The Experts below are selected from a list of 116547 Experts worldwide ranked by ideXlab platform
Jun Wang - One of the best experts on this subject based on the ideXlab platform.
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IJCNN - Superpixel-based sparse representation classifier for Hyperspectral image
2016 International Joint Conference on Neural Networks (IJCNN), 2016Co-Authors: Min Han, Chengkun Zhang, Jun WangAbstract:This paper proposes a novel superpixel-based method for the classification of Hyperspectral image. A superpixel segmentation algorithm called entropy rate superpixel is applied to extract the spatial contextual information in the Hyperspectral image, which can change the size and shape of the superpixel adaptively according to spatial structures. Then, a joint sparse representation model is applied to approximate the pixels within each superpixel using a certain number of common samples from a given dictionary in the form of sparse linear combination. Here we use a greedy algorithm called simultaneous orthogonal matching pursuit to pursue the optimal sparse coefficients matrix and a new kind of classification criterion is tested and used to determine the classification results. Experimental results on the Indian Pines hyperspsectral image demonstrate that the proposed method can explore the spatial information effectively and give promising performance when compared with several state-of-art classification methods.
Bosoon Park - One of the best experts on this subject based on the ideXlab platform.
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Hyperspectral imaging using RGB color for foodborne pathogen detection
Journal of Electronic Imaging, 2015Co-Authors: Seung-chul Yoon, Kurt C. Lawrence, Tae-sung Shin, Gerald W. Heitschmidt, Bosoon Park, Gary R. GambleAbstract:This paper reports the development of a spectral reconstruction technique for predicting Hyperspectral images from RGB color images and classifying food-borne pathogens in agar plates using reconstructed Hyperspectral images. The six representative non-O157 Shiga-toxin producing Escherichia coli (STEC) serogroups (O26, O45, O103, O111, O121, and O145) grown on Rainbow agar plates were used for the study. A line-scan pushbroom Hyperspectral imaging spectrometer was used to scan full reflectance spectra of pure non-O157 STEC cultures in the visible and near-infrared spectral range from 400 to 1000 nm. RGB color images were generated by simulation from Hyperspectral images. Polynomial multivariate least-squares regression analysis was used to reconstruct Hyperspectral images from RGB color images. The mean R-squared value for Hyperspectral image reconstruction was ∼0.98 in the spectral range between 400 and 700 nm for linear, quadratic, and cubic polynomial regression models. The accuracy of the Hyperspectral image classification algorithm based on k-nearest neighbors algorithm of principal component scores was validated to be 92% with the test set (99% with the original Hyperspectral images). The results of the study suggested that color-based Hyperspectral imaging would be feasible without much loss of prediction accuracy compared to true Hyperspectral imaging.
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Hyperspectral Image Processing Methods
Food Engineering Series, 2015Co-Authors: Seung-chul Yoon, Bosoon ParkAbstract:Hyperspectral image processing extracts, stores and manipulates both spatial and spectral information contained in Hyperspectral images across the visible and near-infrared portion of the electromagnetic spectrum. The demand for new Hyperspectral image processing tools and techniques more appropriate for near sensing in laboratories or fields of various science and engineering communities has been increasing in recent years. In addition to the Hyperspectral image processing algorithms developed for remote sensing applications, chemometrics and multivariate statistical data analysis techniques and their preprocessing techniques have been applied to process Hyperspectral images. Hyperspectral image processing workflows are fundamentally different from the conventional color image processing workflows although both data types are multidimensional and multivariate. A typical Hyperspectral image processing workflow for near-sensing applications includes normalization, correction, dimensionality reduction, spectral library building, and data processing. In this book chapter, recent advances in Hyperspectral image processing algorithms and workflows for Hyperspectral image processing are discussed. The main topics are image acquisition, calibration, spectral and spatial preprocessing, scatter correction, binning, and feature extraction and selection.
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Hyperspectral image reconstruction using RGB color for foodborne pathogen detection on agar plates
Image Processing: Machine Vision Applications VII, 2014Co-Authors: Seung-chul Yoon, Kurt C. Lawrence, Tae-sung Shin, Bosoon Park, Gerald W. HeitschmidtAbstract:This paper reports the latest development of a color vision technique for detecting colonies of foodborne pathogens grown on agar plates with a Hyperspectral image classification model that was developed using full Hyperspectral data. The Hyperspectral classification model depended on reflectance spectra measured in the visible and near-infrared spectral range from 400 and 1,000 nm (473 narrow spectral bands). Multivariate regression methods were used to estimate and predict Hyperspectral data from RGB color values. The six representative non-O157 Shiga-toxin producing Eschetichia coli (STEC) serogroups (O26, O45, O103, O111, O121, and O145) were grown on Rainbow agar plates. A line-scan pushbroom Hyperspectral image sensor was used to scan 36 agar plates grown with pure STEC colonies at each plate. The 36 Hyperspectral images of the agar plates were divided in half to create training and test sets. The mean Rsquared value for Hyperspectral image estimation was about 0.98 in the spectral range between 400 and 700 nm for linear, quadratic and cubic polynomial regression models and the detection accuracy of the Hyperspectral image classification model with the principal component analysis and k-nearest neighbors for the test set was up to 92% (99% with the original Hyperspectral images). Thus, the results of the study suggested that color-based detection may be viable as a multispectral imaging solution without much loss of prediction accuracy compared to Hyperspectral imaging.
Bin Pan - One of the best experts on this subject based on the ideXlab platform.
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l0 based sparse Hyperspectral unmixing using spectral information and a multi objectives formulation
Isprs Journal of Photogrammetry and Remote Sensing, 2018Co-Authors: Zhenwei Shi, Bin PanAbstract:Abstract Sparse unmixing aims at recovering pure materials from hyperpspectral images and estimating their abundance fractions. Sparse unmixing is actually l 0 problem which is NP-h ard, and a relaxation is often used. In this paper, we attempt to deal with l 0 problem directly via a multi-objective based method, which is a non-convex manner. The characteristics of Hyperspectral images are integrated into the proposed method, which leads to a new spectra and multi-objective based sparse unmixing method (SMoSU). In order to solve the l 0 norm optimization problem, the spectral library is encoded in a binary vector, and a bit-wise flipping strategy is used to generate new individuals in the evolution process. However, a multi-objective method usually produces a number of non-dominated solutions, while sparse unmixing requires a single solution. How to make the final decision for sparse unmixing is challenging. To handle this problem, we integrate the spectral characteristic of Hyperspectral images into SMoSU. By considering the spectral correlation in Hyperspectral data, we improve the Tchebycheff decomposition function in SMoSU via a new regularization item. This regularization item is able to enforce the individual divergence in the evolution process of SMoSU. In this way, the diversity and convergence of population is further balanced, which is beneficial to the concentration of individuals. In the experiments part, three synthetic datasets and one real-world data are used to analyse the effectiveness of SMoSU, and several state-of-art sparse unmixing algorithms are compared.
Seung-chul Yoon - One of the best experts on this subject based on the ideXlab platform.
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Hyperspectral imaging using RGB color for foodborne pathogen detection
Journal of Electronic Imaging, 2015Co-Authors: Seung-chul Yoon, Kurt C. Lawrence, Tae-sung Shin, Gerald W. Heitschmidt, Bosoon Park, Gary R. GambleAbstract:This paper reports the development of a spectral reconstruction technique for predicting Hyperspectral images from RGB color images and classifying food-borne pathogens in agar plates using reconstructed Hyperspectral images. The six representative non-O157 Shiga-toxin producing Escherichia coli (STEC) serogroups (O26, O45, O103, O111, O121, and O145) grown on Rainbow agar plates were used for the study. A line-scan pushbroom Hyperspectral imaging spectrometer was used to scan full reflectance spectra of pure non-O157 STEC cultures in the visible and near-infrared spectral range from 400 to 1000 nm. RGB color images were generated by simulation from Hyperspectral images. Polynomial multivariate least-squares regression analysis was used to reconstruct Hyperspectral images from RGB color images. The mean R-squared value for Hyperspectral image reconstruction was ∼0.98 in the spectral range between 400 and 700 nm for linear, quadratic, and cubic polynomial regression models. The accuracy of the Hyperspectral image classification algorithm based on k-nearest neighbors algorithm of principal component scores was validated to be 92% with the test set (99% with the original Hyperspectral images). The results of the study suggested that color-based Hyperspectral imaging would be feasible without much loss of prediction accuracy compared to true Hyperspectral imaging.
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Hyperspectral Image Processing Methods
Food Engineering Series, 2015Co-Authors: Seung-chul Yoon, Bosoon ParkAbstract:Hyperspectral image processing extracts, stores and manipulates both spatial and spectral information contained in Hyperspectral images across the visible and near-infrared portion of the electromagnetic spectrum. The demand for new Hyperspectral image processing tools and techniques more appropriate for near sensing in laboratories or fields of various science and engineering communities has been increasing in recent years. In addition to the Hyperspectral image processing algorithms developed for remote sensing applications, chemometrics and multivariate statistical data analysis techniques and their preprocessing techniques have been applied to process Hyperspectral images. Hyperspectral image processing workflows are fundamentally different from the conventional color image processing workflows although both data types are multidimensional and multivariate. A typical Hyperspectral image processing workflow for near-sensing applications includes normalization, correction, dimensionality reduction, spectral library building, and data processing. In this book chapter, recent advances in Hyperspectral image processing algorithms and workflows for Hyperspectral image processing are discussed. The main topics are image acquisition, calibration, spectral and spatial preprocessing, scatter correction, binning, and feature extraction and selection.
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Hyperspectral image reconstruction using RGB color for foodborne pathogen detection on agar plates
Image Processing: Machine Vision Applications VII, 2014Co-Authors: Seung-chul Yoon, Kurt C. Lawrence, Tae-sung Shin, Bosoon Park, Gerald W. HeitschmidtAbstract:This paper reports the latest development of a color vision technique for detecting colonies of foodborne pathogens grown on agar plates with a Hyperspectral image classification model that was developed using full Hyperspectral data. The Hyperspectral classification model depended on reflectance spectra measured in the visible and near-infrared spectral range from 400 and 1,000 nm (473 narrow spectral bands). Multivariate regression methods were used to estimate and predict Hyperspectral data from RGB color values. The six representative non-O157 Shiga-toxin producing Eschetichia coli (STEC) serogroups (O26, O45, O103, O111, O121, and O145) were grown on Rainbow agar plates. A line-scan pushbroom Hyperspectral image sensor was used to scan 36 agar plates grown with pure STEC colonies at each plate. The 36 Hyperspectral images of the agar plates were divided in half to create training and test sets. The mean Rsquared value for Hyperspectral image estimation was about 0.98 in the spectral range between 400 and 700 nm for linear, quadratic and cubic polynomial regression models and the detection accuracy of the Hyperspectral image classification model with the principal component analysis and k-nearest neighbors for the test set was up to 92% (99% with the original Hyperspectral images). Thus, the results of the study suggested that color-based detection may be viable as a multispectral imaging solution without much loss of prediction accuracy compared to Hyperspectral imaging.
Min Han - One of the best experts on this subject based on the ideXlab platform.
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IJCNN - Superpixel-based sparse representation classifier for Hyperspectral image
2016 International Joint Conference on Neural Networks (IJCNN), 2016Co-Authors: Min Han, Chengkun Zhang, Jun WangAbstract:This paper proposes a novel superpixel-based method for the classification of Hyperspectral image. A superpixel segmentation algorithm called entropy rate superpixel is applied to extract the spatial contextual information in the Hyperspectral image, which can change the size and shape of the superpixel adaptively according to spatial structures. Then, a joint sparse representation model is applied to approximate the pixels within each superpixel using a certain number of common samples from a given dictionary in the form of sparse linear combination. Here we use a greedy algorithm called simultaneous orthogonal matching pursuit to pursue the optimal sparse coefficients matrix and a new kind of classification criterion is tested and used to determine the classification results. Experimental results on the Indian Pines hyperspsectral image demonstrate that the proposed method can explore the spatial information effectively and give promising performance when compared with several state-of-art classification methods.
