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  • Title/Summary/Keyword: Hyperspectral Data

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Analysis of Spectral Reflectance Characteristics Using Hyperspectral Sensor at Diverse Phenological Stages of Soybeans

  • Go, Seung-Hwan;Park, Jin-Ki;Park, Jong-Hwa
    • Korean Journal of Remote Sensing
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    • v.37 no.4
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    • pp.699-717
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    • 2021
  • South Korea is pushing for the advancement of crop production technology to achieve food self-sufficiency and meet the demand for safe food. A medium-sized satellite for agriculture is being launched in 2023 with the aim of collecting and providing information on agriculture, not only in Korea but also in neighboring countries. The satellite is to be equipped with various sensors, though reference data for ground information are lacking. Hyperspectral remote sensing combined with 1st derivative is an efficient tool for the identification of agricultural crops. In our study, we develop a system for hyperspectral analysis of the ground-based reflectance spectrum, which is monitored seven times during the cultivation period of three soybean crops using a PSR-2500 hyperspectral sensor. In the reflection spectrum of soybean canopy, wavelength variations correspond with stages of soybean growths. The spectral reflection characteristics of soybeans can be divided according to growth into the vegetative (V)stage and the reproductive (R)stage. As a result of the first derivative analysis of the spectral reflection characteristics, it is possible to identify the characteristics of each wavelength band. Using our developed monitoring system, we observed that the near-infrared (NIR) variation was largest during the vegetative (V1-V3) stage, followed by a similar variation pattern in the order of red-edge and visible. In the reproductive stage (R1-R8), the effect of the shape and color of the soybean leaf was reflected, and the pattern is different from that in the vegetative (V) stage. At the R1 to R6 stages, the variation in NIR was the largest, and red-edge and green showed similar variation patterns, but red showed little change. In particular, the reflectance characteristics of the R1 stage provides information that could help us distinguish between the three varieties of soybean that were studied. In the R7-R8 stage, close to the harvest period, the red-edge and NIR variation patterns and the visible variation patterns changed. These results are interpreted as a result of the large effects of pigments such as chlorophyll for each of the three soybean varieties, as well as from the formation and color of the leaf and stem. The results obtained in this study provide useful information that helps us to determine the wavelength width and range of the optimal band for monitoring and acquiring vegetation information on crops using satellites and unmanned aerial vehicles (UAVs)

Sub-Pixel Analysis of Hyperspectral Image Using Linear Spectral Mixing Model and Convex Geometry Concept

  • Kim, Dae-Sung;Kim, Yong-Il;Lim, Young-Jae
    • Korean Journal of Geomatics
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    • v.4 no.1
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    • pp.1-8
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    • 2004
  • In the middle-resolution remote sensing, the Ground Sampled Distance (GSD) that the detector senses and samples is generally larger than the actual size of the objects (or materials) of interest, and so several objects are embedded in a single pixel. In this case, as it is impossible to detect these objects by the conventional spatial-based image processing techniques, it has to be carried out at sub-pixel level through spectral properties. In this paper, we explain the sub-pixel analysis algorithm, also known as the Linear Spectral Mixing (LSM) model, which has been experimented using the Hyperion data. To find Endmembers used as the prior knowledge for LSM model, we applied the concept of the convex geometry on the two-dimensional scatter plot. The Atmospheric Correction and Minimum Noise Fraction techniques are presented for the pre-processing of Hyperion data. As LSM model is the simplest approach in sub-pixel analysis, the results of our experiment is not good. But we intend to say that the sub-pixel analysis shows much more information in comparison with the image classification.

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Development and Verification of the Compact Airborne Imaging Spectrometer System

  • Lee, Kwang-Jae;Yong, Sang-Soon;Kim, Yong-Seung
    • Korean Journal of Remote Sensing
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    • v.24 no.5
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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.

Development of AI oxygen temperature measurement technology using hyperspectral optical visualization technology (초분광 광학가시화 기술을 활용한 인공지능 산소온도 측정기술 개발)

  • Jeong Hun Lee;Bo Ra Kim;Seung Hun Lee;Joon Sik Kim;Min Yoon;Gyeong Rae Cho
    • Journal of the Korean Society of Visualization
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    • v.21 no.1
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    • pp.103-109
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    • 2023
  • This research developed a measurement technique that can measure the oxygen temperature inside a high temperature furnace. Instead of measuring only changes in frequency components within a small range used in the existing variable laser absorption spectroscopy, laser spectroscopy technology was used to spread out wavelength of the light source passing through the gas Based on a total of 20,000 image data, research was conducted to predict the temperature of a high-temperature furnace using CNN with black and white images in the form of spectral bands by temperature of 25 to 800 degrees. The optimal model was found through Hyper parameter optimization, R2 score is 0.89, and the accuracy of the test data is 88.73%. Based on this research, it is expected that concentration measurement and air-fuel ratio control technology can be applied.

A Study on the Spectral Information and Reflectance Characteristic of Levee Crack (제방 균열의 분광정보 및 반사율 특성에 관한 연구)

  • Kim, Jong-Tae;Lee, Chang-Hun;Kang, Joon-Gu
    • Journal of the Korea Academia-Industrial cooperation Society
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    • v.21 no.9
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    • pp.17-24
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    • 2020
  • This study examined the spectral information and reflectance of cracks of an embankment with drone-based hyperspectral imagery for crack detection. A Nano-Hyperspec mounted on a drone was used as a sensor, and hyperspectral videos of different intensities of illumination of the cracks on the embankment located in the downstream of Andong-Dam were obtained. An analysis of the data value of the illumination and peak data-value, the coefficients of determination were calculated to be 0.9864 of the uncracked areas and 0.9851 of the cracked area. The reflectance of each area showed a similar value and pattern, regardless of the intensity of illumination. This result may have occurred because the reference values of the white reference as the calculation criteria of reflectance varied according to the intensity of illumination. The reflectance at the cracked area was 5.65% lower in visible light and 4.58% lower in near-infrared light than that at the uncracked area. The detection of cracks may offer more precise results in further studies when the gimbal direction and camera angles of the drone are calibrated. Because hyperspectral imagery enables the detection of crack depths and types of clay minerals, which are difficult to identify in general RGB imagery, it can serve as a preemptive measure for evaluating the embankment stability.

Detection of Small Green Space in an Urban Area Using Airborne Hyperspectral Imagery and Spectral Angle Mapper (분광각매퍼 기법을 적용한 항공기 탑재 초분광영상의 소규모 녹지공간 탐지)

  • Kim, Tae-Woo;Choi, Don-Jeong;We, Gwang-Jae;Suh, Yong-Cheol
    • Journal of the Korean Association of Geographic Information Studies
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    • v.16 no.2
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    • pp.88-100
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    • 2013
  • Urban green space is one of most important aspects of urban infrastructure for improving the quality of life of city dwellers as it reduces the heat island effect and is used for recreation and relaxation. However, no systematic management of urban green space has been introduced in Korea as past practices focused on efficient development. A way to calculate the amount of green space needed to complement an urban area must be developed to preserve urban green space and to determine 'regulations determining the total amount of greenery'. In recent years, various studies have quantified urban green space and infrastructure using remotely sensed data. However, it is difficult to detect a myriad small green spaces in a city effectively when considering the spatial resolution of the data used in existing research. In this paper, we quantified small urban green spaces using CASI-1500 hyperspectral imagery. We calculated MCARI, a vegetation index for hyperspectral imagery, to evaluate the greenness of small green spaces. In addition, we applied image-classification methods, including the ISODATA algorithm and Spectral Angle Mapper, to detect small green spaces using supervised and unsupervised classifications. This could be used to categorize land-cover into four classes: unclassified, impervious, suspected green, and vegetation green.

Vicarious Radiometric Calibration of RapidEye Satellite Image Using CASI Hyperspectral Data (CASI 초분광 영상을 이용한 RapidEye 위성영상의 대리복사보정)

  • Chang, An Jin;Choi, Jae Wan;Song, Ah Ram;Kim, Ye Ji;Jung, Jin Ha
    • Journal of Korean Society for Geospatial Information Science
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    • v.23 no.3
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    • pp.3-10
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    • 2015
  • All kinds of objects on the ground have inherent spectral reflectance curves, which can be used to classify the ground objects and to detect the target. Remotely sensed data have to be transferred to spectral reflectance for accurate analysis. There are formula methods provided by the institution, mathematical model method and ground-data-based method. In this study, RapidEye satellite image was converted to reflectance data using spectral reflectance of a CASI hyperspectral image by using vicarious radiometric calibration. The results were compared with those of the other calibration methods and ground data. The proposed method was closer to the ground data than ATCOR and New Kurucz 2005 method and equal with ELM method.

Hyperspectral Target Detection by Iterative Error Analysis based Spectral Unmixing (Iterative Error Analysis 기반 분광혼합분석에 의한 초분광 영상의 표적물질 탐지 기법)

  • Kim, Kwang-Eun
    • Korean Journal of Remote Sensing
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    • v.33 no.5_1
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    • pp.547-557
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    • 2017
  • In this paper, a new spectral unmixing based target detection algorithm is proposed which adopted Iterative Error Analysis as a tool for extraction of background endmembers by using the target spectrum to be detected as initial endmember. In the presented method, the number of background endmembers is automatically decided during the IEA by stopping the iteration when the maximum change in abundance of the target is less than a given threshold value. The proposed algorithm does not have the dependence on the selection of image endmembers in the model-based approaches such as Orthogonal Subspace Projection and the target influence on the background statistics in the stochastic approaches such as Matched Filter. The experimental result with hyperspectral image data where various real and simulated targets are implanted shows that the proposed method is very effective for the detection of both rare and non-rare targets. It is expected that the proposed method can be effectively used for mineral detection and mapping as well as target object detection.

A Study on Comparison of Phycocyanin Extraction Methods for Hyperspectral Remote Sensing of Cyanobacteria in Turbid Inland Waters (국내 담수역 남조류 원격탐사를 위한 피코시아닌 추출법 비교 연구)

  • Ha, Rim;Shin, Hyunjoo;Nam, Gibeom;Park, Sanghyun;Kang, Taegu;Song, Hyunoh;Lee, Hyuk
    • Journal of Korean Society on Water Environment
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    • v.32 no.6
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    • pp.520-527
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    • 2016
  • Phycocyanin (PC) is one of the water-soluble accessory pigments of cyanobacteria species, and its concentration is used to estimate the presence and relative abundance of cyanobacteria. In laboratory experiments, PC content of field data were determined using Sarada's freeze-thaw method in algal bloom season. The effectiveness of three selected extraction methods (repeated freeze-thaw method, homogenization, power control) for PC were determined. The extraction efficiency of phycocyanin was the highest (of the methods compared) when a single freezing-thawing cycle was followed by pre-sonication. Applying this optimized method to surface water of Korean inland waters, the average concentration distribution was estimated at 2.951.9mg/m3. It has been shown that the optimized pre-sonication method is suitable to measure cyanobacteria PC content for the characterization of inland waters. The approach and results of this study indicates the potential of effective methods for remote monitoring and management of water quality in turbid inland waters using hyperspectral remote sensing.

Water Column Correction of Airborne Hyperspectral Image for Benthic Cover Type Classification of Coastal Area (연안 해저 피복 분류를 위한 항공 초분광영상의 수심보정)

  • Shin, Jung Il;Cho, Hyung Gab;Kim, Sung Hak;Choi, Im Ho;Jung, Kyu Kui
    • 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 (RW) 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.