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

arXiv: https://arxiv.org/abs/2306.00385 · HTML: https://mlsanity.com/paper/2306.00385/

## Key facts

- Cited by 26 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 14 references that are in the map.
- First posted on arXiv 1 Jun 2023.

## Builds on (most cited first, 3 of 3)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift](https://mlsanity.com/paper/1502.03167/) (2015)
- [CompressAI: a PyTorch library and evaluation platform for end-to-end compression research](https://mlsanity.com/paper/2011.03029/) (2020)

## Built on by (most cited first, 26 of 26)

- [Neural Plasticity-Inspired Multimodal Foundation Model for Earth Observation](https://mlsanity.com/paper/2403.15356/) (2024)
- [SpectralEarth: Training Hyperspectral Foundation Models at Scale](https://mlsanity.com/paper/2408.08447/) (2024)
- [One for All: Toward Unified Foundation Models for Earth Vision](https://mlsanity.com/paper/2401.07527/) (2024)
- [HSIGene: A Foundation Model For Hyperspectral Image Generation](https://mlsanity.com/paper/2409.12470/) (2024)
- [Lossy Neural Compression for Geospatial Analytics: A Review](https://mlsanity.com/paper/2503.01505/) (2025)
- [Onboard deep lossless and near-lossless predictive coding of hyperspectral images with line-based attention](https://mlsanity.com/paper/2403.17677/) (2024)
- [GreenHyperSpectra: A multi-source hyperspectral dataset for global vegetation trait prediction](https://mlsanity.com/paper/2507.06806/) (2025)
- [Unlocking the Potential: Multi-task Deep Learning for Spaceborne Quantitative Monitoring of Fugitive Methane Plumes](https://mlsanity.com/paper/2401.12870/) (2024)
- [TerraMAE: Learning Spatial-Spectral Representations from Hyperspectral Earth Observation Data via Adaptive Masked Autoencoders](https://mlsanity.com/paper/2508.07020/) (2025)
- [HyperFM: An Efficient Hyperspectral Foundation Model with Spectral Grouping](https://mlsanity.com/paper/2604.21127/) (2026)
- [HyCoT: A Transformer-Based Autoencoder for Hyperspectral Image Compression](https://mlsanity.com/paper/2408.08700/) (2024)
- [Multispectral to Hyperspectral using Pretrained Foundational model](https://mlsanity.com/paper/2502.19451/) (2025)
- [Hyperspectral Vision Transformers for Greenhouse Gas Estimations from Space](https://mlsanity.com/paper/2504.16851/) (2025)
- [CARL: Camera-Agnostic Representation Learning for Spectral Image Analysis](https://mlsanity.com/paper/2504.19223/) (2025)
- [SpectralEarth-FM: Bringing Hyperspectral Imagery into Multimodal Earth Observation Pretraining](https://mlsanity.com/paper/2605.21075/) (2026)
- [Deep-Learning-based Change Detection with Spaceborne Hyperspectral PRISMA data](https://mlsanity.com/paper/2310.13627/) (2023)
- [HyBiomass: Global Hyperspectral Imagery Benchmark Dataset for Evaluating Geospatial Foundation Models in Forest Aboveground Biomass Estimation](https://mlsanity.com/paper/2506.11314/) (2025)
- [Onboard Hyperspectral Super-Resolution with Deep Pushbroom Neural Network](https://mlsanity.com/paper/2507.20765/) (2025)
- [Adjustable Spatio-Spectral Hyperspectral Image Compression Network](https://mlsanity.com/paper/2507.23447/) (2025)
- [HYDRA: HYbrid knowledge Distillation and spectral Reconstruction Algorithm for high channel hyperspectral camera applications](https://mlsanity.com/paper/2510.16664/) (2025)
- [SpecAware: A Spectral-Content Aware Foundation Model for Unifying Multi-Sensor Learning in Hyperspectral Remote Sensing Mapping](https://mlsanity.com/paper/2510.27219/) (2025)
- [MCGA: Mixture of Codebooks Hyperspectral Reconstruction via Grayscale-Aware Attention](https://mlsanity.com/paper/2507.09885/) (2025)
- [HyVIC: A Metric-Driven Spatio-Spectral Hyperspectral Image Compression Architecture Based on Variational Autoencoders](https://mlsanity.com/paper/2603.26468/) (2026)
- [ChronoEarth-492K: A Large Scale and Long Horizon Spatiotemporal Hyperspectral Earth Observation Dataset and Benchmark](https://mlsanity.com/paper/2605.15666/) (2026)
- [HyperImageNet: A Large-Scale High-Spatial Resolution Hyperspectral Imagery Classification Benchmark](https://mlsanity.com/paper/2607.21050/) (2026)
- [HyperSAM: A Promptable Foundation Model for Hyperspectral Remote Sensing](https://mlsanity.com/paper/2609.37340/) (2026)

## Abstract (arXiv)

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 × 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