Tutorial: Time series hyperspectral image analysis

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Title: Tutorial: Time series hyperspectral image analysis
Authors: Dorrepaal, Ronan
Malegori, Cristina
Gowen, Aoife
Permanent link: http://hdl.handle.net/10197/7616
Date: 22-Apr-2016
Abstract: A hyperspectral image is a large dataset in which each pixel corresponds to a spectrum, thus providing high-quality detail of a sample surface. Hyperspectral images are thus characterised by dual information, spectral and spatial, which allows for the acquisition of both qualitative and quantitative information from a sample. A hyperspectral image, commonly known as a 'hypercube', comprises two spatial dimensions and one spectral dimension. The data of such a file contain both chemical and physical information. Such files need to be analysed with a computational 'chemometric' approach in order to reduce the dimensionality of the data, while retaining the most useful spectral information. Time series hyperspectral imaging data comprise multiple hypercubes, each presenting the sample at a different time point, requiring additional considerations in the data analysis. This paper provides a step-by-step tutorial for time series hyperspectral data analysis, with detailed command line scripts in the Matlab and R computing languages presented in the supplementary data. The example time series data, available for download, are a set of time series hyperspectral images following the setting of a cement-based biomaterial. Starting from spectral pre-processing (image acquisition, background removal, dead pixels and spikes, masking) and pre-treatments, the typical steps encountered in time series hyperspectral image processing are then presented, including unsupervised and supervised chemometric methods. At the end of the tutorial paper, some general guidelines on hyperspectral image processing are proposed.
Funding Details: European Commission - Seventh Framework Programme (FP7)
European Research Council
Type of material: Journal Article
Publisher: NIR Publications
Copyright (published version): 2016 IM Publications LLP
Keywords: Hyperspectral;Chemometrics;Matlab;R;Spike-detection;Pre-treatment;Masking;Training;Calibration;Validation
DOI: 10.1255/jnirs.1208
Language: en
Status of Item: Peer reviewed
Appears in Collections:Biosystems and Food Engineering Research Collection

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