• Proceedings of the Korean Society of Surveying, Geodesy, Photogrammetry, and Cartography Conference
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• 2006.04a
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• pp.165-169
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• 2006

Purpose of this research is estimation of water depth by hyperspectral remote sensing in area that access of ship is difficult This research used EO-1 Hyperion satellite imagery. Atmospheric and geometric correction is executed. Compress of band used MNF transforms. Diffuse Attenuation Coefficient of target area is decided in imagery for water depth estimation. Determination of Emdmember in pixel is using Linear Spectral Unmixing techniques. Water depth estimated using this result.

• Proceedings of the KSRS Conference
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• 2006.03a
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• pp.223-227
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• 2006

최근 위성기술의 발전은 크게 두 가지 방향으로 진행되고 있다. 하나는 고해상도(High Resolution)라는 말로 대표되는 공간해상도(Spatial Resolution)의 향상이고, 다른 하나는 초분광(Hyperspectral)으로 대표되는 분광해상도(Spectral Resolution)의 향상이다. 특히 초분광영상(Hyperspectral Image)은 지상피복 및 대상물에 대해 실험실에서 얻을 수 있을 정도의 연속적이고 좁은 파장 간격의 분광정보를 제공하고 있어, 기존에 사용하던 다중분광영상(Multispectral Image) 보다 많은 양의 정보를 사용자에게 제공한다. 본 논문에서는 다중분광영상과 초분광영상의 분광 정보를 활용한 영상분류능력을 비교분석하고 그 결과를 평가하였다. 분석결과는 다중분광영상에서 식별이 어려웠던 초지, 농지, 나지에 대한 분석 능력이 초분광영상에서 상당히 향상됨으로써 감독분류에서 약 20% 정도의 정확도 향상을 가져왔으며, 무감독분류의 경우에는 미소한 차이로 그 정확도가 향상된다는 것이다. 이런 결과는 향후 초분광영상의 토지 피복분류 및 대상물 탐사에 긍정적인 활용 방안을 제시할 수 있음을 알려주고 있다.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.35 no.4
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• pp.291-302
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• 2017

Radiometric calibration is a prerequisite to quantitative remote sensing, and its accuracy has a direct impact on the reliability and accuracy of the quantitative application of remotely sensed data. This paper presents absolute radiometric calibration of the KOMPSAT-3 (KOrea Multi Purpose SATellite-3) and cross calibration using the Landsat-8 OLI (Operational Land Imager). Absolute radiometric calibration was performed using a reflectance-based method. Correlations between TOA (Top Of Atmosphere) radiances and the spectral band responses of the KOMPSAT-3 sensors in Goheung, South Korea, were significant for multispectral bands. A cross calibration method based on the Landsat-8 OLI was also used to assess the two sensors using near simultaneous image pairs over the Libya-4 PICS (Pseudo Invariant Calibration Sites). The spectral profile of the target was obtained from EO-1 (Earth Observing-1) Hyperion data over the Libya-4 PICS to derive the SBAF (Spectral Band Adjustment Factor). The results revealed that the TOA radiance of the KOMPSAT-3 agree with Landsat-8 within 5.14% for all bands after applying the SBAF. The radiometric coefficient presented here appears to be a good standard for maintaining the optical quality of the KOMPSAT-3.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.26 no.5D
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• pp.885-894
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• 2006

The purpose of this study is focused on the development of the effective classification technique using ultra multiband of hyperspectral image. This study suggests the classification technique using canonical correlation analysis, one of multivariate statistical analysis in hyperspectral image classification. High accuracy of classification result is expected for this classification technique as the number of bands increase. This technique is compared with Maximum Likelihood Classification(MLC). The hyperspectral image is the EO1-hyperion image acquired on September 2, 2001, and the number of bands for the experiment were chosen at 30, considering the band scope except the thermal band of Landsat TM. We chose the comparing base map as Ground Truth Data. We evaluate the accuracy by comparing this base map with the classification result image and performing overlay analysis visually. The result showed us that in MLC's case, it can't classify except water, and in case of water, it only classifies big lakes. But Canonical Correlation Classification (CCC) classifies the golf lawn exactly, and it classifies the highway line in the urban area well. In case of water, the ponds that are in golf ground area, the ponds in university, and pools are also classified well. As a result, although the training areas are selected without any trial and error, it was possible to get the exact classification result. Also, the ability to distinguish golf lawn from other vegetations in classification classes, and the ability to classify water was better than MLC technique. Conclusively, this CCC technique for hyperspectral image will be very useful for estimating harvest and detecting surface water. In advance, it will do an important role in the construction of GIS database using the spectral high resolution image, hyperspectral data.