• Current Optics and Photonics
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• v.4 no.6
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• pp.530-539
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• 2020

Hyperspectral images feature a relatively narrow band and are easily disturbed by noise. Accurate estimation of the types and parameters of noise in hyperspectral images can provide prior knowledge for subsequent image processing. Existing hyperspectral-noise estimation methods often pay more attention to the use of spectral information while ignoring the spatial information of hyperspectral images. To evaluate the noise in hyperspectral images more accurately, we have proposed a spectral-spatial cooperative noise-evaluation method. First, the feature of spatial information was extracted by Gabor-filter and K-means algorithms. Then, texture edges were extracted by the Otsu threshold algorithm, and homogeneous image blocks were automatically separated. After that, signal and noise values for each pixel in homogeneous blocks were split with a multiple-linear-regression model. By experiments with both simulated and real hyperspectral images, the proposed method was demonstrated to be effective and accurate, and the composition of the hyperspectral image was verified.

• Korean Journal of Remote Sensing
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• v.21 no.4
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• pp.341-369
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• 2005

Hyperspectral images have emerged as a new and promising remote sensing data that can overcome the limitations of existing optical image data. This study was designed to provide a comprehensive review on definition, data processing methods, and applications of hyperspectral data. Various types of airborne, spaceborne, and field hyperspectral image sensors were surveyed from the available literatures and internet search. To understand the current status of hyperspectral remote sensing technology and research development, we collected several hundreds research papers from international journals (IEEE Transactions on Geoscience and Remote Sensing, International Journal of Remote Sensing, Remote Sensing of Environment and AVIRIS Workshop Proceedings), and categorized them by sensor types, data processing techniques, and applications. Although several hyperspectral sensors have been developing, AVIRIS has been a primary data source that the most hyperspectral remote sensing researches were relied on. Since hyperspectral data have very large data volume with many spectral bands, several data processing techniques that are particularly oriented to hyperspectral data have been developed. Although atmospheric correction, spectral mixture analysis, and spectral feature extraction are among those processing techniques, they are still in experimental stage and need further refinement until the fully operational adaptation. Geology and mineral exploration were major application in early stage of hyperspectral sensing because of the distinct spectral features of rock and minerals that could be easily observed with hyperspectral data. The applications of hyperspectral sensing have been expanding to vegetation, water resources, and military areas where the multispectral sensing was not very effective to extract necessary information.

• Proceedings of the KSRS Conference
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• 2005.10a
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• pp.221-224
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• 2005

Hyperspectral image data have great potential to depict more detailed information on biophysical characteristics of surface materials, which are not usually available with multispectral data. This study aims to test the potential of hyperspectral data for classifying five soil classes defined by the vector product interim terrain data (VITD). In this study, we try to classify surface materials of bare soil over the study area in Korea using both hyperspectral and multispectral image data. Training and test samples for classification are selected with using VITD vector map. The spectral angle mapper (SAM) method is applied to the EO-I Hyperion data and Landsat ETM+ data, that has been radiometrically corrected and geo-rectified. Higher classification accuracy is obtained with the hyperspectral data for classifying five soil classes of gravel, evaporites, inorganic silt and sand.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.32 no.3
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• pp.185-190
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• 2014

The purpose of this research is to extract spectral reflectance characteristics of concretes through basic experiment on concrete specimens and site experiment on actual concrete structures using a field portable spectrometer and a VNIR hyperspectral sensor. A spectrometer (GER-3700) and a VNIR hyperspectral camera (AisaEagle VNIR Hyperspectral Camera) were utilized for extracting spectral characteristics of concrete specimens. Concretes normally show similar patterns that have correlation above 80%, while the high-strengthened concretes display very different results from the normal-strength concretes. We also made a certain conclusion in the laboratory experiment on concrete specimens that both the spectrometer and the VNIR camera vary in spectral reflectance depending on concrete strengths.

• Proceedings of the Korean Society of Surveying, Geodesy, Photogrammetry, and Cartography Conference
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• 2004.11a
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• pp.251-256
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• 2004

The image classification is one of the most important studies in the remote sensing. In general, the MLC(Maximum Likelihood Classification) classification that in consideration of distribution of training information is the most effective way but it produces a bad result when we apply it to actual hyperspectral image with the same classification technique. The purpose of this research is to reveal that which one is the most effective and suitable way of the classification algorithms iii the hyperspectral image classification. To confirm this matter, we apply the MLC classification algorithm which has distribution information and SAM(Spectral Angle Mapper), SFF(Spectral Feature Fitting) algorithm which use average information of the training class to both multispectral image and hyperspectral image. I conclude this result through quantitative and visual analysis using confusion matrix could confirm that SAM and SFF algorithm using of spectral pattern in vector domain is more effective way in the hyperspectral image classification than MLC which considered distribution.

• Journal of Korea Multimedia Society
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• v.12 no.12
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• pp.1755-1760
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• 2009

In this research, for the first time, we tried to analyse Raman hyperspectral dentin data using Independent Component Analysis (ICA) to see its possibility of adoption for the dental analysis software. We captured hyperspectral dentin data on 569 spots on a molar with dental lesion by HR800 Micro Raman Spectrometer at UMKC-CRISP (University of Missouri at Kansas City-Center for Research on Interfacial Structure and Properties). Each spot has 1,005 hyperspectral data. We applied ICA to the captured hyperspectral data of dentin for evaluating ICA approach, and compared it with the well known multivariate analysis method, PCA. As a result of the experiment, ICA approach shows better local characteristic of dentin than the result of PCA. We confirmed that ICA also could be a good method along with PCA in the dental analysis software.

• Korean Journal of Remote Sensing
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• v.23 no.3
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• pp.171-179
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• 2007

Hyperspectral images have shown a great potential for the applications in resource management, agriculture, mineral exploration and environmental monitoring. However, due to the large volume of data, processing of hyperspectral images faces some difficulties. This paper introduces the development of an image processing tool (HYVIEW) that is particularly designed for handling hyperspectral image data. Current version of HYVIEW is dealing with efficient algorithms for displaying hyperspectral images, selecting bands to create color composites, and atmospheric correction. Three band-selection schemes for producing color composites are available based on three most popular indexes of OIF, SI and CI. HYVIEW can effectively demonstrate the differences in the results of the three schemes. For the atmospheric correction, HYVIEW utilizes a pre-calculated LUT by which the complex process of correcting atmospheric effects can be performed fast and efficiently.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.1
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• pp.327-346
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• 2019

The goal of hyperspectral pansharpening is to combine a hyperspectral image (HSI) with a panchromatic image (PANI) derived from the same scene to obtain a single fused image. In this paper, a new hyperspectral pansharpening approach using adaptive weighted regression and guided filter is proposed. First, the intensity information (INT) of the HSI is obtained by the adaptive weighted regression algorithm. Especially, the optimization formula is solved to obtain the closed solution to reduce the calculation amount. Then, the proposed method proposes a new way to obtain the sufficient spatial information from the PANI and INT by guided filtering. Finally, the fused HSI is obtained by adding the extracted spatial information to the interpolated HSI. Experimental results demonstrate that the proposed approach achieves better property in preserving the spectral information as well as enhancing the spatial detail compared with other excellent approaches in visual interpretation and objective fusion metrics.

• Journal of the Korean Society of Industry Convergence
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• v.26 no.5
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• pp.785-792
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• 2023

Because hyperspectral imaging provides detailed spectral information across a broad range of wavelengths, it can be utilized in numerous applications, including environmental monitoring, food quality inspection, medical diagnosis, material identification, art authentication, and crime scene analysis. However, hyperspectral images often contain various types of distortions due to the environmental conditions during image acquisition, which necessitates the proper removal of these distortions through a data preprocessing process. In this study, a preprocessing method was investigated to effectively correct the distortion caused by artificial light sources used in indoor hyperspectral imaging. For this purpose, a halogen-tungsten artificial light source was installed indoors, and hyperspectral images were acquired. The acquired images were then corrected for distortion using a preprocessing that does not require complex auxiliary equipment. After the corrections were made, the results were analyzed. According to the analysis, a statistical transformation technique using mean and standard deviation with reference to a reference signal was found to be the most effective in correcting distortions caused by artificial light sources.

• Journal of Biosystems Engineering
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• v.41 no.4
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• pp.396-407
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• 2016

Background: Although hyperspectral imaging was originally introduced for military, remote sensing, and astrophysics applications, the use of analytical hyperspectral imaging techniques has been expanded to include monitoring of agricultural crops and commodities due to the broad range and highly specific and sensitive spectral information that can be acquired. Combining hyperspectral imaging with remote sensing expands the range of targets that can be analyzed. Results: Hyperspectral imaging technology can rapidly provide data suitable for monitoring a wide range of plant conditions such as plant stress, nitrogen status, infections, maturity index, and weed discrimination very rapidly, and its use in remote sensing allows for fast spatial coverage. Conclusions: This paper reviews current research on and potential applications of hyperspectral imaging and remote sensing for outdoor field monitoring of agricultural crops. The instrumentation and the fundamental concepts and approaches of hyperspectral imaging and remote sensing for agriculture are presented, along with more recent developments in agricultural monitoring applications. Also discussed are the challenges and limitations of outdoor applications of hyperspectral imaging technology such as illumination conditions and variations due to leaf and plant orientation.