• Proceedings of the Korea Water Resources Association Conference
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• 2005.05b
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• pp.132-137
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• 2005

재해분야에 인공위성의 활용도가 높아짐에 따라 본 연구에서는 Landsat 영상과 RADARSAT 영상을 이용하여 안성천유역을 대상으로 침수지역을 추출하고자 하였다. Landsat 영상은 침수 전과 후의 영상을 각각 선정하였으며 RADARSAT 영상은 침수 중과 침수 후 의 영상을 선정하였다. 각 영상에 대하여 전처리와 기하보정을 걸친 후 침수지역을 파악하기 위한 방법으로 토지피복분류 방법을 사용하였고, 그 중 Landsat 영상은 분광반사계를 이용하여 감독분류를 실시하였고, RADARSAT 영상은 무감독 분류를 실시하여 침수 지역을 확인할 수 있었다.

• Korean Journal of Remote Sensing
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• v.38 no.6_1
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• pp.1035-1046
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• 2022

This study evaluated the applicability of multi-resolution data fusion for land-cover classification. In the applicability evaluation, a spatial time-series geostatistical deconvolution/fusion model (STGDFM) was applied as a multi-resolution data fusion model. The study area was selected as some agricultural lands in Iowa State, United States. As input data for multi-resolution data fusion, Moderate Resolution Imaging Spectroradiometer (MODIS) and Landsat satellite images were used considering the landscape of study area. Based on this, synthetic Landsat images were generated at the missing date of Landsat images by applying STGDFM. Then, land-cover classification was performed using both the acquired Landsat images and the STGDFM fusion results as input data. In particular, to evaluate the applicability of multi-resolution data fusion, two classification results using only Landsat images and using both Landsat images and fusion results were compared and evaluated. As a result, in the classification result using only Landsat images, the mixed patterns were prominent in the corn and soybean cultivation areas, which are the main land-cover type in study area. In addition, the mixed patterns between land-cover types of vegetation such as hay and grain areas and grass areas were presented to be large. On the other hand, in the classification result using both Landsat images and fusion results, these mixed patterns between land-cover types of vegetation as well as corn and soybean were greatly alleviated. Due to this, the classification accuracy was improved by about 20%p in the classification result using both Landsat images and fusion results. It was considered that the missing of the Landsat images could be compensated for by reflecting the time-series spectral information of the MODIS images in the fusion results through STGDFM. This study confirmed that multi-resolution data fusion can be effectively applied to land-cover classification.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.13 no.8
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• pp.3692-3697
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• 2012

Landsat 7 ETM+ sensor is getting imageries in the SLC-off state since May 31, 2003 due to mechanical defect of SLC(Scan Line Corrector). Therefore additional correction works are required to use these imageries. In this study, Landsat 7 SLC-off imageries were corrected using Gapfill function and compared with Landsat 5 around the same time. Most of pixels in omitted areas due to SLC-off by producing SLC-off imageries and imageries without visual incompatibility could be achieved as there were not unnatural noises. Also, the corrected imageries were performed land cover classification which was compared with the classification result using reference image. To do this, it could be suggested the possibility of SLC-off imagery. Landsat 7 SLC-off corrected imageries will improve the difficult conditions to detect changes of large areas and be used to detect changes of large areas and classify imageries as well as to recover imagery loss arising regionally such as small scale cloud, etc.

• 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.26 no.2
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• pp.87-98
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• 2010

For generating accurate land cover map over the whole Korean Peninsula, post-mosaic classification method is desirable in large area where multiple image data sets are used. We try to derive an optimal mosaic method of multi-temporal Landsat ETM+ scenes for the land cover classification over the Korea Peninsula. Total 65 Landsat ETM+ scenes were acquired, which were taken in 2000 and 2001. To reduce radiometric difference between adjacent Landsat ETM+ scenes, we apply three relative radiometric correction methods (histogram matching, 1st-regression method referenced center image, and 1st-regression method at each Landsat ETM+ path). After the relative correction, we generated three mosaic images for three seasons of leaf-off, transplanting, leaf-on season. For comparison, three mosaic images were compared by the mean absolute difference and computer classification accuracy. The results show that the mosaic image using 1st-regression method at each path show the best correction results and highest classification accuracy. Additionally, the mosaic image acquired during leaf-on season show the higher radiance variance between adjacent images than other season.

• Korean Journal of Remote Sensing
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• v.35 no.5_2
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• pp.861-871
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• 2019

Landsat satellite images have been increasingly used for disaster damage analysis and disaster monitoring because they can be used for periodic and broad observation of disaster damage area. However, periodic disaster monitoring has limitation because of areas having missing data due to clouds as a characteristic of optical satellite images. Therefore, a study needs to be conducted for restoration of missing areas. This study detected and removed clouds and cloud shadows by using the quality assessment (QA) band provided when acquiring Landsat-8 images, and performed image restoration of removed areas through a spatial and temporal adaptive reflectance fusion (STARFM) algorithm. The restored image by the proposed method is compared with the restored image by conventional image restoration method throught MLC method. As a results, the restoration method by STARFM showed an overall accuracy of 89.40%, and it is confirmed that the restoration method is more efficient than the conventional image restoration method. Therefore, the results of this study are expected to increase the utilization of disaster analysis using Landsat satellite images.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.28 no.2
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• pp.239-246
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• 2010

Satellite image has capability of getting a broad data rapidly. It is possible that acquisition of change information about topography, land, ecosystem and urbanization etc. from multi-temporal satellite Images. In this study, the time-series change of vegetation has detected using four period Landsat Imageries. Also, NDVI was used to recognize the vitality of vegetation. Time series change of vegetation about study area was able to detect effectively by the results of classification and NDVI. It is expected that this study should be utilized as the decision making related to the effective management and plan establishment.

• Proceedings of the Korean Society of Surveying, Geodesy, Photogrammetry, and Cartography Conference
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• 2003.04a
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• pp.167-170
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• 2003

본 연구는 무감독영상해석(Unsupervised Classification)에서 주성분 분석법(Principal Component Analysis)의 응용성을 연구하기 위하여, 주성분 분석법을 K-means, ISODATA 두가지 무감독분류법에 적용하였다. 적용대상지역은 제주도이다. 본 연구에서 주성분 분석 방법중에서 비정규형 주성분 분석방법 (Unstandardized PCA)과 정규형 주성분 분석방법(Standardized PCA) 두가지 경우로 나누어서 각각 연구하였다. 이를 위하여 제주도의 Landsat TM영상과 국토연구원에서 조사한 제주도 식생분류 조사자료와 현장조사 자료 그리고 1/25,000 수치지도를 이용하였다. 그리고 분석된 자료의 정확도를 평가하기 위하여 오차행렬(Error Matrix)을 도입하여 계산하였다. 우선 비정규형 주성분 분석법으로 구한 주성분 영상과 Landsat TM 원래 영상을 오차행렬을 이용하여 제주도의 식생 분류에 각각 적용하였다. 그 결과, K-means 무감독분류법에서는 Landsat TM 자료를 직접 이용한 경우에는 바다와 육상의 분류가 잘 되지 않았으며, 또한 전반적인 영상분류결과가 관측치와 많은 차이를 보였다. 그러나, 주성분 분석법으로 계산된 주성분 영상으로 K-means방법으로 분류 한 결과는 관측치와 잘 일치를 하였다. ISODATA의 경우, Landsat TM 원래영상을 계산하면, K-means으로 분류한 결과보다는 좋은 값을 나타냈으나, 주성분 분석법으로 구한 영상의 계산결과와 비교하면, 주성분 영상으로 구한 분류결과의 정확도가 약 15%정도 높게 나타났다. 정규형 주성분 분석법의 경우를 보면 K-means에서는 Landsat TM원래 자료보다 우수한 결과를 보여주었으나, 비정규형 주성분 분석법으로 계산된 결과보다는 정확도가 다소 떨어지는 단점이 있었고, ISODATA의 경우도 Landsat TM원래 자료보다 약 7%정도의 높은 정확도를 보였으나, 비정규형 영상보다는 약8%정도 낮은 정확도를 보였다. 본 연구에서 주성분 분석법으로 계산된 결과에서 주목되는 것은, 주성분 분석법으로 구한 주성분 영상은 분류방법(K-means, ISODATA, artificial neural networks)에 따라 분류된 결과값이 비슷하게 나타난 반면, Landsat TM원래 자료는 분류방법에 따라 결과값이 많은 차이를 보여 주었다. 그리고 주성분 분석 방법 중에서도 비정규형 주성분 분석법(Unstandardized PCA)이 정규형 주성분 분석법(Standardized PCA)보다 영상분석에서 더 좋은 결과를 보여주는 것으로 나타났다.

• 한국지형공간정보학회:학술대회논문집
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• 1994.10a
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• pp.42-46
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• 1994

In this paper, we studied on an efficient detection and removal of the periodic scanner interference noise in LANDSAT images. Firstly, noise models and their characteristics are discussed. And we proposed a new scheme of noise detection in Fourier domain. Then, an dfficient noise filter can be designed based on the detected noise components. To verifythe effectiveness of our scheme, some experiments guided by our proposed scheme are performed using a real LANDSAT image.

• Proceedings of the Korean Society of Disaster Information Conference
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• 2017.11a
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• pp.63-64
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• 2017

광학 위성영상의 경우 기상조건의 영향을 많이 받기 때문에 연속적인 데이터 취득과 분석이 어렵다. 본 연구에서는 영상 획득률이 상대적으로 낮은 광학 위성영상의 단점을 보완하기 위해 SAR 위성영상과 광학 위성영상을 활용하여 다양한 자연재난에 대해 효율적인 재난관리의 가능성을 북한 황강댐 수표면적 분석사례를 통해 제시하였다. 위성영상 수집기간은 2016년 1월부터 2017년 7월까지 획득된 자료로 SAR 위성영상은 Sentinel-1을, 광학 위성영상은 Landsat-8을 획득하여 분석하였다. 이때 수증기, 구름 등 기상조건에 의해 Landsat-8을 획득하지 못한 부분은 Sentinel-1으로 대체하여 분석하였다. 그 결과, 2016년 5월 19일자 관측된 황강댐의 만수위 당시 수표면적과 2017년 7월 18일에 관측된 황강댐의 수표면적이 유사하여 방류위험성이 있어 상시 모니터링이 필요하다고 판단된다. 본 연구에서는 Sentinel-1와 Landsat-8을 활용하여 효율적인 재난관리를 보여주는 사례를 통하여 선제적인 재난관리에 활용성을 보여준다.