Material Identification in Complex Environments: Neural Network Approaches to Hyperspectral Image Analysis

Sun, 01 Oct 2023 00:00:00 +0000

This study examines plastic identification in complex hyperspectral scenes, where illumination, background materials, spectral mixing, and within-class variation make pixel-level classification substantially harder than controlled laboratory sorting. The data include both visible and near-infrared measurements, making it possible to compare the practical value of different wavelength ranges.

The paper evaluates neural feature-learning strategies—including autoencoders and contrastive learning—alongside established hyperspectral classification approaches. The comparison focuses on how well the learned representations separate plastic from confounding materials under realistic scene variability, and on which sensor spectrum is most informative for this recycling application.

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Material Identification in Complex Environments: Neural Network Approaches to Hyperspectral Image Analysis