• Journal of Korean Society for Geospatial Information Science
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• v.22 no.3
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• pp.155-160
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• 2014

This paper deals with comparison of classification accuracy between three land cover classification results having difference in resolution and they were classified with eight classes including building, road, forest, etc. Airborne hyperspectral image used in this study was acquired at 1000m, 2000m, 3000m elevation and had 24 bands(0.5m spatial resolution), 48 bands(1.0m), 96 bands(1.5m). Assessment of classification accuracy showed that the classification using 48 bands hyperspectral image had outstanding result as compared with other images. For using hyperspectral image, it was verified that 1m spatial resolution image having 48 bands was appropriate to classify land cover and qualitative improvement is expected in thematic map creation using airborne hyperspectral image.

• Aerospace Engineering and Technology
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• v.7 no.1
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• pp.216-222
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• 2008

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-l 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 Korea Water Resources Association Conference
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• 2016.05a
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• pp.101-101
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• 2016

하천원격탐사는 원격탐사의 하위 개념으로서 계측하고자 하는 대상인 하천이나 호소 수체에서 발생하는 빛의 반사, 복사 또는 방출되는 양을 획득하고 분석하여 수리량, 지형 등 하천 조사에 활용하는 기법이다. 일반적으로 원격탐사는 주로 위성영상 자료를 활용하여 수행되어 자료취득비용이 고가이고 해외 위성자료에 의존하여 시공간적인 해상도가 매우 낮아 유역에 비해 공간적인 규모가 작고 변동 시간이 짧은 하천에 적용하는 데 한계가 있어 왔다. 또한, 단순한 사진촬영으로 도출할 수 있는 정보에 한계가 있고 자료를 저장 및 분석할 수 있는 기법도 부족하여 하천조사에 원격탐사를 활용한 사례가 드물었다. 그러나, 최근 드론과 같은 운반체 기술이 획기적으로 개선되고 있고 다양한 영상촬영장비의 개발과 IT기술의 발전으로 인해 위성영상에 비해 시공간적 해상도가 매우 정밀한 자료를 저렴한 비용으로 획득 가능해졌다. 또한, 매우 조밀한 파장대로 세분된 빛의 세기를 측정할 수 있는 초분광 영상을 이용한 원격탐사기법도 하천과 같은 좁은 영역에 적용이 가능해졌다. 초분광영상은 가시광선 외에 자외선과 적외선 영역에 해당하는 반사광을 200개 이상의 조밀한 파장대로 나누어 측정할 수 있어 수리량, 하상, 식생 등 하천 수체와 관련된 정보를 조사할 가능성이 증가하였다. 본 연구에서는 하천 수체에서 취득한 초분광 영상을 이용하여 하천특성과의 상관관계를 규명하고 이를 통해 초분광 영상 기반의 하천특성 계측 기법을 개발하고자 하였다. 드론과 같은 항공영상에 적용하기 전에, 우선 지상에서 측정된 초분광 영상과 하상재료 및 수심과의 상관관계를 규명하여 초분광 영상의 하천조사로의 사용 가능성을 점검해 보았다. 폭 10m, 수심 1m의 저수심의 소하천에 적용한 결과, 초분광 영상의 표준화 및 패턴 분석을 통해 수중에 위치한 하상재료를 구분할 수 있었고 주성분분석 등을 통해 수심과 상관성도 일부 도출되어 하천조사에 초분광영상이 활용될 수 있음을 확인하였다.

• Korean Journal of Remote Sensing
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• v.21 no.4
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• pp.341-369
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• 2005

Hyperspectral images have emerged as a new and promising remote sensing data that can overcome the limitations of existing optical image data. This study was designed to provide a comprehensive review on definition, data processing methods, and applications of hyperspectral data. Various types of airborne, spaceborne, and field hyperspectral image sensors were surveyed from the available literatures and internet search. To understand the current status of hyperspectral remote sensing technology and research development, we collected several hundreds research papers from international journals (IEEE Transactions on Geoscience and Remote Sensing, International Journal of Remote Sensing, Remote Sensing of Environment and AVIRIS Workshop Proceedings), and categorized them by sensor types, data processing techniques, and applications. Although several hyperspectral sensors have been developing, AVIRIS has been a primary data source that the most hyperspectral remote sensing researches were relied on. Since hyperspectral data have very large data volume with many spectral bands, several data processing techniques that are particularly oriented to hyperspectral data have been developed. Although atmospheric correction, spectral mixture analysis, and spectral feature extraction are among those processing techniques, they are still in experimental stage and need further refinement until the fully operational adaptation. Geology and mineral exploration were major application in early stage of hyperspectral sensing because of the distinct spectral features of rock and minerals that could be easily observed with hyperspectral data. The applications of hyperspectral sensing have been expanding to vegetation, water resources, and military areas where the multispectral sensing was not very effective to extract necessary information.

• Spatial Information Research
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• v.23 no.2
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• pp.31-38
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• 2015

Remote sensing data is used to increasing efficiency on benthic cover type survey. Satellite and aerial imagery has variance of reflectance by water column effect even if bottom is consisted with same cover type and condition. This study tried to analyze advances of surveying extent and accuracy through water column correction of CASI-1500 hyperspectral image. Study area is coast of Gangneung city, South Korea where benthic environment is rapidly changing with bleaching of coral reef. Water column correction coefficient was estimated using regression models between water reflectance ($R_W$) and depth for sand bottom then the coefficients were applied to whole image. The results shows that expanded interpretable depth from 6-7m to 15m and decreased variation of reflectance by depth. Additionally, water column corrected reflectance image shows 13%p increased accuracy on benthic cover type classification.

• Proceedings of the KSRS Conference
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• 2006.03a
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• pp.206-209
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• 2006

강우-유출 모델링에 있어 토지피복/이용 상태는 중요한 입력변수로 사용되지만 기존의 다중분광영상을 이용한 분류에는 한계가 있다. 본 연구에서는 위성탑재 초분광영상인 Hyperion 영상의 분광혼합분석을 통해 도시지역의 수문학적 토지피복/이용 분류를 실시하였으며 분류등급의 기준은 널리 사용되고 있는 SCS 토지피복/이용 등급을 이용하였다. 정확도분석을 위해 항공사진을 디지타이징하여 불투수면적의 비율을 비교하였으며 분광혼합분석 결과와 항공사진에서 불투수면적의 비율은 유사하게 나타났다. 그러나 SCS의 분류등급은 미국을 기준으로 개발되었기 때문에 임계치를 이용하여 분류된 등급과 실제 항공사진판독의 결과가 일부 다르게 나타나는 것을 알 수 있었다.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.31 no.2
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• pp.165-172
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• 2013

In this paper, high-quality mosaic image is generated by high-resolution hyperspectral strip images using scale-invariant feature transform (SIFT) algorithm, which is one of the representative image matching methods. The experiments are applied to AISA Eagle images geo-referenced by using GPS/INS information acquired when it was taken on flight. The matching points between three strips of hyperspectral images are extracted using SIFT method, and the transformation models between images are constructed from the points. Mosaic image is, then, generated using the transformation models constructed from corresponding images. Optimal band appropriate for the matching point extraction is determined by selecting representative bands of hyperspectral data and analyzing the matched results based on each band. Mosaic image generated by proposed method is visually compared with the mosaic image generated from initial geo-referenced AISA hyperspectral images. From the comparison, we could estimate geometrical accuracy of generated mosaic image and analyze the efficiency of our methodology.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.32 no.spc4_2
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• pp.421-430
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• 2014

Aerial imagery, Satellite imaging and Hyperspectral imaging(HSI) are widely using at mapping those of agriculture, woodland, waters shoreline, and land cover, but are rarely applied at the Cadastre. There are many study cases on the overlay of aerial imagery and satellite imaging with Cadastral Map and the upgrade and registration of Cadastre' Land Category, however, reported as successful. Therefore, this study has been aimed to show the use of the Hyperspectral Imaging technology for Cadastre, especially for the land category. Also, the HSI sensor could function as a geospatial acquisition tool for error checks of the existed land categories, and as a helpful tool for acquiring the attributes and spatial data, such as the agriculture, soil, and vegetation, etc. This result indicates that HSI sensor can implement the Multipurpse Cadastre(MPC) by fusing with the cadastral information.

• Journal of Cadastre & Land InformatiX
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• v.44 no.2
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• pp.103-112
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• 2014

The objective of this study is to suggest the possibility on land cover classification using hyperspectal imagery on area which includes lands and waters. After atmospheric correction as a preprocessing work was conducted on hyperspectral imagery acquired by airborne hyperspectral sensor CASI-1500, the effect of atmospheric correction to a few land cover class in before and after atmospheric correction was compared and analyzed. As the result of accuracy of land cover classification by highspectral imagery using reference data as airphoto and digital topographic map, maximum likelihood method represented overall accuracy as 67.0% and minimum distance method showed overall accuracy as 52.4%. Also product accuracy of land cover classification on road, dry field and green house, but that on river, forest, grassland showed low because the area of those was composed of complex object. Therefore, the study needs to select optimal band to classify specific object and to construct spectral library considering spectral characteristics of specific object.

• Korean Journal of Remote Sensing
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• v.30 no.1
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• pp.25-36
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• 2014

Multispectral image classification of individual tree species is often difficult because of the spectral similarity among species. In this study, we attempted to analyze the suitability of hyperspectral image to classify coniferous tree species. Several image sets and classification methods were applied and the classification results were compared with the ones from multispectral image. Two airborne hyperspectral images (AISA, CASI) were obtained over the study area in the Gwangneung National Forest. For the comparison, ETM+ multispectral image was simulated using hyperspectral images as to have lower spectral resolution. We also used the transformed hyperspectral data to reduce the data volume for the classification. Three supervised classification schemes (SAM, SVM, MLC) were applied to thirteen image sets. In overall, hyperspectral image provides higher accuracies than multispectral image to discriminate coniferous species. AISA-dual image, which include additional SWIR spectral bands, shows the best result as compared with other hyperspectral images that include only visible and NIR bands. Furthermore, MNF transformed hyperspectral image provided higher classification accuracies than the full-band and other band reduced data. Among three classifiers, MLC showed higher classification accuracy than SAM and SVM classifiers.