• 제목/요약/키워드: Hyperspectral Data

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Land Cover Classification of Coastal Area by SAM from Airborne Hyperspectral Images (항공 초분광 영상으로부터 연안지역의 SAM 토지피복분류)

  • LEE, Jin-Duk;BANG, Kon-Joon;KIM, Hyun-Ho
    • Journal of the Korean Association of Geographic Information Studies
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    • v.21 no.1
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    • pp.35-45
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    • 2018
  • Image data collected by an airborne hyperspectral camera system have a great usability in coastal line mapping, detection of facilities composed of specific materials, detailed land use analysis, change monitoring and so forh in a complex coastal area because the system provides almost complete spectral and spatial information for each image pixel of tens to hundreds of spectral bands. A few approaches after classifying by a few approaches based on SAM(Spectral Angle Mapper) supervised classification were applied for extracting optimal land cover information from hyperspectral images acquired by CASI-1500 airborne hyperspectral camera on the object of a coastal area which includes both land and sea water areas. We applied three different approaches, that is to say firstly the classification approach of combined land and sea areas, secondly the reclassification approach after decompostion of land and sea areas from classification result of combined land and sea areas, and thirdly the land area-only classification approach using atmospheric correction images and compared classification results and accuracies. Land cover classification was conducted respectively by selecting not only four band images with the same wavelength range as IKONOS, QuickBird, KOMPSAT and GeoEye satelllite images but also eight band images with the same wavelength range as WorldView-2 from 48 band hyperspectral images and then compared with the classification result conducted with all of 48 band images. As a result, the reclassification approach after decompostion of land and sea areas from classification result of combined land and sea areas is more effective than classification approach of combined land and sea areas. It is showed the bigger the number of bands, the higher accuracy and reliability in the reclassification approach referred above. The results of higher spectral resolution showed asphalt or concrete roads was able to be classified more accurately.

Usefulness of Canonical Correlation Classification Technique in Hyper-spectral Image Classification (하이퍼스펙트럴영상 분류에서 정준상관분류기법의 유용성)

  • Park, Min-Ho
    • 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.

Review of applicability of Turbidity-SS relationship in hyperspectral imaging-based turbid water monitoring (초분광영상 기반 탁수 모니터링에서의 탁도-SS 관계식 적용성 검토)

  • Kim, Jongmin;Kim, Gwang Soo;Kwon, Siyoon;Kim, Young Do
    • Journal of Korea Water Resources Association
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    • v.56 no.12
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    • pp.919-928
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    • 2023
  • Rainfall characteristics in Korea are concentrated during the summer flood season. In particular, when a large amount of turbid water flows into the dam due to the increasing trend of concentrated rainfall due to abnormal rainfall and abnormal weather conditions, prolonged turbid water phenomenon occurs due to the overturning phenomenon. Much research is being conducted on turbid water prediction to solve these problems. To predict turbid water, turbid water data from the upstream inflow is required, but spatial and temporal data resolution is currently insufficient. To improve temporal resolution, the development of the Turbidity-SS conversion equation is necessary, and to improve spatial resolution, multi-item water quality measurement instrument (YSI), Laser In-Situ Scattering and Transmissometry (LISST), and hyperspectral sensors are needed. Sensor-based measurement can improve the spatial resolution of turbid water by measuring line and surface unit data. In addition, in the case of LISST-200X, it is possible to collect data on particle size, etc., so it can be used in the Turbidity-SS conversion equation for fraction (Clay: Silt: Sand). In addition, among recent remote sensing methods, the spatial distribution of turbid water can be presented when using UAVs with higher spatial and temporal resolutions than other payloads and hyperspectral sensors with high spectral and radiometric resolutions. Therefore, in this study, the Turbidity-SS conversion equation was calculated according to the fraction through laboratory analysis using LISST-200X and YSI-EXO, and sensor-based field measurements including UAV (Matrice 600) and hyperspectral sensor (microHSI 410 SHARK) were used. Through this, the spatial distribution of turbidity and suspended sediment concentration, and the turbidity calculated using the Turbidity-SS conversion equation based on the measured suspended sediment concentration, was presented. Through this, we attempted to review the applicability of the Turbidity-SS conversion equation and understand the current status of turbid water occurrence.

Evaluating the Land Surface Characterization of High-Resolution Middle-Infrared Data for Day and Night Time (고해상도 중적외선 영상자료의 주야간 지표면 식별 특성 평가)

  • Baek, Seung-Gyun;Jang, Dong-Ho
    • Journal of the Korean Association of Geographic Information Studies
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    • v.15 no.2
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    • pp.113-125
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    • 2012
  • This research is aimed at evaluating the land surface characterization of KOMPSAT-3A middle infrared (MIR) data. Airborne Hyperspectral Scanner (AHS) data, which has MIR bands with high spatial resolution, were used to assess land surface temperature (LST) retrieval and classification accuracy of MIR bands. Firstly, LST values for daytime and nighttime, which were calculated with AHS thermal infrared (TIR) bands, were compared to digital number of AHS MIR bands. The determination coefficient of AHS band 68 (center wavelength 4.64μm) was over 0.74, and was higher than other MIR bands. Secondly, The land cover maps were generated by unsupervised classification methods using the AHS MIR bands. Each class of land cover maps for daytime, such as water, trees, green grass, roads, roofs, was distinguished well. But some classes of land cover maps for nighttime, such as trees versus green grass, roads versus roofs, were not separated. The image classification using the difference images between daytime AHS MIR bands and nighttime AHS MIR bands were conducted to enhance the discrimination ability of land surface for AHS MIR imagery. The classification accuracy of the land cover map for zone 1 and zone 2 was 67.5%, 64.3%, respectively. It was improved by 10% compared to land cover map of daytime AHS MIR bands and night AHS MIR bands. Consequently, new algorithm based on land surface characteristics is required for temperature retrieval of high resolution MIR imagery, and the difference images between daytime and nighttime was considered to enhance the ability of land surface characterization using high resolution MIR data.

Development of Drought Stress Measurement Method for Red Pepper Leaves using Hyperspectral Short Wave Infrared Imaging Technique (초분광 단파적외선 영상 기술을 이용한 고추의 수분스트레스 측정 기술 개발)

  • Park, Eunsoo;Cho, Byoung-Kwan
    • Journal of Bio-Environment Control
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    • v.23 no.1
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    • pp.50-55
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    • 2014
  • This study was conducted to investigate the responses of red pepper (Hongjinju) leaves under water stress. Hyperspectral short wave infrared (SWIR, 1000~1800 nm) reflectance imaging techniques were used to acquire the spectral images for the red pepper leaves with and without water stress. The acquired spectral data were analyzed with a multivariate analysis method of ANOVA (analysis of variance). The ANOVA model suggested that 1449 nm wavebands was the most effective to determine the stress responses of the red pepper leaves exposed to the water deficiency. The waveband of 1449 nm was closely related to the water absorption band. The processed spectral image of 1449 nm could separate the non-stress, moderate stress (-20 kPa), and severe stress (-50 kPa) groups of red pepper leaves distinctively. Results demonstrated that hyperspectral imaging technique can be applied to monitoring the stress responses of red pepper leaves which are an indicator of physiological and biochemical changes under water deficiency.

A Study on Feature Selection and Feature Extraction for Hyperspectral Image Classification Using Canonical Correlation Classifier (정준상관분류에 의한 하이퍼스펙트럴영상 분류에서 유효밴드 선정 및 추출에 관한 연구)

  • Park, Min-Ho
    • KSCE Journal of Civil and Environmental Engineering Research
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    • v.29 no.3D
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    • pp.419-431
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    • 2009
  • The core of this study is finding out the efficient band selection or extraction method discovering the optimal spectral bands when applying canonical correlation classifier (CCC) to hyperspectral data. The optimal efficient bands grounded on each separability decision technique are selected using Multispec(C) software developed by Purdue university of USA. Total 6 separability decision techniques are used, which are Divergence, Transformed Divergence, Bhattacharyya, Mean Bhattacharyya, Covariance Bhattacharyya, Noncovariance Bhattacharyya. For feature extraction, PCA transformation and MNF transformation are accomplished by ERDAS Imagine and ENVI software. For the comparison and assessment on the effect of feature selection and feature extraction, land cover classification is performed by CCC. The overall accuracy of CCC using the firstly selected 60 bands is 71.8%, the highest classification accuracy acquired by CCC is 79.0% as the case that executes CCC after appling Noncovariance Bhattacharyya. In conclusion, as a matter of fact, only Noncovariance Bhattacharyya separability decision method was valuable as feature selection algorithm for hyperspectral image classification depended on CCC. The lassification accuracy using other feature selection and extraction algorithms except Divergence rather declined in CCC.

A Study on the Object-based Classification Method for Wildfire Fuel Type Map (산불연료지도 제작을 위한 객체기반 분류 방법 연구)

  • Yoon, Yeo-Sang;Kim, Youn-Soo;Kim, Yong-Seung
    • Aerospace Engineering and Technology
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    • v.6 no.1
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    • pp.213-221
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    • 2007
  • This paper showed how to analysis the object-based classification for wildfire fuel type map using Hyperion hyperspectral remote sensing data acquired in April, 2002 and compared the results of the object-based classification with the results of the pixel-based classification. Our methodological approach for wildfire fuel type map firstly processed correcting abnormal pixels and atypical bands and also calibrating atmospheric noise for enhanced image quality. Fuel type map is characterized by the results of the spectral mixture analysis(SMA). Object-based approach was based on segment-based endmember selection, while pixel-based method used standard SMA. To validate and compare, we used true-color high resolution orthoimagery.

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A COMPARISON OF OBJECTED-ORIENTED AND PIXELBASED CLASSIFICATION METHODS FOR FUEL TYPE MAP USING HYPERION IMAGERY

  • Yoon, Yeo-Sang;Kim, Yong-Seung
    • Proceedings of the KSRS Conference
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    • v.1
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    • pp.297-300
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    • 2006
  • The knowledge of fuel load and composition is important for planning and managing the fire hazard and risk. However, fuel mapping is extremely difficult because fuel properties vary at spatial scales, change depending on the seasonal situations and are affected by the surrounding environment. Remote sensing has potential of reduction the uncertainty in mapping fuels and offers the best approach for improving our abilities. This paper compared the results of object-oriented classification to a pixel-based classification for fuel type map derived from Hyperion hyperspectral data that could be enable to provide this information and allow a differentiation of material due to their typical spectra. Our methodological approach for fuel type map is characterized by the result of the spectral mixture analysis (SMA) that can used to model the spectral variability in multi- or hyperspectral images and to relate the results to the physical abundance of surface constitutes represented by the spectral endmembers. Object-oriented approach was based on segment based endmember selection, while pixel-based method used standard SMA. To validate and compare, we used true-color high resolution orthoimagery

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Validation of the Radiometric Characteristics of Landsat 8 (LDCM) OLI Sensor using Band Aggregation Technique of EO-1 Hyperion Hyperspectral Imagery (EO-1 Hyperion 초분광 영상의 밴드 접합 기법을 이용한 Landsat 8 (LDCM) OLI 센서의 방사 특성 검증)

  • Chi, Junhwa
    • Korean Journal of Remote Sensing
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    • v.29 no.4
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    • pp.399-406
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    • 2013
  • The quality of satellite imagery should be improved and stabilized to satisfy numerous users. The radiometric characteristics of an optical sensor can be a measure of data quality. In this study, a band aggregation technique and spectral response function of hyperspectral images are used to simulate multispectral images. EO-1 Hyperion and Landsat-8 OLI images acquired with about 30 minutes difference in overpass time were exploited to evaluate radiometric coefficients of OLI. Radiance values of the OLI and the simulated OLI were compared over three subsets covered by different land types. As a result, the index of agreement shows over 0.99 for all VNIR bands although there are errors caused by space/time and sensors.

An Analysis of Spectral Characteristic Information on the Water Level Changes and Bed Materials (수위변화에 따른 하상재료의 분광특성정보 분석)

  • Kang, Joongu;Lee, Changhun;Kim, Jihyun;Ko, Dongwoo;Kim, Jongtae
    • Ecology and Resilient Infrastructure
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    • v.6 no.4
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    • pp.243-249
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    • 2019
  • The purpose of this study is to analyze the reflectance of bed materials according to changes in the water level using a drone-based hyperspectral sensor. For this purpose, we took hyperspectral images of bed materials such as soil, gravel, cobble, reed, and vegetation to compare and analyze the spectral data of each material. To adjust the water level, we constructed an experimental channel to control the discharge and installed the bed materials within the channel. In this study, we configured 3 cases according to the water level (0.0 m, 0.3 m, 0.6 m). After the imaging process, we used the mean value of 10 points for each bed material as analytical data. According to the analysis, each material showed a similar reflectance by wavelength and the intrinsic reflectance characteristics of each material were shown in the visible and near-infrared region. Also, the deeper the water level, the lower the peak reflectance in the visible and near-infrared region, and the rate of decrease differed depending on the bed material. We expect the intrinsic properties of these bed materials to be used as basic research data to evaluate river environments in the future.