# Deep Learning for Classification of Hyperspectral Data: A Comparative Review

arXiv: https://arxiv.org/abs/1904.10674 · HTML: https://mlsanity.com/paper/1904.10674/

## Key facts

- Cited by 51 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Apr 2019.

## Built on by (most cited first, 30 of 51)

- [Seeing biodiversity: perspectives in machine learning for wildlife conservation](https://mlsanity.com/paper/2110.12951/) (2021)
- [SpectralEarth: Training Hyperspectral Foundation Models at Scale](https://mlsanity.com/paper/2408.08447/) (2024)
- [SpectralMamba: Efficient Mamba for Hyperspectral Image Classification](https://mlsanity.com/paper/2404.08489/) (2024)
- [A Trainable Spectral-Spatial Sparse Coding Model for Hyperspectral Image Restoration](https://mlsanity.com/paper/2111.09708/) (2021)
- [A Review on Deep Learning in UAV Remote Sensing](https://mlsanity.com/paper/2101.10861/) (2021)
- [Towards a Collective Agenda on AI for Earth Science Data Analysis](https://mlsanity.com/paper/2104.05107/) (2021)
- [HSIGene: A Foundation Model For Hyperspectral Image Generation](https://mlsanity.com/paper/2409.12470/) (2024)
- [Imbalanced Big Data Oversampling: Taxonomy, Algorithms, Software, Guidelines and Future Directions](https://mlsanity.com/paper/2107.11508/) (2021)
- [Deep Reinforcement Learning for Band Selection in Hyperspectral Image Classification](https://mlsanity.com/paper/2103.08741/) (2021)
- [Aerial Scene Parsing: From Tile-level Scene Classification to Pixel-wise Semantic Labeling](https://mlsanity.com/paper/2201.01953/) (2022)
- [Toulouse Hyperspectral Data Set: a benchmark data set to assess semi-supervised spectral representation learning and pixel-wise classification techniques](https://mlsanity.com/paper/2311.08863/) (2023)
- [Hyperspectral Pansharpening: Critical Review, Tools and Future Perspectives](https://mlsanity.com/paper/2407.01355/) (2024)
- [Large-Scale Hyperspectral Image Clustering Using Contrastive Learning](https://mlsanity.com/paper/2111.07945/) (2021)
- [Optical Remote Sensing Image Understanding with Weak Supervision: Concepts, Methods, and Perspectives](https://mlsanity.com/paper/2204.09120/) (2022)
- [Unsupervised Segmentation of Hyperspectral Remote Sensing Images with Superpixels](https://mlsanity.com/paper/2204.12296/) (2022)
- [Graph Neural Networks Extract High-Resolution Cultivated Land Maps from Sentinel-2 Image Series](https://mlsanity.com/paper/2208.02349/) (2022)
- [Self Supervised Learning for Few Shot Hyperspectral Image Classification](https://mlsanity.com/paper/2206.12117/) (2022)
- [Physics-informed Variational Autoencoders for Improved Robustness to Environmental Factors of Variation](https://mlsanity.com/paper/2210.10418/) (2022)
- [DiffSpectralNet : Unveiling the Potential of Diffusion Models for Hyperspectral Image Classification](https://mlsanity.com/paper/2312.12441/) (2023)
- [Unsupervised Hyperspectral and Multispectral Image Blind Fusion Based on Deep Tucker Decomposition Network with Spatial-Spectral Manifold Learning](https://mlsanity.com/paper/2409.09670/) (2024)
- [HyperCap: Hyperspectral Land Cover Captioning Dataset for Vision Language Models](https://mlsanity.com/paper/2505.12217/) (2025)
- [Multi-Scale Spectral Attention Module-based Hyperspectral Segmentation in Autonomous Driving Scenarios](https://mlsanity.com/paper/2506.18682/) (2025)
- [GAF-NAU: Gramian Angular Field encoded Neighborhood Attention U-Net for Pixel-Wise Hyperspectral Image Classification](https://mlsanity.com/paper/2204.10099/) (2022)
- [Bayesian Convolutional Neural Networks for Limited Data Hyperspectral Remote Sensing Image Classification](https://mlsanity.com/paper/2205.09250/) (2022)
- [Probabilistic Deep Metric Learning for Hyperspectral Image Classification](https://mlsanity.com/paper/2211.08349/) (2022)
- [Remote Sensing Scene Classification with Masked Image Modeling (MIM)](https://mlsanity.com/paper/2302.14256/) (2023)
- [Supervised Embedded Methods for Hyperspectral Band Selection](https://mlsanity.com/paper/2401.11420/) (2024)
- [AVHYAS: A Free and Open Source QGIS Plugin for Advanced Hyperspectral Image Analysis](https://mlsanity.com/paper/2106.12776/) (2021)
- [Multi-modal land cover mapping of remote sensing images using pyramid attention and gated fusion networks](https://mlsanity.com/paper/2111.03845/) (2021)
- [A Multibranch Convolutional Neural Network for Hyperspectral Unmixing](https://mlsanity.com/paper/2208.02361/) (2022)

## Abstract (arXiv)

In recent years, deep learning techniques revolutionized the way remote sensing data are processed. Classification of hyperspectral data is no exception to the rule, but has intrinsic specificities which make application of deep learning less straightforward than with other optical data. This article presents a state of the art of previous machine learning approaches, reviews the various deep learning approaches currently proposed for hyperspectral classification, and identifies the problems and difficulties which arise to implement deep neural networks for this task. In particular, the issues of spatial and spectral resolution, data volume, and transfer of models from multimedia images to hyperspectral data are addressed. Additionally, a comparative study of various families of network architectures is provided and a software toolbox is publicly released to allow experimenting with these methods. 1 This article is intended for both data scientists with interest in hyperspectral data and remote sensing experts eager to apply deep learning techniques to their own dataset.