Three-Dimensional Fourier Scattering Transform and Classification of Hyperspectral Images

Abstract

Recent research has resulted in many new techniques that are able to capture the special properties of hyperspectral data for hyperspectral image analysis, with hyperspectral image classification as one of the most active tasks. Timefrequency methods decompose spectra into multi-spectral bands, while hierarchical methods like neural networks incorporate spatial information across scales and model multiple levels of dependencies between spectral features. The Fourier scattering transform is an amalgamation of time-frequency representations with neural network architectures, both of which have recently been proven to provide significant advances in spectral-spatial classification. We test the proposed three dimensional Fourier scattering method on four standard hyperspectral datasets, and present results that indicate that the Fourier scattering transform is highly effective at representing spectral data when compared with other state-of-the-art spectral-spatial classification methods.

How to quantify fields or textures? A guide to the scattering transform

“…So far, it has been used primarily in audio/visual signal processing (e.g., Andén & Mallat 2011;Bruna & Mallat 2013;Sifre & Mallat 2013;Andén & Mallat 2014). It has already been used in a number of scientific applications: intermittency in turbulence (Bruna et al 2015), quantum chemistry and material science (Hirn et al 2017;Eickenberg et al 2018;Sinz et al 2020), plasma physics (Glinsky et al 2020), geography (Kavalerov et al 2019), astrophysics (Allys et al 2019Saydjari et al 2021;Regaldo-Saint Blancard et al 2020), and cosmology (Cheng et al 2020;Cheng & Ménard 2021). In several of these applications, the scattering transform reached state-of-the-art performance compared to the CNNs in use at the time.…”