HyDe: The First Open-Source, Python-Based, GPU-Accelerated Hyperspectral Denoising Package

TL;DR

HyDe is an open-source, GPU-accelerated Python toolbox for hyperspectral image denoising, integrating various methods including deep neural networks, with improved efficiency and usability for real-world applications.

Contribution

It introduces the first open-source, GPU-accelerated hyperspectral denoising package with a unified interface and a novel training method for diverse hyperspectral data.

Findings

Methods maintain similar denoising performance to original implementations.

HyDe consumes nearly ten times less energy than traditional methods.

Effective denoising of large HSIs with limited memory using sliding window approach.

Abstract

As with any physical instrument, hyperspectral cameras induce different kinds of noise in the acquired data. Therefore, Hyperspectral denoising is a crucial step for analyzing hyperspectral images (HSIs). Conventional computational methods rarely use GPUs to improve efficiency and are not fully open-source. Alternatively, deep learning-based methods are often open-source and use GPUs, but their training and utilization for real-world applications remain non-trivial for many researchers. Consequently, we propose HyDe: the first open-source, GPU-accelerated Python-based, hyperspectral image denoising toolbox, which aims to provide a large set of methods with an easy-to-use environment. HyDe includes a variety of methods ranging from low-rank wavelet-based methods to deep neural network (DNN) models. HyDe's interface dramatically improves the interoperability of these methods and the performance of the underlying functions. In fact, these methods maintain similar HSI denoising performance to their original implementations while consuming nearly ten times less energy. Furthermore, we present a method for training DNNs for denoising HSIs which are not spatially related to the training dataset, i.e., training on ground-level HSIs for denoising HSIs with other perspectives including airborne, drone-borne, and space-borne. To utilize the trained DNNs, we show a sliding window method to effectively denoise HSIs which would otherwise require more than 40 GB. The package can be found at: \url{https://github.com/Helmholtz-AI-Energy/HyDe}.