• 대한원격탐사학회지
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• 제28권3호
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• pp.297-305
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• 2012

This study was performed to estimate forest canopy density (FCD) using airborne hyperspectral data acquired in the Independence Hall of Korea in central Korea. The airborne hyperspectral data were obtained with 36 narrow spectrum ranges of visible (Red, Green, and Blue) and near infrared spectrum (NIR) scope. The FCD mapping model developed by the International Tropical Timber Organization (ITTO) uses vegetation index (VI), bare soil index (BI), shadow index (SI), and temperature index (TI) for estimating FCD. Vegetation density (VD) was calculated through the integration of VI and BI, and scaled shadow index (SSI) was extracted from SI after the detection of black soil by TI. Finally, the FCD was estimated with VD and SSI. For the estimation of FCD in this study, VI and SI were extracted from hyperspectral data. But BI and TI were not available from hyperspectral data. Hyperspectral data makes the numerous combination of each band for calculating VI and SI. Therefore, the principal component analysis (PCA) was performed to find which band combinations are explanatory. This study showed that forest canopy density can be efficiently estimated with the help of airborne hyperspectral data. Our result showed that most forest area had 60 ~ 80% canopy density. On the other hand, there was little area of 10 ~ 20% canopy density forest.

• 한국측량학회지
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• 제34권2호
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• pp.143-151
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• 2016

The distribution of seabed rock in the coastal area is relevant to navigation safety and development of ocean resources where it is an essential hydrographic measurement. Currently, the distribution of seabed rock relies on interpretations of water depth data or point based bottom materials survey methods, which have low efficiency. This study uses the airborne bathymetric Lidar data and the hyperspectral image to detect seabed rock in the coastal area of the East Sea. Airborne bathymetric Lidar data detected seabed rocks with texture information that provided 88% accuracy and 24% commission error. Using the airborne hyperspectral image, a classification result of rock and sand gave 79% accuracy, 11% commission error and 7% omission error. The texture data and hyperspectral image were fused to overcome the limitations of individual data. The classification result using fused data showed an improved result with 96% accuracy, 6% commission error and 1% omission error.

• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 2003년도 Proceedings of ACRS 2003 ISRS
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• pp.334-336
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• 2003

Satellite and airborne hyperspectral sensor images are suitable for investigating the vegetation state in agricultural land. However, image data obtained by an optical sensor inevitably includes mixels caused by high altitude observation. Therefore, mixel analysis method, which estimates both the pure spectra and the coverage of endmembers simultaneously, is required in order to distinguish the qualitative spectral changes due to the chlorophyll quantity or crop variety, from the quantitative coverage change. In this paper, we apply our agricultural independent component analysis (ICA) model to an airborne hyperspectral sensor image, which includes noise and fluctuation of coverage, and estimate pure spectra and the mixture ratio of crop and soil in agricultural land simultaneously.

• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 2003년도 Proceedings of ACRS 2003 ISRS
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• pp.1049-1051
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• 2003

This study investigated the use of airborne image data to provide estimates of within -field variability in soil properties and crop growth as an alternative to extensive field data collection. Hyperspectral and multispectral images were acquired in 2000, 2001, and 2002 for central Missouri experimental fields. Data were converted to reflectance using chemically-treated reference tarps with known reflectance levels. Geometric distortion of the hyperspectral pushbroom sensor images was corrected with a rubber sheeting transformation. Statistical analyses were used to relate image data to field-measured soil properties and crop characteristics. Results showed that this approach has potential; however, it is important to address a number of implementation issues to insure quality data and accurate interpretations.

• 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.

• 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 2003년도 Proceedings of ACRS 2003 ISRS
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• pp.1076-1080
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• 2003

$CO_2$ uptake of vegetation is one of the important variables in order to estimate photosynthetic activity, plant growth and carbon budget estimations. The objective of this research was to develop a new estimation method of $CO_2$ uptake of vegetation based on airborne hyperspectral remote sensing measurements in combination with a photosynthetic rate curve model. In this study, a compact airborne spectrographic imager (CASI) was used to obtain image over a field that had been set up to study the $CO_2$ uptake of corn on August 7, 2002. Also, a field survey was conducted concurrently with the CASI overpass. As a field survey, chlorophyll a content, photosynthetic rate curve, Leaf area, dry biomass and light condition were measured. The developed estimation method for $CO_2$ uptake consists of three major parts: a linear mixture model, an enhanced big leaf model and a photosynthetic rate curve model. The Accuracy of this scheme indicates that $CO_2$ uptake of vegetation could be estimated by using airborne hyperspectral remote sensing data in combination with a physiological model.

• 대한원격탐사학회지
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• 제29권2호
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• pp.161-172
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• 2013

분광 영상의 효과적인 테스트베드 구축은 초분광 영상의 다양한 활용을 위하여 선행되어야한다. 본 연구에서는 다양한 연구 분야에 적용할 수 있는 테스트베드의 구축 방법 및 효용성에 대한 기초 연구를 수행하였다. 이를 위하여, 기존의 국내 외 테스트베드 생성 방법을 분석하고, 이를 바탕으로 하여 항공기 기반 초분광 센서의 촬영을 위한 테스트베드를 설계하였다. 구축된 테스트베드를 촬영한 영상에서 기준자료를 생성시키기 위하여, 본 연구에서는 대리보정에 의한 전처리 기법을 적용하고, 이에 대한 효용성을 분석하였다. 실험결과, 대리보정은 타프를 이용하는 것이 가장 이상적이지만, 상황에 따라서 분광반사율이 일정하거나, 변화폭이 상대적으로 적은 물질을 이용하는 것이 가능하다는 것을 확인하였다. 본 연구에서 촬영한 테스트베드 자료는 국내 외의 초분광 영상 처리 연구에 참조자료로 사용될 수 있을 것으로 사료된다.

• 대한원격탐사학회지
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• 제24권5호
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• pp.397-408
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• 2008

A wide variety of applications of imaging spectrometer have been proved using data from airborne systems. The Compact Airborne Imaging Spectrometer System (CAISS) was jointly designed and developed as the airborne hyperspectral imaging system by Korea Aerospace Research Institute (KARI) and ELOP inc., Israel. The primary mission of the CAISS is to acquire and provide full contiguous spectral information with high spatial resolution for advanced applications in the field of remote sensing. The CAISS consists of six physical units; the camera system, the gyro-stabilized mount, the jig, the GPS/INS, the power inverter and distributor, and the operating system. These subsystems are to be tested and verified in the laboratory before the flight. Especially the camera system of the CAISS has to be calibrated and validated with the calibration equipments such as the integrating sphere and spectral lamps. To improve data quality and its availability, it is the most important to understand the mechanism of imaging spectrometer system and the radiometric and spectral characteristics. The several performance tests of the CAISS were conducted in the camera system level. This paper presents the major characteristics of the CAISS, and summarizes the results of performance tests in the camera system level.

• 한국지리정보학회지
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• 제15권3호
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• pp.52-65
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• 2012

작물과 산림을 포함한 식생에 대한 순1차 생산(net primary production, NPP)와 총1차 생산(gross primary production, GPP)은 바이오매스와 식생의 탄소저장과 밀접한 관련이 있으며, 원격탐사를 이용해 바이오매스를 추정하는 많은 노력이 이루어지고 있다. 바이오매스는 광합성에 매우 중요한 요소인 클로로필(엽록소)의 총 함유량으로 추정할 수 있는데, 클로로필을 추정하기 위해서 다양한 식생지수들이 개발되었다. 식생지수들은 개발에 사용된 식생의 종류와 원격탐사 데이터에 따라 조금씩 차이를 가지고 있다. 하이퍼스펙트럴 영상은 다중분광 영상에 비하여 세분화된 각 파장대마다 물질에 따른 반사 및 흡수 특성이 다르기 때문에, 기존의 식생지수를 그대로 사용하기에 무리가 따른다. 본 연구는 항공기 탑재 하이퍼스펙트럴 영상을 이용하여 산림에 대한 바이오매스 추정을 위한 매개변수로 활용되는 적합한 식생지수는 무엇인지 평가하는 것을 목적으로 한다. 이를 위해 하이퍼스펙트럴 영상의 밴드 특성을 고려하여 다수의 식생지수 산출식 중 9개를 선정하고, SMA(spectral mixture analysis)를 통하여 대상지역의 산림을 대표하는 3개의 endmember를 추출하였다. 9개의 식생지수와 추출된 endmembers의 상관관계를 분석하였다. 상관분석 결과는 산림이 분포된 지역에서 Pearson 상관계수는 MTVI1과 TVI가 0.877의 상관계수를 가졌으며, 식생이 적고 토양의 분포가 확연한 지역에서는 MCARI가 0.9061로 매우 높은 상관계수를 보였다. 전반적으로 MTVI1과 TVI이 0.757의 동일한 상관계수를 가지며 식생에 대한 3개의 endmember를 가장 잘 설명하는 것으로 나타났다.