• Korean Journal of Agricultural Science
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• v.45 no.4
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• pp.823-837
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• 2018

The objective of this study was to evaluate the firmness and the sweetness index (SI) of tomatoes with a hyperspectral imaging (HSI) technique within the wavelength range of 1000 - 1550 nm. The hyperspectral images of 95 tomatoes were acquired with a push-broom hyperspectral reflectance imaging system, from which the mean spectra of each tomato were extracted from the regions of interest. The reference firmness and sweetness index of the same sample was measured and calibrated with their corresponding spectral data by partial least squares (PLS) regression with different preprocessing methods. The calibration model developed by PLS regression based on the Savitzky-Golay second-derivative preprocessed spectra resulted in a better performance for both the firmness and the SI of the tomatoes compared to models developed by other preprocessing methods. The correlation coefficients ($R_{pred}$) were 0.82, and 0.74 with a standard error of prediction of 0.86 N, and 0.63, respectively. Then, the feature wavelengths were identified using a model-based variable selection method, i.e., variable importance in projection, from the PLS regression analyses. Finally, chemical images were derived by applying the respective regression coefficients on the spectral image in a pixel-wise manner. The resulting chemical images provided detailed information on the firmness and the SI of the tomatoes. The results show that the proposed HSI technique has potential for rapid and non-destructive evaluation of firmness and the sweetness index of tomatoes.

• Korean Journal of Remote Sensing
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• v.30 no.5
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• pp.665-675
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• 2014

In this paper spectral characteristics and spectral patterns of pine wilt disease at different development stage were analyzed in Geoje-do where the disease has already spread. Ground-based hyperspectral imaging containing hundreds of wavelength band is feasible with continuous screening and monitoring of disease symptoms during pathogenesis. The research is based on an hyperspectral imaging of trees from infection phase to witherer phase using a ground based hyperspectral camera within the area of pine wilt disease outbreaks in Geojedo for the analysis of pine wilt disease. Hyperspectral imaging through hundreds of wavelength band is feasible with a ground based hyperspectral camera. In this research, we carried out wavelength band change analysis on trees from infection phase to witherer phase using ground based hyperspectral camera and comparative analysis with major vegetation indices such as Normalized Difference Vegetation Index (NDVI), Red Edge Normalized Difference Vegetation Index (reNDVI), Photochemical Reflectance Index (PRI) and Anthocyanin Reflectance Index 2 (ARI2). As a result, NDVI and reNDVI were analyzed to be effective for infection tree detection. The 688 nm section, in which withered trees and healthy trees reflected the most distinctions, was applied to reNDVI to judge the applicability of the section. According to the analysis result, the vegetation index applied including 688 nm showed the biggest change range by infection progress.

• Journal of the Korean Association of Geographic Information Studies
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• v.18 no.1
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• pp.48-63
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• 2015

In this study, we introduce various hyperspectral data processing techniques for the monitoring of shallow and coastal waters to enlarge the application range and to improve the accuracy of the end results in Korea. Unlike land, more accurate atmospheric correction is needed in coastal region showing relatively low reflectance in visible wavelengths. Sun-glint which occurs due to a geometry of sun-sea surface-sensor is another issue for the data processing in the ocean application of hyperspectal imagery. After the preprocessing of the hyperspectral data, a semi-analytical algorithm based on a radiative transfer model and a spectral library can be used for bathymetry mapping in coastal area, type classification and status monitoring of benthos or substrate classification. In general, semi-analytical algorithms using spectral information obtained from hyperspectral imagey shows higher accuracy than an empirical method using multispectral data. The water depth and quality are constraint factors in the ocean application of optical data. Although a radiative transfer model suggests the theoretical limit of about 25m in depth for bathymetry and bottom classification, hyperspectral data have been used practically at depths of up to 10 m in shallow and coastal waters. It means we have to focus on the maximum depth of water and water quality conditions that affect the coastal applicability of hyperspectral data, and to define the spectral library of coastal waters to classify the types of benthos and substrates.

• Journal of Korea Water Resources Association
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• v.54 no.12
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• pp.1265-1274
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• 2021

The analysis of hyperspectral characteristics of materials near the South Han River has been conducted using riverside area measurements by drone installed hyperspectral sensors. Each spectrum reflectance of the riverside materials were compared and analyzed which were consisted of grass, concrete, soil, etc. To verify the drone installed hyperspectral measurements, a ground spectrometer was deployed for field measurements and comparisons for the materials. The comparison results showed that the riverside materials had their unique hyperspectral band characteristics, and the field measurements were similar to the remote sensing data. For the classification of the riverside area, the K-means clustering method and SVM classification method were utilized. The supervised SVM method showed accurate classification of the riverside area than the unsupervised K-means method. Using classification and clustering methods, the inherent spectral characteristic for each material was found to classify the riverside materials of hyperspectral images from drones.

• International Journal of Computer Science & Network Security
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• v.23 no.12
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• pp.213-219
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• 2023

In recent years, popularity of deep learning (DL) is increased due to its ability to extract features from Hyperspectral images. A lack of discrimination power in the features produced by traditional machine learning algorithms has resulted in poor classification results. It's also a study topic to find out how to get excellent classification results with limited samples without getting overfitting issues in hyperspectral images (HSIs). These issues can be addressed by utilising a new learning network structure developed in this study.EfficientNet-B4-Based Convolutional network (EN-B4), which is why it is critical to maintain a constant ratio between the dimensions of network resolution, width, and depth in order to achieve a balance. The weight of the proposed model is optimized by Search and Rescue Operations (SRO), which is inspired by the explorations carried out by humans during search and rescue processes. Tests were conducted on two datasets to verify the efficacy of EN-B4, with Indian Pines (IP) and the University of Pavia (UP) dataset. Experiments show that EN-B4 outperforms other state-of-the-art approaches in terms of classification accuracy.

• 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.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.26 no.2
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• pp.129-137
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• 2008

This study focuses on the radiometric normalization, which is one of the pre-processing steps to apply the change detection technique fur hyperspectral images. The PIFs which had radiometric consistency under the time interval were automatically extracted by applying spectral angle, and used as sample pixels for linear regression of the radiometric normalization. We also dealt with the problem about the number of PIFs for linear regression with iteratively quantitative methods. The results were assessed in comparison with image regression, histogram matching, and FLAASH. In conclusion, we show that linear regression method with PIFs can carry out the efficient result for radiometric normalization.

• Korean Journal of Remote Sensing
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• v.29 no.5
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• pp.527-535
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• 2013

Extraction of correct endmembers is prerequisite to successful spectral unmixing analysis. Various endmember extraction algorithms have been proposed and most experiments based on endmember extraction have used synthetic image and AVIRIS image data. However, these data can present different characteristics comparing with hyperspectral images acquired from real domestic environment. For this study, a test-bed was constructed for analysing the difference on diverse substances and sizes in domestic areas, and AISA hyperspectral imagery acquired from the test-bed was tested with two well-known endmember extraction algorithms: IEA, and N-FINDR. The results indicated that two different algorithms depended on the number of endmembers and material types in the test-bed. Therefore, optimized number of endmembers and characteristics of materials in test-bed site should be considered for the effective application of endmember extraction algorithms.

• 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.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.21 no.5
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• pp.149-154
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• 2021

Unlike or common images, Hyperspectral images were taken by continuous electromagnetic spectral into numerous bands according to wavelengths and are high-capacity high-resolution images. It has more information than ordinary images, so it is used to explore objects and materials. To reduce the amount of information in hyper-spectral images to be processed, band selection is utilized. Existing band selection techniques are heuristic techniques based on statistics, which take a long time and often lack generality and universality. To compensate for this, this paper utilizes quantization concept to draw representative bands through Discrete Range, we use them for band selection algorithm. Experimental results showed that the proposed technique performed much faster than conventional band selection methods, and that the performance accuracy was similar to that of the original even though the number of bands was reduced by one-seventh to one-tenth.