# Hyperspectral Anomaly Detection Methods: A Survey and Comparative Study

arXiv: https://arxiv.org/abs/2507.05730 · HTML: https://mlsanity.com/paper/2507.05730/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 75 references that are in the map.
- First posted on arXiv 8 Jul 2025.

## Builds on (most cited first, 10 of 10)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Coarse-to-Fine Sparse Transformer for Hyperspectral Image Reconstruction](https://mlsanity.com/paper/2203.04845/) (2022)
- [3D Quasi-Recurrent Neural Network for Hyperspectral Image Denoising](https://mlsanity.com/paper/2003.04547/) (2020)
- [Successful Combination of Database Search and Snowballing for Identification of Primary Studies in Systematic Literature Studies](https://mlsanity.com/paper/2307.02612/) (2023)
- [GAN-based Hyperspectral Anomaly Detection](https://mlsanity.com/paper/2007.02441/) (2020)
- [Anomaly Detection Based on Isolation Mechanisms: A Survey](https://mlsanity.com/paper/2403.10802/) (2024)
- [One-Step Detection Paradigm for Hyperspectral Anomaly Detection via Spectral Deviation Relationship Learning](https://mlsanity.com/paper/2303.12342/) (2023)
- [Efficient Nonlinear RX Anomaly Detectors](https://mlsanity.com/paper/2012.05799/) (2020)
- [ERX: A Fast Real-Time Anomaly Detection Algorithm for Hyperspectral Line Scanning](https://mlsanity.com/paper/2408.14947/) (2024)
- [A spectral-spatial fusion anomaly detection method for hyperspectral imagery](https://mlsanity.com/paper/2202.11889/) (2022)

## Abstract (arXiv)

Hyperspectral images are high-dimensional datasets comprising hundreds of contiguous spectral bands, enabling detailed analysis of materials and surfaces. Hyperspectral anomaly detection (HAD) refers to the technique of identifying and locating anomalous targets in such data without prior information about a hyperspectral scene or target spectrum. This technology has seen rapid advancements in recent years, with applications in agriculture, defence, military surveillance, and environmental monitoring. Despite this significant progress, existing HAD methods continue to face challenges such as high computational complexity, sensitivity to noise, and limited generalisation across diverse datasets. This study presents a comprehensive comparison of various HAD techniques, categorising them into statistical models, representation-based methods, classical machine learning approaches, and deep learning models. We evaluated these methods across 17 benchmarking datasets using different performance metrics, such as ROC, AUC, and separability map to analyse detection accuracy, computational efficiency, their strengths, limitations, and directions for future research. Our findings highlight that deep learning models achieved the highest detection accuracy, while statistical models demonstrated exceptional speed across all datasets. This survey aims to provide valuable insights for researchers and practitioners working to advance the field of hyperspectral anomaly detection methods.