Parametric prediction of the POD and PFA for reflective hyperspectral imaging systems: dependencies on target, scene and sensor design characteristics, and detection algorithms

2006 ◽  
Author(s):  
Edward M. Bassett III ◽  
Terrence S. Lomheim ◽  
Jeffrey A. Lang ◽  
Thomas L. Hayhurst
Keyword(s):  
Hyperspectral Imaging ◽  
Imaging Systems ◽  
Sensor Design ◽  
Detection Algorithms ◽  
Design Characteristics

Detection Algorithms in Hyperspectral Imaging Systems: An Overview of Practical Algorithms

2014 ◽  
Vol 31 (1) ◽  
pp. 24-33 ◽  
Author(s):  
Dimitris Manolakis ◽  
Eric Truslow ◽  
Michael Pieper ◽  
Thomas Cooley ◽  
Michael Brueggeman
Keyword(s):  
Hyperspectral Imaging ◽  
Imaging Systems ◽  
Detection Algorithms ◽  
Practical Algorithms

The design of wavelets for limited-angle tomographic hyperspectral imaging systems

2010 ◽  
Author(s):  
James F. Scholl ◽  
E. Keith Hege ◽  
Eustace L. Dereniak
Keyword(s):  
Hyperspectral Imaging ◽  
Imaging Systems ◽  
Limited Angle

scholarly journals Information efficiency in hyperspectral imaging systems

2002 ◽  
Vol 11 (3) ◽  
pp. 347 ◽  
Author(s):  
Stephen E. Reichenbach ◽  
Luyin Cao ◽  
Ram M. Narayanan
Keyword(s):  
Hyperspectral Imaging ◽  
Imaging Systems ◽  
Information Efficiency

scholarly journals Special Section Guest Editorial: Hyperspectral Imaging Systems

2012 ◽  
Vol 51 (11) ◽  
pp. 111701
Author(s):  
John N. Lee ◽  
Christopher G. Simi
Keyword(s):  
Hyperspectral Imaging ◽  
Guest Editorial ◽  
Special Section ◽  
Imaging Systems

scholarly journals Near-infrared Spectroscopy and Hyperspectral Imaging for Sugar Content Evaluation in Potatoes over Multiple Growing Seasons

2020 ◽  
Author(s):  
Ahmed M Rady ◽  
Daniel E Guyer ◽  
Nicholas J Watson
Keyword(s):  
Hyperspectral Imaging ◽  
Near Infrared ◽  
Multispectral Imaging ◽  
Sucrose Concentration ◽  
Sugar Content ◽  
Imaging Systems ◽  
Potato Tubers ◽  
Forward Selection ◽  
Concentration Data ◽  
Growing Seasons

Abstract Sugar content is one of the most important properties of potato tubers as it directly affects their processing and the final product quality, especially for fried products. In this study, data obtained from spectroscopic (interactance and reflectance) and hyperspectral imaging systems were used individually or fused to develop non-cultivar nor growing season-specific regression and classification models for potato tubers based on glucose and sucrose concentration. Data was acquired over three growing seasons for two potato cultivars. The most influential wavelengths were selected from the imaging systems using interval partial least squares for regression and sequential forward selection for classification. Hyperspectral imaging showed the highest regression performance for glucose with a correlation coefficient (ratio of performance to deviation) or r(RPD) of 91.8(2.41) which increased to 94%(2.91) when the data was fused with the interactance data. The sucrose regression results had the highest accuracy using data obtained from the interactance system with r(RPD) values of 74.5%(1.40) that increased to 84.4%(1.82) when the data was fused with the reflectance data. Classification was performed to identify tubers with either high or low sugar content. Classification performance showed accuracy values as high as 95% for glucose and 80.1% for sucrose using hyperspectral imaging, with no noticeable improvement when data was fused from the other spectroscopic systems. When testing the robustness of the developed models over different seasons, it was found that the regression models had r(RPD) values of 55(1.19)–90.3%(2.34) for glucose and 35.8(1.07)–82.2%(1.29) for sucrose. Results obtained in this study demonstrate the feasibility of developing a rapid monitoring system using multispectral imaging and data fusion methods for online evaluation of potato sugar content.

scholarly journals Rapid and Nondestructive Discrimination of Geographical Origins of Longjing Tea using Hyperspectral Imaging at Two Spectral Ranges Coupled with Machine Learning Methods

2020 ◽  
Vol 10 (3) ◽  
pp. 1173 ◽  
Author(s):  
Zhiqi Hong ◽  
Yong He
Keyword(s):  
Hyperspectral Imaging ◽  
Nutritional Value ◽  
Geographical Origin ◽  
Principal Component ◽  
Hyperspectral Images ◽  
Imaging Systems ◽  
Support Vector ◽  
Spectral Ranges ◽  
Tea Leaf ◽  
Non Destructive

Longjing tea is one of China’s protected geographical indication products with high commercial and nutritional value. The geographical origin of Longjing tea is an important factor influencing its commercial and nutritional value. Hyperspectral imaging systems covering the two spectral ranges of 380–1030 nm and 874–1734 nm were used to identify a single tea leaf of Longjing tea from six geographical origins. Principal component analysis (PCA) was conducted on hyperspectral images to form PCA score images. Differences among samples from different geographical origins were visually observed from the PCA score images. Support vector machine (SVM) and partial least squares discriminant analysis (PLS-DA) models were built using the full spectra at the two spectral ranges. Decent classification performances were obtained at the two spectral ranges, with the overall classification accuracy of the calibration and prediction sets over 84%. Furthermore, prediction maps for geographical origins identification of Longjing tea were obtained by applying the SVM models on the hyperspectral images. The overall results illustrate that hyperspectral imaging at both spectral ranges can be applied to identify the geographical origin of single tea leaves of Longjing tea. This study provides a new, rapid, and non-destructive alternative for Longjing tea geographical origins identification.

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