HySpecNet-11k: A Large-Scale Hyperspectral Dataset for Benchmarking Learning-Based Hyperspectral Image Compression Methods

TL;DR

HySpecNet-11k is a large-scale hyperspectral dataset with over 11,000 image patches, designed to facilitate training and benchmarking of learning-based hyperspectral image compression methods and other unsupervised tasks.

Contribution

The paper introduces HySpecNet-11k, a comprehensive hyperspectral dataset that enables effective training and evaluation of advanced compression algorithms and other learning-based hyperspectral analysis methods.

Findings

Benchmarking of state-of-the-art autoencoder architectures on HySpecNet-11k.

Demonstration of HySpecNet-11k's utility for training deep learning models.

Public availability of dataset, code, and pre-trained models.

Abstract

The development of learning-based hyperspectral image compression methods has recently attracted great attention in remote sensing. Such methods require a high number of hyperspectral images to be used during training to optimize all parameters and reach a high compression performance. However, existing hyperspectral datasets are not sufficient to train and evaluate learning-based compression methods, which hinders the research in this field. To address this problem, in this paper we present HySpecNet-11k that is a large-scale hyperspectral benchmark dataset made up of 11,483 nonoverlapping image patches. Each patch is a portion of 128 $\times$ 128 pixels with 224 spectral bands and a ground sample distance of 30 m. We exploit HySpecNet-11k to benchmark the current state of the art in learning-based hyperspectral image compression by focussing our attention on various 1D, 2D and 3D convolutional autoencoder architectures. Nevertheless, HySpecNet-11k can be used for any unsupervised learning task in the framework of hyperspectral image analysis. The dataset, our code and the pre-trained weights are publicly available at https://hyspecnet.rsim.berlin