# Generative Adversarial Networks for Realistic Synthesis of Hyperspectral Samples

arXiv: https://arxiv.org/abs/1806.02583 · HTML: https://mlsanity.com/paper/1806.02583/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Jun 2018.

## Built on by (most cited first, 9 of 9)

- [Generating Physically-Consistent Satellite Imagery for Climate Visualizations](https://mlsanity.com/paper/2104.04785/) (2021)
- [SatDM: Synthesizing Realistic Satellite Image with Semantic Layout Conditioning using Diffusion Models](https://mlsanity.com/paper/2309.16812/) (2023)
- [Diffusion Models for Earth Observation Use-cases: from cloud removal to urban change detection](https://mlsanity.com/paper/2311.06222/) (2023)
- [A Multibranch Convolutional Neural Network for Hyperspectral Unmixing](https://mlsanity.com/paper/2208.02361/) (2022)
- [Language-Informed Hyperspectral Image Synthesis for Imbalanced-Small Sample Classification via Semi-Supervised Conditional Diffusion Model](https://mlsanity.com/paper/2502.19700/) (2025)
- [Quantum Machine Learning for Remote Sensing: Exploring potential and challenges](https://mlsanity.com/paper/2311.07626/) (2023)
- [Ai4Fapar: How artificial intelligence can help to forecast the seasonal earth observation signal](https://mlsanity.com/paper/2402.06684/) (2024)
- [Leveraging Multi-Temporal Sentinel 1 and 2 Satellite Data for Leaf Area Index Estimation With Deep Learning](https://mlsanity.com/paper/2410.19787/) (2024)
- [Conditional Generative Framework with Peak-Aware Attention for Robust Chemical Detection under Interferences](https://mlsanity.com/paper/2601.21246/) (2026)

## Abstract (arXiv)

This work addresses the scarcity of annotated hyperspectral data required to train deep neural networks. Especially, we investigate generative adversarial networks and their application to the synthesis of consistent labeled spectra. By training such networks on public datasets, we show that these models are not only able to capture the underlying distribution, but also to generate genuine-looking and physically plausible spectra. Moreover, we experimentally validate that the synthetic samples can be used as an effective data augmentation strategy. We validate our approach on several public hyper-spectral datasets using a variety of deep classifiers.