The role of the continuous wavelet transform in mineral identification using hyperspectral imaging in the long-wave infrared by using SVM classifier

Saeed Sojasi; Bardia Yousefi; Kévin Liaigre; Clemente Ibarra-Castanedo; Georges Beaudoin; Xavier P. V. Maldague; François Huot; Martin Chamberland

doi:10.1117/12.2264580

5 May 2017 The role of the continuous wavelet transform in mineral identification using hyperspectral imaging in the long-wave infrared by using SVM classifier

Saeed Sojasi, Bardia Yousefi, Kévin Liaigre, Clemente Ibarra-Castanedo, Georges Beaudoin, Xavier P. V. Maldague, François Huot, Martin Chamberland

Author Affiliations +

Proceedings Volume 10214, Thermosense: Thermal Infrared Applications XXXIX; 102141K (2017) https://doi.org/10.1117/12.2264580
Event: SPIE Commercial + Scientific Sensing and Imaging, 2017, Anaheim, CA, United States

Abstract

Hyperspectral imaging (HSI) in the long-wave infrared spectrum (LWIR) provides spectral and spatial information concerning the emissivity of the surface of materials, which can be used for mineral identification. For this, an endmember, which is the purest form of a mineral, is used as reference. All pure minerals have specific spectral profiles in the electromagnetic wavelength, which can be thought of as the mineral’s fingerprint. The main goal of this paper is the identification of minerals by LWIR hyperspectral imaging using a machine learning scheme. The information of hyperspectral imaging has been recorded from the energy emitted from the mineral’s surface. Solar energy is the source of energy in remote sensing, while a heating element is the energy source employed in laboratory experiments. Our work contains three main steps where the first step involves obtaining the spectral signatures of pure (single) minerals with a hyperspectral camera, in the long-wave infrared (7.7 to 11.8 μm), which measures the emitted radiance from the minerals’ surface. The second step concerns feature extraction by applying the continuous wavelet transform (CWT) and finally we use support vector machine classifier with radial basis functions (SVM-RBF) for classification/identification of minerals. The overall accuracy of classification in our work is 90.23± 2.66%. In conclusion, based on CWT’s ability to capture the information of signals can be used as a good marker for classification and identification the minerals substance.

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Saeed Sojasi, Bardia Yousefi, Kévin Liaigre, Clemente Ibarra-Castanedo, Georges Beaudoin, Xavier P. V. Maldague, François Huot, and Martin Chamberland "The role of the continuous wavelet transform in mineral identification using hyperspectral imaging in the long-wave infrared by using SVM classifier", Proc. SPIE 10214, Thermosense: Thermal Infrared Applications XXXIX, 102141K (5 May 2017); https://doi.org/10.1117/12.2264580

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