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Exploring local spatial features in hyperspectral images
Author(s) -
Ahmad Mohamad,
Vitale Raffaele,
Silva Carolina S.,
Ruckebusch Cyril,
Cocchi Marina
Publication year - 2020
Publication title -
journal of chemometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.47
H-Index - 92
eISSN - 1099-128X
pISSN - 0886-9383
DOI - 10.1002/cem.3295
Subject(s) - hyperspectral imaging , principal component analysis , pattern recognition (psychology) , data cube , artificial intelligence , wavelet , computer science , wavelet transform , image resolution , spatial analysis , full spectral imaging , context (archaeology) , mathematics , data mining , geology , statistics , paleontology
We propose a methodological framework to extract spatial features in hyperspectral imaging data and establish a link between these features and the spectral regions, capturing the observed structural patterns. The proposed approach consists of five main steps: (i) two‐dimensional stationary wavelet transform (2D‐SWT) is applied to a hyperspectral data cube, decomposing each single‐channel image with a selected wavelet filter up to the maximum decomposition level; (ii) a gray‐level co‐occurrence matrix is calculated for every 2D‐SWT image resulting from stage (i); (iii) distinctive spatial features are determined by computing morphological descriptors from each gray‐level co‐occurrence matrix; (iv) the morphological descriptors are rearranged in a two‐dimensional data array; and (v) this data matrix is subjected to principal component analysis (PCA) for exploring the variability of the aforementioned descriptors across spectral channels. As a result, groups of spectral wavelengths associated to specific spatial features can be pointed out yielding a better understanding and interpretation of the data. In principle, this information can also be further exploited, for example, to improve the separation of pure spectral profiles in a multivariate curve resolution context.