• Title/Summary/Keyword: Hyperspectral Data

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Application of EO-1 HYPERION Data to Classifying Geological Materials

  • Choe, E.Y.;Yoon, W.J.;Kang, M.K.;Kim, T.H.
    • Proceedings of the KSRS Conference
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    • 2003.11a
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    • pp.576-578
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    • 2003
  • Hyperspectral image divides VNIR region to over 200 bands which can show continuous spectrum with 10 nm spectral resolution. This property is useful in geology where a spectral feature which is decided by chemical compositions and crystalline structures is recorded well. While this field has been studied variously in foreign countries, the studies are in the early stage in Korea. In this study, characteristic materials associated with AMD were classified by using EO-1 HYPERION data which is a spaceborne hyperspectral image and topographical map and DEM and geochemical map were analyzed in conjunction with the image in order to examine that classified minerals are secondary minerals by AMD.

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Vicarious Calibration-based Robust Spectrum Measurement for Spectral Libraries Using a Hyperspectral Imaging System

  • Chi, Junhwa
    • Korean Journal of Remote Sensing
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    • v.34 no.4
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    • pp.649-659
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    • 2018
  • The aim of this study is to develop a protocol for obtaining spectral signals that are robust to varying lighting conditions, which are often found in the Polar regions, for creating a spectral library specific to those regions. Because hyperspectral image (HSI)-derived spectra are collected on the same scale as images, they can be directly associated with image data. However, it is challenging to find precise and robust spectra that can be used for a spectral library from images taken under different lighting conditions. Hence, this study proposes a new radiometric calibration protocol that incorporates radiometric targets with a traditional vicarious calibration approach to solve issues in image-based spectrum measurements. HSIs obtained by the proposed method under different illumination levels are visually uniform and do not include any artifacts such as stripes or random noise. The extracted spectra capture spectral characteristics such as reflectance curve shapes and absorption features better than those that have not been calibrated. The results are also validated quantitatively. The calibrated spectra are shown to be very robust to varying lighting conditions and hence are suitable for a spectral library specific to the Polar regions.

Analysis of vegetation change in Taehwa River basin using drone hyperspectral image and multiple vegetation indices (드론 초분광 영상과 다중 식생지수를 활용한 태화강 유역 식생변화 분석)

  • Kim, Yong-Suk
    • Journal of the Korean Society of Environmental Restoration Technology
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    • v.24 no.1
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    • pp.97-110
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    • 2021
  • Vegetation index information is an important figure that is used in many fields such as landscape architecture, urban planning, and environment. Vegetation may vary slightly in vegetation vitality depending on photosynthesis and chlorophyll content. In this study, a range of vegetation worth preserving in the Taehwa River water system was determined, and hyperspectral images of drones were acquired (August, October), and the results were presented through DVI(Normalized Defference Vegetation Index), EVI(Enhanced Vegetation Index), PRI(Photochemical Reflectance Index), ARI (Anthocyanin Reflectance Index) index analysis. In addition, field spectral data and VRS-GPS(Virtual Reference System-GPS) surveys were performed to ensure the quality and location accuracy of the spectral band. As a result of the analysis, NDVI and EVI showed low vegetation vitality in October, -0.165 and -0.085, respectively, and PRI and ARI increased to 0.011 and 7.588 in October, respectively. For general vegetation vitality, it was suggested that NDVI and EVI analysis were effectively performed, and PRI and ARI were thought to be effective in analyzing detailed characteristics of plants by spectral band. It is expected that it can be widely used for park design and landscape information modeling by using drone image information construction and vegetation information.

Research on the Applicability of Target-detection Methods for Land-based Hyperspectral Imaging

  • Qianghui Wang;Bing Zhou;Wenshen Hua;Jiaju Ying;Xun Liu;Lei Deng
    • Current Optics and Photonics
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    • v.8 no.3
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    • pp.282-299
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    • 2024
  • Target detection (TD) is a research hotspot in the field of hyperspectral imaging (HSI). Traditional TD methods often mine targets from HSIs under a single imaging condition, without considering the influence of imaging conditions. In fact, the spectra of ground objects in HSIs are uncertain and affected by the imaging conditions (weather, atmospheric, light, time, and other angle conditions including zenith angle). Hyperspectral data changes under different imaging conditions. Therefore, the detection result for a single imaging condition cannot accurately reflect the effectiveness of the detection method used. It is necessary to analyze the performance of various detection methods under different imaging conditions, to find a more applicable detection method. In this paper, we study the performance of TD methods under various land-based imaging conditions. We first summarize classical TD methods and evaluation methods. Then, the detection effects under various imaging conditions are analyzed. Finally, the concepts of the stability coefficient (SC) and effective area under the curve (EAUC) are proposed to comprehensively evaluate the applicability of detection methods under land-based imaging conditions, in terms of both detection accuracy and stability. This is conducive to our selection of detection methods with better applicability in land-based contexts, to improve detection accuracy and stability.

Hyperspectral Image Analysis Technology Based on Machine Learning for Marine Object Detection (해상 객체 탐지를 위한 머신러닝 기반의 초분광 영상 분석 기술)

  • Sangwoo Oh;Dongmin Seo
    • Journal of the Korean Society of Marine Environment & Safety
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    • v.28 no.7
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    • pp.1120-1128
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    • 2022
  • In the event of a marine accident, the longer the exposure time to the sea increases, the faster the chance of survival decreases. However, because the search area of the sea is extremely wide compared to that of land, marine object detection technology based on the sensor mounted on a satellite or an aircraft must be applied rather than ship for an efficient search. The purpose of this study was to rapidly detect an object in the ocean using a hyperspectral image sensor mounted on an aircraft. The image captured by this sensor has a spatial resolution of 8,241 × 1,024, and is a large-capacity data comprising 127 spectra and a resolution of 0.7 m per pixel. In this study, a marine object detection model was developed that combines a seawater identification algorithm using DBSCAN and a density-based land removal algorithm to rapidly analyze large data. When the developed detection model was applied to the hyperspectral image, the performance of analyzing a sea area of about 5 km2 within 100 s was confirmed. In addition, to evaluate the detection accuracy of the developed model, hyperspectral images of the Mokpo, Gunsan, and Yeosu regions were taken using an aircraft. As a result, ships in the experimental image could be detected with an accuracy of 90 %. The technology developed in this study is expected to be utilized as important information to support the search and rescue activities of small ships and human life.

Analysis of Potential on Measurement of SO2 and NO2 using Radiative Transfer Model and Hyperspectral Sensor (복사전달모델과 초분광센서를 이용한 아황산가스와 이산화질소의 농도 측정 가능성 분석)

  • Shin, Jung-il;Kim, Ik-Jae;Choi, Min-Jae;Lim, Seong-Ha
    • Journal of the Korea Academia-Industrial cooperation Society
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    • v.19 no.6
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    • pp.658-663
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    • 2018
  • Current measuring methods for air quality are based on ground measurement networks and satellite data. New methods of collecting evidence with advanced sensors are needed because current methods have limitations in collecting evidence for the illegal emission of air pollutants at narrow areas or specific sites. This study analyzed the possibility of using an ultraviolet hyperspectral sensor to measure the concentration of nitrogen dioxide and sulfur dioxide. Two types of spectra were used: simulated spectra for gases with various concentrations using a radiative transfer model and observed spectra for each gas for a concentration. To understand the possibility of using a hyperspectral sensor, the differences between the simulated spectra and the observed spectra were analyzed, and the variation of simulated spectra were then analyzed according to the concentration. The results showed good agreement between observed spectra and simulated spectra. In addition, the absorption depth at specific wavelengths in the simulated spectra had a very strong correlation with the gas concentration. The gas concentration could be estimated using the hyperspectral sensor. In the future, validation would be needed to estimate the gas concentration through observations of various concentrations of gases using a hyperspectral sensor.

A Study on the Unsupervised Classification of Hyperion and ETM+ Data Using Spectral Angle and Unit Vector

  • Kim, Dae-Sung;Kim, Yong-Il;Yu, Ki-Yun
    • Korean Journal of Geomatics
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    • v.5 no.1
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    • pp.27-34
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    • 2005
  • Unsupervised classification is an important area of research in image processing because supervised classification has the disadvantages such as long task-training time and high cost and low objectivity in training information. This paper focuses on unsupervised classification, which can extract ground object information with the minimum 'Spectral Angle Distance' operation on be behalf of 'Spectral Euclidian Distance' in the clustering process. Unlike previous studies, our algorithm uses the unit vector, not the spectral distance, to compute the cluster mean, and the Single-Pass algorithm automatically determines the seed points. Atmospheric correction for more accurate results was adapted on the Hyperion data and the results were analyzed. We applied the algorithm to the Hyperion and ETM+ data and compared the results with K-Means and the former USAM algorithm. From the result, USAM classified the water and dark forest area well and gave more accurate results than K-Means, so we believe that the 'Spectral Angle' can be one of the most accurate classifiers of not only multispectral images but hyperspectral images. And also the unit vector can be an efficient technique for characterizing the Remote Sensing data.

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A New Method for Hyperspectral Data Classification

  • Dehghani, Hamid.;Ghassemian, Hassan.
    • Proceedings of the KSRS Conference
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    • 2003.11a
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    • pp.637-639
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    • 2003
  • As the number of spectral bands of high spectral resolution data increases, the capability to detect more detailed classes should also increase, and the classification accuracy should increase as well. Often, it is impossible to access enough training pixels for supervise classification. For this reason, the performance of traditional classification methods isn't useful. In this paper, we propose a new model for classification that operates based on decision fusion. In this classifier, learning is performed at two steps. In first step, only training samples are used and in second step, this classifier utilizes semilabeled samples in addition to original training samples. At the beginning of this method, spectral bands are categorized in several small groups. Information of each group is used as a new source and classified. Each of this primary classifier has special characteristics and discriminates the spectral space particularly. With using of the benefits of all primary classifiers, it is made sure that the results of the fused local decisions are accurate enough. In decision fusion center, some rules are used to determine the final class of pixels. This method is applied to real remote sensing data. Results show classification performance is improved, and this method may solve the limitation of training samples in the high dimensional data and the Hughes phenomenon may be mitigated.

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Evaluation of Firmness and Sweetness Index of Tomatoes using Hyperspectral Imaging

  • Rahman, Anisur;Faqeerzada, Mohammad Akbar;Joshi, Rahul;Cho, Byoung-Kwan
    • Proceedings of the Korean Society for Agricultural Machinery Conference
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    • 2017.04a
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    • pp.44-44
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    • 2017
  • The objective of this study was to evaluate firmness, and sweetness index (SI) of tomatoes (Lycopersicum esculentum) by using hyperspectral imaging (HSI) in the range of 1000-1400 nm. The mean spectra of the 95 matured tomato samples were extracted from the hyperspectral images, and the reference firmness and sweetness index of the same sample were measured and calibrated with their corresponding spectral data by partial least squares (PLS) regression with different preprocessing method. The results showed that the regression model developed by PLS regression based on Savitzky-Golay (S-G) second-derivative preprocessed spectra resulted in better performance for firmness, and SI of tomatoes compared to models developed by other preprocessing methods, with correlation coefficients (rpred) of 0.82, and 0.74 with standard error of prediction (SEP) of 0.86 N, and 0.63 respectively. Then, the feature wavelengths were identified using model-based variable selection method, i.e., variable important in projection (VIP), resulting from the PLS regression analyses and finally chemical images were derived by applying the respective regression coefficient on the spectral image in a pixel-wise manner. The resulting chemical images provided detailed information on firmness, and sweetness index (SI) of tomatoes. Therefore, these research demonstrated that HIS technique has a potential for rapid and non-destructive evaluation of the firmness and sweetness index of tomatoes.

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Unsupervised Change Detection of Hyperspectral images Using Range Average and Maximum Distance Methods (구간평균 기법과 직선으로부터의 최대거리를 이용한 초분광영상의 무감독변화탐지)

  • Kim, Dae-Sung;Kim, Yong-Il;Pyeon, Mu-Wook
    • Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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    • v.29 no.1
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    • pp.71-80
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    • 2011
  • Thresholding is important step for detecting binary change/non-change information in the unsupervised change detection. This study proposes new unsupervised change detection method using Hyperion hyperspectral images, which are expected with data increased demand. A graph is drawn with applying the range average method for the result value through pixel-based similarity measurement, and thresholding value is decided at the maximum distance point from a straight line. The proposed method is assessed in comparison with expectation-maximization algorithm, coner method, Otsu's method using synthetic images and Hyperion hyperspectral images. Throughout the results, we validated that the proposed method can be applied simply and had similar or better performance than the other methods.