• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 2003년도 Proceedings of ACRS 2003 ISRS
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• pp.820-824
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• 2003

One of the most important research areas on remote sensing is spectral unmixing of hyper-spectral data. For spectral unmixing of hyper spectral data, accurate land cover information is necessary. But obtaining accurate land cover information is difficult process. Obtaining land cover information from high-resolution data may be a useful solution. In this study spectral signature of endmembers on ASTER acquired in October was calculated from land cover information on IKONOS acquired in September. Then the spectral signature of endmembers applied to ASTER images acquired on January and March. Then the result of spectral unmxing of them evauateted. The spectral signatures of endmembers could be applied to different seasonal images. When it applied to an ASTER image which have similar zenith angle to the image of the spectral signatures of endmembers, spectral unmixing result was reliable. Although test data has different zenith angle from the image of spectral signatures of endmembers, the spectral unmixing results of urban and vegetation were reliable.

• 한국측량학회지
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• 제33권3호
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• pp.211-218
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• 2015

The analysis of remote sensing data depends on sensor specifications that provide accurate and consistent measurements. However, it is not easy to establish confidence and consistency in data that are analyzed by different sensors using various radiometric scales. For this reason, the cross-calibration method is used to calibrate remote sensing data with reference image data. In this study, we used an airborne hyperspectral image in order to calibrate a multispectral image. We presented an automatic cross-calibration method to calibrate a multispectral image using hyperspectral data and spectral mixture analysis. The spectral characteristics of the multispectral image were adjusted by linear regression analysis. Optimal endmember sets between two images were estimated by spectral mixture analysis for the linear regression analysis, and bands of hyperspectral image were aggregated based on the spectral response function of the two images. The results were evaluated by comparing the Root Mean Square Error (RMSE), the Spectral Angle Mapper (SAM), and average percentage differences. The results of this study showed that the proposed method corrected the spectral information in the multispectral data by using hyperspectral data, and its performance was similar to the manual cross-calibration. The proposed method demonstrated the possibility of automatic cross-calibration based on spectral mixture analysis.

• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 1999년도 Proceedings of International Symposium on Remote Sensing
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• pp.363-370
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• 1999

Remotely sensed data is regarded as a potentially effective data source for the measurement of water quality and for the environmental change of water bodies. In this study, we measured the spectral reflectance by using multi-spectral image of low resolution camera(LRC) which will be loaded in the OSMI multi-purpose satellite(KOMPSAT) scheduled to be launched on 1999 to use the data in analyzing water pollution. We also investigated the possibility of extraction of water quality factors in water bodies by using remotely sensed low resolution data such as NOAA/AVHRR. In this study, Shiwha-District and Sang-Sam Lake was set up as the subject areas for the study. In this part of the study, we measured the spectral reflectance of the water surface to analyze the radiance of the water bodies in low resolution spectral band and tried to analyze the water quality factors in water bodies by using radiance feature from another remotely sensed data such as NOAA/AVHRR. As the method of this study, first, we measured the spectral reflectance of the water surface by using SFOV( Single Field of View) to measure the reflectance of water quality analysis from every channel in LRC spectral band(0.4~O.9${\mu}{\textrm}{m}$). Second, we investigated the usefulness of ground truth data and the LRC data by measuring every spectral reflectance of water quality factors. Third, we analyzed water quality factors by using the radiance feature from another remotely sensed data such as NOAA/AVHRR. We carried out ratio process of what we selected Chlorophyll-a and suspended sediments as the first factors of the water quality. The results of the analysis are below. First, the amount of pollutants of Shiwha-Lake has been increasing every you since 1987 by factors of eutrophication. Second, as a result of the reflectance, Chlorophyll-a represented high spectral reflectance mainly around 0.52${\mu}{\textrm}{m}$ of green spectral band, and turbidity represented high spectral reflectance at 0.57${\mu}{\textrm}{m}$. But suspended sediments absorbed high at 0.8${\mu}{\textrm}{m}$. Third, Chlorophyll-a and suspended sediments could have a distribution chart as a result of the water quality analysis by using NOAA/AVHRR data.

• ETRI Journal
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• 제38권3호
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• pp.540-550
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• 2016

Spectral clustering is a powerful tool for exploratory data analysis. Many existing spectral clustering algorithms typically measure the similarity by using a Gaussian kernel function or an undirected k-nearest neighbor (kNN) graph, which cannot reveal the real clusters when the data are not well separated. In this paper, to improve the spectral clustering, we consider a robust similarity measure based on the shared nearest neighbors in a directed kNN graph. We propose two novel algorithms for spectral clustering: one based on the number of shared nearest neighbors, and one based on their closeness. The proposed algorithms are able to explore the underlying similarity relationships between data points, and are robust to datasets that are not well separated. Moreover, the proposed algorithms have only one parameter, k. We evaluated the proposed algorithms using synthetic and real-world datasets. The experimental results demonstrate that the proposed algorithms not only achieve a good level of performance, they also outperform the traditional spectral clustering algorithms.

• Journal of the Korean Data and Information Science Society
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• 제18권4호
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• pp.1135-1143
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• 2007

The singular value decomposition and the spectral decomposition are the useful methods in the area of matrix computation for multivariate techniques such as principal component analysis and multidimensional scaling. These techniques aim to find a simpler geometric structure for the data points. The singular value decomposition and the spectral decomposition are the methods being used in these techniques for this purpose. In this paper, the singular value decomposition and the spectral decomposition are compared.

• ETRI Journal
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• 제25권1호
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• pp.52-54
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• 2003

We propose an improved algorithm for detecting audio watermarks based on a spread spectrum in the spectral domain. Since the energy of a watermark is much smaller than that of the cover audio data, pre-processing to reduce the effect of the cover data is needed to reliably extract watermarks. We introduce a spectral envelope filter as a pre-process that enhances detecting performance by filtering out the intrinsic spectral character of cover data. The proposed watermarking structure can be easily included in the compression system and can extract watermarks from partially decompressed spectral data. Our experimental results demonstrate that with a bit error rate of around 10 dB against general attacks, the proposed detecting scheme works better than detectors without the spectral filter.

• 대한원격탐사학회지
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• 제33권4호
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• pp.411-422
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• 2017

This research aims to analyze spectral reflectance of intertidal zone and its changes under various environmental conditions. We sampled sand of Mongsanpo tidal flat, Korea, and measured its spectral reflectance by using a spectroradiometer under various water contents, compositions and granularity. We also simulated the reflectance of Landsat 7 ETM+ and compared it with an actual satellite data. Five locations were selected for sampling from the coastline towards the ocean. Grain size diminished stepwise from the coastline to ocean direction, while spectral reflectance differed with wavelength. Water contents lowered the overall reflectance especially at the water absorption bands. Spectral reflectance data were then converted into the simulated one by using Landsat 7 ETM+ spectral reflectance function to be compared with the actual Landsat 7 ETM+ images. It showed the decrease of the spectral reflectance due to the increase of moisture contents from seashore towards the ocean. It is shown that Landsat 7 ETM+ imagery can be efficient to extract moisture contents in the tidal flat while compositional analysis needs satellite sensors with much higher spectral resolution.

• 대한약학회:학술대회논문집
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• 대한약학회 2002년도 Proceedings of the Convention of the Pharmaceutical Society of Korea Vol.2
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• pp.402.2-402.2
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• 2002

An algorithm for finding the best regression models has been developed using NIR spectral data. In cases of regression analysis for quantitation with NIR spectral data, it is very critical to find essential features from the spectral data. This task was accessed in two ways. The first one was to use all-possible combinations of varibles (wavelengths). Correlation coefficients at each spectral points were calculated to get initial set of variables and all of the possible combinations of variable sets were tested with SEC. SEP and/or $R^2$. (omitted)

• 대한원격탐사학회지
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• 제26권5호
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• pp.465-475
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• 2010

분광라이브러리는 지구에 존재하는 다양한 물질의 분광반사 자료와 그에 대한 보조자료를 축적한 데이터베이스로 정의할 수 있다. 분광라이브러리는 다양한 분야에서 물질의 종류와 특성을 분석하기 위한 참조자료로 사용되고 있으며 원격탐사 분야에서는 광학영상 자료와 연계하여 토지 피복의 종류와 속성을 분류하고 더 나아가 초분광영상의 해석에 중요한 자료로 사용되고 있다. 현존 분광라이브러리는 측정항목이나 측정방법, 보조자료 등이 표준화되어 있지 않으며, 특히 국내에 직접 적용하기에는 지표물의 종류가 한정되어 있다. 이 논문은 국내 환경에 직접 적용할 수 있는 분광라이브러리를 설계하고 구축하는 과정을 인공물에 대한 분광반사측정을 실시하였고 동시에 설계된 보조자료의 항목에 대한 측정 및 조사를 실시하였다. 또한 외국에서 개발된 분광라이브러리에서 보조자료가 충실한 광물 및 암석, 토양, 물 등의 분광반사자료를 함께 포함하고 있다. 구축된 분광라이브러리의 활용성 증대와 검색의 용이성을 위해 분광라이브러리 뷰어를 개발하였다.

• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 2003년도 Proceedings of ACRS 2003 ISRS
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• pp.283-285
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• 2003

This paper describes a data fusion method for high spatial resolution satellite imagery. The pixels located around an object edge have spectral mixing because of the geometric primitive of pixel. The larger a size of pixel is, the wider an area of spectral mixing is. The intensity of pixels adjacent edges were modified by the spectral characteristics of the pixels located inside of objects. The methods developed in this study were tested using IKONOS Multispectral and Pan data of a part of Jeju-shi in Korea. The test application shows that the spectral information of the pixels adjacent edges were improved well.