• 한국측량학회:학술대회논문집
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• 한국측량학회 2007년도 춘계학술발표회 논문집
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• pp.207-210
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• 2007

본 연구에서는 고해상도의 IKONOS panchromatic 영상과 multispectral 영상을 IHS와 PCA 방법으로 중합하고 그 결과를 비교하였다. 평가에 있어서는 중합된 영상들과 원영상간의 필셀 값에 대한 평균제곱근오차를 구하고 그 결과를 분석하였다. 분석 결과, multispectral band 1, 3, 4를 사용하는 IHS 방법, multispectral band 1, 2, 4를 사용하는 IHS 방법 및 multispectral band 1, 3, 4를 사용하는 PCA 방법이 원영상의 특성을 잘 보존하는 것으로 평가되었다.

• 대한원격탐사학회지
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• 제24권3호
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• pp.257-266
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• 2008

This study presents an approach to synthesize multispectral images at a higher resolution by exploiting a high-resolution image acquired in panchromatic modality. The synthesized images should be similar to the multispectral images that would have been observed by the corresponding sensor at the same high resolution. The proposed scheme is designed to reconstruct the multispectral images at the higher resolution with as less color distortion as possible. It uses a regression model of the second order to fit panchromatic data to multispectral observations. Based on the regression model, the multispectral images at the higher spatial resolution of the panchromatic image are optimized by a quadratic programming. In this study, the new method was applied to the IKONOS 1m panchromatic and 4m multispectral data, and the results were compared with them of several current approaches. Experimental results demonstrate that the proposed scheme can achieve significant improvement over other methods.

• 한국측량학회지
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• 제36권5호
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• pp.413-422
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• 2018

Many applications using satellite data from high-resolution multispectral sensors require an image fusion step, known as pansharpening, before processing and analyzing the multispectral images when spatial fidelity is crucial. Image fusion methods are to improve images with higher spatial and spectral resolutions by reducing spectral distortion, which occurs on image fusion processing. The image fusion methods can be classified into MRA (Multi-Resolution Analysis) and CSA (Component Substitution Analysis) approaches. To suggest the efficient image fusion method for Pleiades and KOMPSAT (Korea Multi-Purpose Satellite) 3 satellites, this study will evaluate image fusion methods for multispectral and panchromatic images. HPF (High-Pass Filtering), SFIM (Smoothing Filter-based Intensity Modulation), GS (Gram Schmidt), and GSA (Adoptive GS) were selected for MRA and CSA based image fusion methods and applied on multispectral and panchromatic images. Their performances were evaluated using visual and quality index analysis. HPF and SFIM fusion results presented low performance of spatial details. GS and GSA fusion results had enhanced spatial information closer to panchromatic images, but GS produced more spectral distortions on urban structures. This study presented that GSA was effective to improve spatial resolution of multispectral images from Pleiades 1A and KOMPSAT 3.

• 전자공학회논문지B
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• 제33B권8호
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• pp.42-49
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• 1996

In this paper, we propose a satellite multispectral image data compression method using classified vector quantization. This method classifies each pixel vector considering band characteristics of multispectral images. For each class, we perform both intraband and interband vector quantization to romove spatial and spectral redundancy, respectively. And residual vector quantization for error images is performed to reduce error of interband vector quantization. Thus, this method improves compression efficiency because of removing both intraband(spatial) and interband (spectral) redundancy in multispectral images, effectively. Experiments on landsat TM multispectral image show that compression efficiency of proposed method is better than that of conventional method.

• 대한원격탐사학회지
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• 제24권5호
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• pp.525-533
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• 2008

This study presents a pansharpening method, so called FitPAN, to synthesize multispectral images at a higher resolution by exploiting a high-resolution image acquired in panchromatic modality. FitPAN is a modified version of the quadratic programming approach proposed in (Lee, 2008), which is designed to generate synthesized multispectral images similar to the multispectral images that would have been observed by the corresponding sensor at the same high resolution. The proposed scheme aims at reconstructing the multispectral images at the higher resolution with as less spectral distortion as possible. This study also proposes a sharpening process to eliminate some distortions appeared in the fused image of the higher resolution. It employs the Point-Jacobian MAP iteration utilizing the contextual information of the original panchromatic image. In this study, the new method was applied to the IKONOS 1m panchromatic and 4m multispectral data, and the results were compared with them of several current approaches. Experimental results demonstrate that the proposed scheme can achieve significant improvement in both spectral and block distortion.

• 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.

• 대한원격탐사학회지
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• 제22권2호
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• pp.101-110
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• 2006

본 연구에서는 항공 관측으로 얻어진 다중분광영상과 LIDAR (LIght Detection And Ranging) 자료를 이용하여 농업지역의 토지피복 분류 정도를 분석하였다. 다중분광영상은 녹색, 적색, 근적외역의 3분광으로 이루어져 있다. LIDAR 벡터 자료로부터 최초 반사강도 영상과 최초 반사 표고 자료와 최후 반사의 지상 표고 자료의 차이로 산출된 식생 높이 영상이 얻어졌다. 토지피복 분류 방법은 최대우도법을 사용했으며, 다중분광영상의 3밴드 영상 LIDAR의 반사강도 영상, 식생 높이 영상을 이용하였다. 모든 영상을 이용한 토지피복 분류의 전체 정도는 85.6%로 다중분광영상만을 이용한 정도보다 10%이상 향상되었다. 여러 농작물간의 높이의 차이, 수목과 농작물 높이의 차이와 LIDAR 반사강도 차이로 인하여 다중분광영상과 LIDAR 영상을 사용한 토지피복 분류의 정도가 향상되었다.

• 대한원격탐사학회지
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• 제23권6호
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• pp.547-552
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• 2007

본 연구에서는 고해상도의 panchromatic 영상을 이용하여 저해상도의 multispectral 영상을 고해상도로 재구축하는 방법을 제시하고 있다. 제안된 방법은 저해상도와 고해상도 간의 선형 모형 사용하여 실제의 spectral 값에 부합하는 고해상도 영상을 재구축하며 두 단계로 이루어 진다. 첫 단계는 고해상도 feature와 연관된 저해상도의 선형 모형을 이용하여 최소 자승 오류 법에 의한 global 추정 과정이고 두 번째 단계는 재구축된 영상을 지역적으로 원래의 spectral 값과 일관되게 만드는 local 수정 과정이다. 본 연구에서 제안 방법을 이용하여 6m KOMPSAT-1 EOC 자료와 30m LANDSAT ETM+에 적용하였고 또한 IKONOS 1m RGB 영상 생성하였다. 실험 결과는 새로이 제시된 방법이 저해상도 Multispectral 영상의 고해상도 재구축에 탁월한 성능을 가지고 있음을 보여주었다.

• 조명전기설비학회논문지
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• 제30권2호
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• pp.1-10
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• 2016

A system which provides multispectral near-IR and visible gray images of objects is constructed and an algorithm is derived to acquire a natural color image of objects from the gray images. A color image of 24 color patches is obtained by recovering their CIE (International Commission on Illumination) LAB color coordinates $L^*$, $a^*$, $b^*$ from their gray images using the algorithm based on polynomial regression. The system is composed of a custom-designed LED illuminator emitting multispectral near-IR illuminations, fluorescent lamps and a monochrome digital camera. Color reproducibility of the algorithm is estimated in CIELAB color difference ${\Delta}E^*_{ab}$. And as a result, if yellow and magenta color patches with around 10 ${\Delta}E^*_{ab}$ are disregarded, the average ${\Delta}E^*_{ab}$ is 2.9, and this value is within the acceptability tolerance for quality evaluation for digital color complex image.

• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 2002년도 Proceedings of International Symposium on Remote Sensing
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• pp.388-393
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• 2002

Remote sensing data is collected and analyzed to enhance understanding of the terrestrial surface. Since Landsat satellite was launched in 1972, many researches using multispectral data has been achieved. Recently, with the availability of airborne and satellite hyperspectral data, the study on hyperspectral data are being increased. It is known that as the number of spectral bands of high-spectral resolution data increases, the ability to detect more detailed cases should also increase, and the classification accuracy should increase as well. In this paper, we classified the hyperspectral and multispectral data and tested the classification accuracy. The MASTER(MODIS/ASTER Airborne Simulator, 50channels, 0.4~13$\mu$m) and Landsat TM(7channels) imagery including Yeong-Gwang area were used and we adjusted the classification items in several cases and tested their classification accuracy through statistical comparison. As a result of this study, it is shown that hyperspectral data offer more information than multispectral data.