제18권4호
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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.

• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 1999년도 Proceedings of International Symposium on Remote Sensing
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• pp.405-410
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• 1999

Topographic maps can be made and updated with satellite images, but it requires many human interactions that are inefficient and costly. Therefore, the automatizing of the road extraction procedures could increase efficiency in terms of time and cost. Although methods of extracting roads, railroads and rivers from satellite images have been developed in many studies, studies on the road extraction from satellite images of urbanized area are still not relevant, because many artificial components In the city makes the delineation of the roads difficult. So, to extract roads from high resolution satellite images of urbanized area, this study has proposed the combined use of wavelet transform and multi-resolution analysis. In consequence, this study verifies that it is possible to automatize the road extraction from satellite images of urbanized area. And to realize the automatization more completely, various algorithms need to be developed.

• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 2003년도 Proceedings of ACRS 2003 ISRS
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• pp.1464-1466
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• 2003

classification of the satellite images provides information about land cover and/or land use. Quality of the classification result depends mainly on the spatial and spectral resolutions of the images. In this study, image fusion in terms of resolution merging, and band integration with multi-source of the satellite images; Landsat ETM+ and Ikonos were carried out to improve classification. Resolution merging and band integration could generate imagery of high resolution with more spectral bands. Precise image co-registration is required to remove geometric distortion between different sources of images. Combination of unsupervised and supervised classification of the fused imagery was implemented to improve classification. 3D display of the results was possible by combining DEM with the classification result so that interpretability could be improved.

• 대한원격탐사학회지
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• 제32권1호
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• pp.25-38
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• 2016

With the increasing need for high temporal resolution satellite imagery for monitoring land surfaces, this study evaluated the temporal resolution of the NDVI composites from Geostationary Ocean Color Imager (GOCI) data. The GOCI is the first geostationary satellite sensor designed to provide continuous images over a $2,500{\times}2,500km^2$ area of the northeast Asian region with relatively high spatial resolution of 500 m. We used total 2,944 hourly images of the GOCI level 1B radiance data obtained during the one-year period from April 2011 to March 2012. A daily NDVI composite was produced by maximum value compositing of eight hourly images captured during day-time. Further NDVI composites were created with different compositing periods ranging from two to five days. The cloud coverage of each composite was estimated by the cloud detection method developed in study and then compared with the Moderate Resolution Imaging Spectroradiometer (MODIS) Aqua cloud product and 16-day NDVI composite. The GOCI NDVI composites showed much higher temporal resolution with less cloud coverage than the MODIS NDVI products. The average of cloud coverage for the five-day GOCI composites during the one year was only 2.5%, which is a significant improvement compared to the 8.9%~19.3% cloud coverage in the MODIS 16-day NDVI composites.

• 대한원격탐사학회지
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• 제23권5호
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• pp.409-420
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• 2007

Since 1988, pine wilt disease has spread over rapidly in Korea. It is not easy to detect the damaged pine trees by pine wilt disease from conventional remote sensing skills. Thus, many possibilities were investigated to detect the damaged pines using various kinds of remote sensing data including high spatial resolution satellite image of 2000/2003 IKONOS and 2005 QuickBird, aerial photos, and digital airborne data, too. Time series of B&W aerial photos at the scale of 1:6,000 were used to validate the results. A local maximum filtering was adapted to determine whether the damaged pines could be detected or not at the tree level from high resolution satellite images, and to locate the damaged trees. Several enhancement methods such as NDVI and image transformations were examined to find out the optimal detection method. Considering the mean crown radius of pine trees, local maximum filter with 3 pixels in radius was adapted to detect the damaged trees on IKONOS image. CIR images of 50 cm resolution were taken by PKNU-3(REDLAKE MS4000) sensor. The simulated CIR images with resolutions of 1 m, 2 m, and 4 m were generated to test the possibility of tree detection both in a stereo and a single mode. In conclusion, in order to detect the pine tree damaged by pine wilt disease at a tree level from satellite image, a spatial resolution might be less than 1 m in a single mode and/or 1 m in a stereo mode.

• 항공우주기술
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• 제7권2호
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• pp.187-195
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• 2008

고해상도의 인공위성 데이터로부터 지상좌표를 해석하는 센서모델링 기술은 위성영상자료의 활용 확대 및 신뢰성 확보에 가장 중요한 연구부분으로서 이에 대한 연구과 증가되고 있다. 본 연구는 이러한 요구조건을 기본을 하여, 고해상도 인공위성에서 기본적으로 탑재되어 있는 GPS, Star-tracker, Gyro 등의 센서로부터 측정된 위성의 위치, 속도, 자세 및 시간 정보를 이용하여 위성자료로부터 지상좌표를 해석하는 direct sensor model (DSM)과 위성의 궤도 정보를 얻을 수 없는 경우나 궤도에 대한 정보가 불확실하여 물리적 센서모델로는 지형보정을 수행할 수 없는 경우에 사용될 수 있는 rational function model (RFM)의 적용하여 지상좌표를 해석하는 방법에 대해 살펴보고자 한다.

• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 2002년도 Proceedings of International Symposium on Remote Sensing
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• pp.627-632
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• 2002

One main purpose of environmental monitoring after oil spill is developing ESI (Environmental Sensitivity) Maps. Environmental impacts caused by the spilt oil are strongly depending upon the coastal topology and geology. Monitoring all impacted shorelines is almost impossible; using high-resolution satellite images such as IKONOS greatly contributes to improve the efficiency of on-site researches, at the same time, reliability of ESI maps.

• 한국멀티미디어학회논문지
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• 제19권4호
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• pp.725-735
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• 2016

High-resolution satellite images are used in the fields of mapping, natural disaster forecasting, agriculture, ocean-based industries, infrastructure, and environment, and there is a progressive increase in the development and demand for the applications of high-resolution satellite images. Users of the satellite images desire accurate quality of the provided satellite images. Moreover, the distinguishability of each image captured by an actual satellite varies according to the atmospheric environment and solar angle at the captured region, the satellite velocity and capture angle, and the system noise. Hence , NIIRS must be measured for all captured images. There is a significant deficiency in professional human resources and time resources available to measure the NIIRS of few hundred images that are transmitted daily. Currently, NIIRS is measured every few months or even few years to assess the aging of the satellite as well as to verify and calibrate it [3]. Therefore, we develop an algorithm that can measure the national image interpretability rating scales (NIIRS) of a typical satellite image rather than an artificial target satellite image, in order to automatically assess its quality. In this study, the criteria for automatic edge region extraction are derived based on the previous works on manual edge region extraction [4][5], and consequently, we propose an algorithm that can extract the edge region. Moreover, RER and H are calculated from the extracted edge region for automatic edge region extraction. The average NIIRS value was measured to be 3.6342±0.15321 (2 standard deviations) from the automatic measurement experiment on a typical satellite image, which is similar to the result extracted from the artificial target.

• 대한원격탐사학회지
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• 제38권5_1호
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• pp.523-533
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• 2022

이 논문에서는 상호보완적인 공간 및 분광해상도를 가진 다중센서 위성영상을 이용하여 공간해상도와 분광해상도를 향상시키기 위해 영역-점 회귀 크리깅(area-to-point regression kriging, ATPRK) 기반의 2단계 spatio-spectral fusion method (2SSFM)을 제안하였다. 2SSFM은 ATPRK와 random forest 회귀 모형을 결합하여 다중센서 위성영상에서 높은 공간해상도를 갖는 분광 밴드를 예측한다. 첫 번째 단계에서는 다중센서 위성영상 사이의 공간해상도 차이를 감소시키기 위해 ATPRK 기반 공간 상세화를 수행한다. 두 번째 단계에서는 다중센서 위성영상 사이의 분광 밴드의 관계성을 정량화하기 위해 random forest를 이용한 회귀 모델링을 적용하였다. 2SSFM의 예측 성능은 적색 경계와 단파 적외선 밴드를 생성하는 사례 연구를 통해 평가하였다. 사례 연구에서 2SSFM은 실제 분광 밴드와 유사한 분광패턴을 보이면서 공간해상도가 향상된 적색 경계와 단파 적외선 밴드를 생성할 수 있었으며, 2SSFM가 고해상도 위성영상에서 제공하지 않은 분광 밴드 생성에 유용함을 확인할 수 있었다. 따라서 2SSFM을 통해 실제로 획득 불가능하지만 환경 모니터링에 효과적인 분광 밴드를 예측함으로써 다양한 분광 지수를 생성할 수 있을 것으로 기대된다.