# GAN-based Hyperspectral Anomaly Detection

arXiv: https://arxiv.org/abs/2007.02441 · HTML: https://mlsanity.com/paper/2007.02441/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Jul 2020.

## Built on by (most cited first, 10 of 10)

- [Applications of Generative Adversarial Networks in Anomaly Detection: A Systematic Literature Review](https://mlsanity.com/paper/2110.12076/) (2021)
- [One-Step Detection Paradigm for Hyperspectral Anomaly Detection via Spectral Deviation Relationship Learning](https://mlsanity.com/paper/2303.12342/) (2023)
- [Learning a Cross-modality Anomaly Detector for Remote Sensing Imagery](https://mlsanity.com/paper/2310.07511/) (2023)
- [Diffusion Models for Hyperspectral Image Analysis: A Comprehensive Review](https://mlsanity.com/paper/2505.11158/) (2025)
- [AI-Driven HSI: Multimodality, Fusion, Challenges, and the Deep Learning Revolution](https://mlsanity.com/paper/2502.06894/) (2025)
- [CL-CaGAN: Capsule differential adversarial continuous learning for cross-domain hyperspectral anomaly detection](https://mlsanity.com/paper/2505.11793/) (2025)
- [Turbo-GoDec: Exploiting the Cluster Sparsity Prior for Hyperspectral Anomaly Detection](https://mlsanity.com/paper/2601.12337/) (2026)
- [Utilizing the Score of Data Distribution for Hyperspectral Anomaly Detection](https://mlsanity.com/paper/2601.12379/) (2026)
- [CL-BioGAN: Biologically-Inspired Cross-Domain Continual Learning for Hyperspectral Anomaly Detection](https://mlsanity.com/paper/2505.11796/) (2025)
- [Hyperspectral Anomaly Detection Methods: A Survey and Comparative Study](https://mlsanity.com/paper/2507.05730/) (2025)

## Abstract (arXiv)

In this paper, we propose a generative adversarial network (GAN)-based hyperspectral anomaly detection algorithm. In the proposed algorithm, we train a GAN model to generate a synthetic background image which is close to the original background image as much as possible. By subtracting the synthetic image from the original one, we are able to remove the background from the hyperspectral image. Anomaly detection is performed by applying Reed-Xiaoli (RX) anomaly detector (AD) on the spectral difference image. In the experimental part, we compare our proposed method with the classical RX, Weighted-RX (WRX) and support vector data description (SVDD)-based anomaly detectors and deep autoencoder anomaly detection (DAEAD) method on synthetic and real hyperspectral images. The detection results show that our proposed algorithm outperforms the other methods in the benchmark.