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  • Title/Summary/Keyword: Hyperspectral Data

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Accuracy Assessment of Supervised Classification using Training Samples Acquired by a Field Spectroradiometer: A Case Study for Kumnam-myun, Sejong City (지상 분광반사자료를 훈련샘플로 이용한 감독분류의 정확도 평가: 세종시 금남면을 사례로)

  • Shin, Jung Il;Kim, Ik Jae;Kim, Dong Wook
    • Journal of Korean Society for Geospatial Information Science
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    • v.24 no.1
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    • pp.121-128
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    • 2016
  • Many studies are focused on image data and classifier for comparison or improvement of classification accuracy. Therefore studies are needed aspect of the training samples on supervised classification which depend on reference data or skill of analyst. This study tries to assess usability of field spectra as training samples on supervised classification. Classification accuracies of hyperspectral and multispectral images were assessed using training samples from image itself and field spectra, respectively. The results shown about 90% accuracy with training sample collected from image. Using field spectra as training sample, accuracy was decreased 10%p for hyperspectral image, and 20%p for multispectral image. Especially, some classes shown very low accuracies due to similar spectral characteristics on multispectral image. Therefore, field spectra might be used as training samples on classification of hyperspectral image, although it has limitation for multispectral image.

Analysis of Satellite Images to Estimate Forest Biomass (산림 바이오매스를 산정하기 위한 위성영상의 분석)

  • Lee, Hyun Jik;Ru, Ji Ho;Yu, Young Geol
    • Journal of Korean Society for Geospatial Information Science
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    • v.21 no.3
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    • pp.63-71
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    • 2013
  • This study calculated vegetation indexes such as SR, NDVI, SAVI, and LAI to figure out correlations regarding vegetation by using high resolution KOMPSAT-2 images and LANDSAT images based on the forest biomass distribution map that utilized field survey data, satellite images and LiDAR data and then analyzed correlations between their values and forest biomass. The analysis results reveal that the vegetation indexes of high resolution KOMPSAT-2 images had higher correlations than those of LANDSAT images and that NDVI recorded high correlations among the vegetation indexes. In addition, the study analyzed the characteristics of hyperspectral images by using the COMIS of STSAT-3 and Hyperion images of a similar sensor, EO-1, and further the usability of biomass estimation in hyperspectral images by comparing vegetation index, which had relatively high correlations with biomass, with the vegetation indexes of LANDSAT with the same GSD conditions.

Analysis of Hyperspectral Radiometer and Water Constituents Data for Remote Estimation of Water Quality (원격 수질 측정을 위한 현장 초분광 복사계 및 수중 구성성분 관측 자료 분석)

  • Kim, Wonkook;Choi, Jun Myoung
    • Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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    • v.36 no.4
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    • pp.205-211
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    • 2018
  • Remote estimation of water quality via radiometric instruments provides a convenient means for monitoring environmental changes in water bodies in wide areas. Combined with platforms such as satellite, manned/unmanned vehicles, it reduces the measurement cost and time for acquiring water quality information on the interested target areas. To develop accurate retrieval algorithms, however, acquisition of in-situ measurements from various optical environment is critical. In this study, hyperspectral radiometric measurements, the coincident water quality variables, and its optical properties were obtained to analyze the optical environment of the study area. Field data collected around the Tongyeong area showed that the area has optically complex environment, with occasional outbreak of red tide in summer seasons. Effect of water constituents on the optical variables (remote sensing reflectance and absorption coefficients) were qualitatively analyzed.

Noise Band Extraction of Hyperion Image using Quadtree Structure and Fractal Characteristic (Quadtree 구조 및 프랙탈 특성을 이용한 Hyperion 영상의 노이즈 밴드 추출)

  • Chang, An-Jin;Kim, Yong-Il
    • Korean Journal of Remote Sensing
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    • v.26 no.5
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    • pp.489-495
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    • 2010
  • Hyperspectral imaging obtains information with a wider wavelength range a large number of bands. However, a high correlation between each band, computation cost, and noise causes inaccurate results in cases of no pre-processing. The noises of band extraction and elimination positively necessary in hyperspectral imaging. Since the previous studies have used a characteristic the whole image, a local characteristic of the image is considered for the noise band extraction. In this study, the Quadtree, which is a data structure algorithm. and the fractal dimension are adopted for noise band extraction in Hyperion images. The fractal dimensions of the segments divided by the Quadtree structure are calculated, and variation is used. We focused on the extraction of random noise bands in Hyperion images and compared them with the reference data made by visual decisions. The proposed algorithm extracts the most bands, including random noises. It is possible to eliminate more than 30 noise bands, regardless of images.

Convolutional Neural Network with Expert Knowledge for Hyperspectral Remote Sensing Imagery Classification

  • Wu, Chunming;Wang, Meng;Gao, Lang;Song, Weijing;Tian, Tian;Choo, Kim-Kwang Raymond
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • v.13 no.8
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    • pp.3917-3941
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    • 2019
  • The recent interest in artificial intelligence and machine learning has partly contributed to an interest in the use of such approaches for hyperspectral remote sensing (HRS) imagery classification, as evidenced by the increasing number of deep framework with deep convolutional neural networks (CNN) structures proposed in the literature. In these approaches, the assumption of obtaining high quality deep features by using CNN is not always easy and efficient because of the complex data distribution and the limited sample size. In this paper, conventional handcrafted learning-based multi features based on expert knowledge are introduced as the input of a special designed CNN to improve the pixel description and classification performance of HRS imagery. The introduction of these handcrafted features can reduce the complexity of the original HRS data and reduce the sample requirements by eliminating redundant information and improving the starting point of deep feature training. It also provides some concise and effective features that are not readily available from direct training with CNN. Evaluations using three public HRS datasets demonstrate the utility of our proposed method in HRS classification.

Clustering of HIRIS data

  • Huan, Nguyen Van;Kim, Hakil;Kim, Sun-Hwa;Lee, Kyu-Sung
    • Proceedings of the IEEK Conference
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    • 2007.07a
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    • pp.299-300
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    • 2007
  • Along with the development of imaging sensors, hyperspectral imaging technology is growing rapidly and contributing to many fields of science nowadays. However, the bulky size and complex structure make it difficult to be processed. Focused on in this paper is the clustering utility, implemented in HYVEW, a program involving tools and functions to manipulate with hyperspectral images. The clustering process aims to partition the surface of the imaged area into subregions by grouping the spectra subject to the similarity of spectra.

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The Ship Detection Using Airborne and In-situ Measurements Based on Hyperspectral Remote Sensing (초분광 원격탐사 기반 항공관측 및 현장자료를 활용한 선박탐지)

  • Park, Jae-Jin;Oh, Sangwoo;Park, Kyung-Ae;Foucher, Pierre-Yves;Jang, Jae-Cheol;Lee, Moonjin;Kim, Tae-Sung;Kang, Won-Soo
    • Journal of the Korean earth science society
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    • v.38 no.7
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    • pp.535-545
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    • 2017
  • Maritime accidents around the Korean Peninsula are increasing, and the ship detection research using remote sensing data is consequently becoming increasingly important. This study presented a new ship detection algorithm using hyperspectral images that provide the spectral information of several hundred channels in the ship detection field, which depends on high resolution optical imagery. We applied a spectral matching algorithm between the reflection spectrum of the ship deck obtained from two field observations and the ship and seawater spectrum of the hyperspectral sensor of an airborne visible/infrared imaging spectrometer. A total of five detection algorithms were used, namely spectral distance similarity (SDS), spectral correlation similarity (SCS), spectral similarity value (SSV), spectral angle mapper (SAM), and spectral information divergence (SID). SDS showed an error in the detection of seawater inside the ship, and SAM showed a clear classification result with a difference between ship and seawater of approximately 1.8 times. Additionally, the present study classified the vessels included in hyperspectral images by presenting the adaptive thresholds of each technique. As a result, SAM and SID showed superior ship detection abilities compared to those of other detection algorithms.

Soil Water Content Measurement Technology Using Hyperspectral Visible and Near-Infrared Imaging Technique (초분광 근적외선 영상 기술을 이용한 흙의 함수비 측정 기술)

  • Lim, Hwan-Hui;Cheon, Enok;Lee, Deuk-Hwan;Jeon, Jun-Seo;Lee, Seung-Rae
    • Journal of the Korean Geotechnical Society
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    • v.35 no.11
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    • pp.51-62
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    • 2019
  • In this study, a simple method to estimate the soil water content variation in a wide area was proposed using hyperspectral near-infrared images. The reflectance data of a sand, granite soils, and a kaolinite were measured by reflecting the soil samples with different wavelengths in the visible and near-infrared (VNIR) regions using hyperspectral cameras. The measured reflectances and parameters were used to build a water content prediction model using the Partial Least Square Regression (PLSR) analysis. In the water content prediction model, the Area of Reflectance (Near-infrared, NIR) parameter was the most suitable parameter to determine the water content. The parameter was applicable regardless of the soil type, as the coefficient of determination (R2) exceeded 0.9 for each soil sample. Additionally, the mean absolute percentage error (MAPE) was less than 15% when compared with the actual water content of the soil. Therefore, the predictability of water content variation for soils with water content lower than 50% was confirmed. Accordingly through this study, the predictability of water content variation in several soil types using the hyperspectral near-infrared images was confirmed. For further development, a model that incorporates soil classification would be required to improve the accuracy of the model and to predict higher range of water contents.

The Study on Spatial Classification of Riverine Environment using UAV Hyperspectral Image (UAV를 활용한 초분광 영상의 하천공간특성 분류 연구)

  • Kim, Young-Joo;Han, Hyeong-Jun;Kang, Joon-Gu
    • Journal of the Korea Academia-Industrial cooperation Society
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    • v.19 no.10
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    • pp.633-639
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    • 2018
  • High-resolution images using remote sensing (RS) is importance to secure for spatial classification depending on the characteristics of the complex and various factors that make up the river environment. The purpose of this study is to evaluate the accuracy of the classification results and to suggest the possibility of applying the high resolution hyperspectral images obtained by using the drone to perform spatial classification. Hyperspectral images obtained from study area were reduced the dimensionality with PCA and MNF transformation to remove effects of noise. Spatial classification was performed by supervised classifications such as MLC(Maximum Likelihood Classification), SVM(Support Vector Machine) and SAM(Spectral Angle Mapping). In overall, the highest classification accuracy was showed when the MLC supervised classification was used by MNF transformed image. However, it was confirmed that the misclassification was mainly found in the boundary of some classes including water body and the shadowing area. The results of this study can be used as basic data for remote sensing using drone and hyperspectral sensor, and it is expected that it can be applied to a wider range of river environments through the development of additional algorithms.

Development of Non-Destructive Sorting Technique for Viability of Watermelon Seed by Using Hyperspectral Image Processing (초분광 영상기술을 이용한 수박종자 발아여부 비파괴 선별기술 개발)

  • Bae, Hyungjin;Seo, Young-Wook;Kim, Dae-Yong;Lohumi, Santosh;Park, Eunsoo;Cho, Byoung-Kwan
    • Journal of the Korean Society for Nondestructive Testing
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    • v.36 no.1
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    • pp.35-44
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    • 2016
  • Seed viability is one of the most important parameters that is directly related with seed germination performance and seedling emergence. In this study, a hyperspectral imaging (HSI) system having a range of 1000-2500 nm was used to classify viable watermelon seeds from nonviable seeds. In order to obtain nonviable watermelon seeds, a total of 96 seeds were artificially aged by immersing the seeds in hot water (25C) for 15 days. Further, hyperspectral images for 192 seeds (96 normal and 96 aged) were acquired using the developed HSI system. A germination test was performed for all the 192 seeds in order to confirm their viability. Spectral data from the hyperspectral images of the seeds were extracted by selecting pixels from the region of interest. Each seed spectrum was averaged and preprocessed to develop a classification model of partial least square discriminant analysis (PLS-DA). The developed PLS-DA model showed a classification accuracy of 94.7% for the calibration set, and 84.2% for the validation set. The results demonstrate that the proposed technique can classify viable and nonviable watermelon seeds with a reasonable accuracy, and can be further converted into an online sorting system for rapid and nondestructive classification of watermelon seeds with regard to viability.