scholarly journals Comparison of spectral selection methods in the development of classification models from visible near infrared hyperspectral imaging data

2019 ◽  
Author(s):  
Aoife Gowen ◽  
Jun-Li Xu ◽  
Ana Herrero-Langreo
Keyword(s):  
Hyperspectral Imaging ◽  
Near Infrared ◽  
Spatial Information ◽  
Hyperspectral Image ◽  
Imaging Spectroscopy ◽  
Model Performance ◽  
Data Sampling ◽  
Classification Models ◽  
Imaging Data ◽  
Selection Of

Applications of hyperspectral imaging (HSI) to the quantitative and qualitative measurement of samples have grown widely in recent years, due mainly to the improved performance and lower cost of imaging spectroscopy instrumentation. Data sampling is a crucial yet often overlooked step in hyperspectral image analysis, which impacts the subsequent results and their interpretation. In the selection of pixel spectra for the calibration of classification models, the spatial information in HSI data can be exploited. In this paper, a variety of sampling strategies for selection of pixel spectra are presented, exemplified through five case studies. The strategies are compared in terms of the proportion of global variability captured, practicality and predictive model performance. The use of variographic analysis as a guide to the spatial segmentation prior to sampling leads to the selection of representative subsets while reducing the variation in model performance parameters over repeated random selection.

scholarly journals Hyperspectral and Multispectral Image Fusion Techniques

2020 ◽  
Vol 8 (5) ◽  
pp. 467-471
Keyword(s):  
Image Fusion ◽  
Hyperspectral Imaging ◽  
Near Infrared ◽  
Spatial Information ◽  
Hyperspectral Image ◽  
Qualitative Comparative Analysis ◽  
Infrared Region ◽  
Multispectral Image ◽  
Electromagnetic Spectrum ◽  
Fusion Methods

The hyper spectral image covers a broad range of wavelengths in electromagnetic spectrum, spanning from visible to near-infrared region. The basic objective of hyperspectral imaging is to attain the spectrum for each pixel in the image of a scene, with the aim of identifying objects in the scene and its classification. The hyperspectral images give detailed spectral information but their spatial resolution is very poor. So to enhance the visual quality of the hyperspectral image, we can perform image fusion with high spatial information multispectral image. This paper provides a complete description of hyperspectral imaging and image fusion methods of hyperspectral and multispectral images. A quantitative and qualitative comparative analysis on performance of various hyperspectral and multispectral image fusion techniques are also done.

scholarly journals Chemometric Tools for NIRS and NIR Hyperspectral Imaging

2012 ◽  
Vol 69 (1) ◽  
Author(s):  
Laura M. DALE ◽  
André THEWIS ◽  
Ioan ROTAR ◽  
Juan A. FERNANDEZ PIERNA ◽  
Christelle BOUDRY ◽  
...  
Keyword(s):  
Hyperspectral Imaging ◽  
Near Infrared ◽  
Agricultural Sector ◽  
Spatial Information ◽  
Imaging System ◽  
Agricultural Products ◽  
Chemical Information ◽  
Special Focus ◽  
Large Area ◽  
Analytical Tools

Nowadays in agriculture, new analytical tools based on spectroscopic technologies are developed. Near Infrared Spectroscopy (NIRS) is a well known technology in the agricultural sector allowing the acquisition of chemical information from the samples with a large number of advantages, such as: easy to use tool, fast and simultaneous analysis of several components, non-polluting, noninvasive and non destructive technology, and possibility of online or field implementation. Recently, NIRS system was combined with imaging technologies creating the Near Infrared Hyperspectral Imaging system (NIR-HSI). This technology provides simultaneously spectral and spatial information from an object. The main differences between NIR-HSI and NIRS is that many spectra can be recorded simultaneously from a large area of an object with the former while with NIRS only one spectrum was recorded for analysis on a small area. In this work, both technologies are presented with special focus on the main spectrum and images analysis methods. Several qualitative and quantitative applications of NIRS and NIR-HSI in agricultural products are listed. Developments of NIRS and NIR-HSI will enhance progress in the field of agriculture by providing high quality and safe agricultural products, better plant and grain selection techniques or compound feed industry’s productivity among others.

scholarly journals Assessing the Spectral Characteristics of Dye- and Pigment-Based Inkjet Prints by VNIR Hyperspectral Imaging

Sensors ◽  
2022 ◽  
Vol 22 (2) ◽  
pp. 603
Author(s):  
Lukáš Krauz ◽  
Petr Páta ◽  
Jan Kaiser
Keyword(s):  
Hyperspectral Imaging ◽  
Near Infrared ◽  
Hyperspectral Image ◽  
Spectral Characteristics ◽  
Principal Component ◽  
Hyperspectral Image Processing ◽  
Different Types ◽  
The Difference ◽  
Hyperspectral Camera

Fine art photography, paper documents, and other parts of printing that aim to keep value are searching for credible techniques and mediums suitable for long-term archiving purposes. In general, long-lasting pigment-based inks are used for archival print creation. However, they are very often replaced or forged by dye-based inks, with lower fade resistance and, therefore, lower archiving potential. Frequently, the difference between the dye- and pigment-based prints is hard to uncover. Finding a simple tool for countrified identification is, therefore, necessary. This paper assesses the spectral characteristics of dye- and pigment-based ink prints using visible near-infrared (VNIR) hyperspectral imaging. The main aim is to show the spectral differences between these ink prints using a hyperspectral camera and subsequent hyperspectral image processing. Two diverse printers were exploited for comparison, a hobby dye-based EPSON L1800 and a professional pigment-based EPSON SC-P9500. The identical prints created via these printers on three different types of photo paper were recaptured by the hyperspectral camera. The acquired pixel values were studied in terms of spectral characteristics and principal component analysis (PCA). In addition, the obtained spectral differences were quantified by the selected spectral metrics. The possible usage for print forgery detection via VNIR hyperspectral imaging is discussed in the results.

scholarly journals Identification of Weeds Based on Hyperspectral Imaging and Machine Learning

2021 ◽  
Vol 11 ◽  
Author(s):  
Yanjie Li ◽  
Mahmoud Al-Sarayreh ◽  
Kenji Irie ◽  
Deborah Hackell ◽  
Graeme Bourdot ◽  
...  
Keyword(s):  
Machine Learning ◽  
Hyperspectral Imaging ◽  
Support Vector ◽  
Classification Models ◽  
Imaging Data ◽  
Weed Species ◽  
Automated Identification ◽  
Standard Normal Variate ◽  
Californian Thistle ◽  
Ranunculus Acris

Weeds can be major environmental and economic burdens in New Zealand. Traditional methods of weed control including manual and chemical approaches can be time consuming and costly. Some chemical herbicides may have negative environmental and human health impacts. One of the proposed important steps for providing alternatives to these traditional approaches is the automated identification and mapping of weeds. We used hyperspectral imaging data and machine learning to explore the possibility of fast, accurate and automated discrimination of weeds in pastures where ryegrass and clovers are the sown species. Hyperspectral images from two grasses (Setaria pumila [yellow bristle grass] and Stipa arundinacea [wind grass]) and two broad leaf weed species (Ranunculus acris [giant buttercup] and Cirsium arvense [Californian thistle]) were acquired and pre-processed using the standard normal variate method. We trained three classification models, namely partial least squares-discriminant analysis, support vector machine, and Multilayer Perceptron (MLP) using whole plant averaged (Av) spectra and superpixels (Sp) averaged spectra from each weed sample. All three classification models showed repeatable identification of four weeds using both Av and Sp spectra with a range of overall accuracy of 70–100%. However, MLP based on the Sp method produced the most reliable and robust prediction result (89.1% accuracy). Four significant spectral regions were found as highly informative for characterizing the four weed species and could form the basis for a rapid and efficient methodology for identifying weeds in ryegrass/clover pastures.

scholarly journals Selection of NIR wavelengths from hyperspectral imaging data for the quality evaluation of Acerola fruit

2015 ◽  
Author(s):  
Cristina Malegori ◽  
Aoife Gowen ◽  
Emanuel Marques ◽  
Maria Fernanda Pimentel Avelar ◽  
Sergio Freitas ◽  
...  
Keyword(s):  
Hyperspectral Imaging ◽  
Quality Evaluation ◽  
Imaging Data ◽  
Selection Of

Prediction of water content in biscuits using near infrared hyperspectral imaging spectroscopy and chemometrics

2020 ◽  
Vol 28 (3) ◽  
pp. 140-147
Author(s):  
Eloïse Lancelot ◽  
Philippe Courcoux ◽  
Sylvie Chevallier ◽  
Alain Le-Bail ◽  
Benoît Jaillais
Keyword(s):  
Linear Regression ◽  
Multiple Linear Regression ◽  
Water Content ◽  
Hyperspectral Imaging ◽  
Analysis Of Variance ◽  
Near Infrared ◽  
Imaging Spectroscopy ◽  
Principal Component ◽  
Coefficient Of Determination ◽  
Water Contents

The possibility of using near infrared hyperspectral imaging spectroscopy to quantify the water content in commercial biscuits was investigated. Principal component analysis was successfully applied to hyperspectral images of commercial biscuits to monitor their water contents. Variables were selected and water contents quantified using analysis of variance, followed by multiple linear regression, and the results were compared with those obtained with variable importance in projection partial least squares. Initially equal to 212, the number of variables after application of analysis of variance was equal to 10. Analysis of variance–multiple linear regression gave better results: the coefficient of determination (R2) was higher than 0.92 and the root mean square error of validation was less than 0.015. The “prediction images” obtained were very relevant and can be used to study biscuit defects. The methodology developed could be implemented at the industrial level for biscuit quality control and for online monitoring of the uniform distribution of water in the superficial layer of biscuits.

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