• 한국환경과학회지
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• 제30권2호
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• pp.173-184
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• 2021

Ecological disturbance plants distributed throughout the country are causing a lot of damage to us directly or indirectly in terms of ecology, economy and health. These plants are not easy to manage and remove because they have a strong fertility, and it is very difficult to express them quantitatively. In this study, drone hyperspectral sensor data and Field spectroradiometer were acquired around the experimental area. In order to secure the quality accuracy of the drone hyperspectral image, GPS survey was performed, and a location accuracy of about 17cm was secured. Spectroscopic libraries were constructed for 7 kinds of plants in the experimental area using a Field spectroradiometer, and drone hyperspectral sensors were acquired in August and October, respectively. Spectral data for each plant were calculated from the acquired hyperspectral data, and spectral angles of 0.08 to 0.36 were derived. In most cases, good values of less than 0.5 were obtained, and Ambrosia trifida and Lactuca scariola, which are common in the experimental area, were extracted. As a result, it was found that about 29.6% of Ambrosia trifida and 31.5% of Lactuca scariola spread in October than in August. In the future, it is expected that better results can be obtained for the detection of ecosystem distribution plants if standardized indicators are calculated by constructing a precise spectral angle standard library based on more data.

• 한국지능시스템학회논문지
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• 제20권4호
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• pp.534-540
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• 2010

고차원 특성과 높은 상관성은 하이퍼스펙트럴 데이터의 주요 특징이다. LDA와 그 변형 선형 투사 방법들이 고차원 스펙트럴 정보로부터 저차원의 특징을 추출하는데 사용되었다. LDA는 학습 데이터가 적은 경우 흔히 발생하는 과대적합으로 인해 일반화 성능이 낮아지는 문제가 발생하는데, 이를 완화하기 위하여 LDA 규칙화(regularization) 방법들이 제시되었다. 그 중, 평탄도(smoothness) 제약에 기반한 LDA 규칙화 기법은 높은 상관성을 갖는 하이퍼스펙트럴 데이터의 특성에 적합한 특징 추출 기법이다. 본 논문에서는 하이퍼스펙트럴 데이터 분류에서 평탄도 제약을 갖는 LDA 규칙화 방법을 소개하고 학습 데이터 조건에 따른 성능을 실험적으로 분석한다. 또한, 분류 성능의 향상을 위한 스펙트럴 정보와 공간적 정보의 상관성을 함께 활용하는 이중 평탄도 LDA 규칙화 기법을 제시한다.

• 한국멀티미디어학회논문지
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• 제16권1호
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• pp.19-28
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• 2013

In this research, we presented an effective clustering method based on ICA for the analysis of huge Raman hyperspectral dental data. The hyperspectral dataset captured by HR800 micro Raman spectrometer at UMKC-CRISP(University of Missouri-Kansas City Center for Research on Interfacial Structure and Properties), has 569 local points. Each point has 1,005 hyperspectal dentin data. We compared the clustering effectiveness and the clustering time for the case of using all dataset directly and the cases of using the scores after PCA and ICA. As the result of experiment, the cases of using the scores after PCA and ICA showed, not only more detailed internal dentin information in the aspect of medical analysis, but also about 7~19 times much shorter processing times for clustering. ICA based approach also presented better performance than that of PCA, in terms of the detailed internal information of dentin and the clustering time. Therefore, we could confirm the effectiveness of ICA for the analysis of Raman hyperspectral dental data.

• 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 2004년도 Proceedings of ISRS 2004
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• pp.10-13
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• 2004

Hyperspectral data has a great advantage to classify various surface materials that are spectrally similar. In this study, we attempted to classify man-made materials in urban area using Hyperion data. Hyperion imagery of Seoul was initially processed to minimize radiometric distortions caused by sensor and atmosphere. Using color aerial photographs. we defined seven man-made surfaces (concrete, asphalt road. railroad, buildings, roof, soil, shadow) for the classification in Seoul. The hyperspectral data showed the potential to identify those manmade materials that were difficult to be classified by multispectral data. However. the classification of road and buildings was not quite satisfactory due to the relatively low spatial resolution of Hyperion image. Further, the low radiometric quality of Hyperion sensor was another limitation for the application in urban area.

• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 2006년도 Proceedings of ISRS 2006 PORSEC Volume II
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• pp.619-622
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• 2006

Atmospheric correction is one of critical procedures to extract quantitative information related to biophysical variables from hyperspectral data. In this study, we attempted to generate the water vapor contents image from hyperspectral data itself and developed the atmospheric correction algorithm for EO-1 Hyperion data using pre-calculated atmospheric look-up-table (LUT) for fast processing. To apply the new atmospheric correction algorithm, Hyperion data acquired June 3, 2001 over Seoul area is used. Reflectance spectrums of various targets on atmospheric corrected Hyperion reflectance images showed the general spectral pattern although there must be further development to reduce the spectral noise.

• 한국공간정보시스템학회:학술대회논문집
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• 한국공간정보시스템학회 2007년도 GIS 공동춘계학술대회 논문집
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• pp.139-144
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• 2007

Recording data in hundreds of narrow contiguous spectral intervals, hyperspectral images have provided the opportunity to detect small differences in material composition. But a limitation of a hyperspectral image is the signal to noise ratio (SNR) lower than that of a multispectral image. This paper presents the efficiency of Spectral Similarity Scale (SSS) in change detection of hyperspectral image and the experiment was performed with Hyperion data. SSS is an algorithm that objectively quantifies differences between reflectance spectra in both magnitude and direction dimensions. The thresholds for detecting the change area were determined through Expectation-Maximization (EM) algorithm. The experimental result shows that the SSS algorithm and EM algorithm are efficient enough to be applied to the unsupervised change detection of hyperspectral images.

• 한국군사과학기술학회지
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• 제13권2호
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• pp.328-335
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• 2010

A hyperspectral imaging spectrometer has shown significant advantages in performance over other existing ones for remote sensing applications. It can collect hundreds of narrow, adjacent spectral bands for each image, which provides a wealth of information on unique spectral characteristics of objects. We have developed a compact hyperspectral imaging system that successively shows high spatial and spectral resolutions and fast data processing performance. In this paper, we present an overview of the hyperspectral imaging system including the strucure of geometrical optics and several image processing schemes such as wavelength calibration and noise reduction for image data on Visible and Near-Infrared(VNIR) and Shortwave-Infrared(SWIR) band.

• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 2004년도 Proceedings of ISRS 2004
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• pp.570-573
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• 2004

We proposed a new system to collect hyperspectral data using a general cargo crane. The system has advantageous characteristics for precise data collection and analysis. In order to evaluate the validity, we performed the system on actual rice paddy field and analyzed the observed data. On the process, we could successfully extract the pure vegetation spectrum with high stability. The data taken by the system are useful for detail analysis among fields. In future development, it can be used for obtaining the grand truth for calibrating satellite or aerial hyperspectral images.

• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 2006년도 Proceedings of ISRS 2006 PORSEC Volume II
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• pp.736-739
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• 2006

While hyperspectral data are very rich in information, their processing poses several challenges such as computational requirements, noise removal and relevant information extraction. In this paper, the application of advanced scale-space filtering to selected hyperspectral bands was investigated. In particular, a pre-processing tool, consisting of anisotropic diffusion and morphological leveling filtering, has been developed, aiming to an edge-preserving smoothing and simplification of hyperspectral data, procedures which are of fundamental importance during feature extraction and object detection. Two scale space parameters define the extent of image smoothing (anisotropic diffusion iterations) and image simplification (scale of morphological levelings). Experimental results demonstrated the effectiveness of the developed scale space filtering for the enhancement and smoothing of hyperspectral remote sensing data and their advantage against watershed over-segmentation problems and edge detection.