# Machine learning based hyperspectral image analysis: A survey

arXiv: https://arxiv.org/abs/1802.08701 · HTML: https://mlsanity.com/paper/1802.08701/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Feb 2018.

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

- [A Comprehensive Survey for Hyperspectral Image Classification: The Evolution from Conventional to Transformers and Mamba Models](https://mlsanity.com/paper/2404.14955/) (2024)
- [A review of machine learning in processing remote sensing data for mineral exploration](https://mlsanity.com/paper/2103.07678/) (2021)
- [MethaneMapper: Spectral Absorption aware Hyperspectral Transformer for Methane Detection](https://mlsanity.com/paper/2304.02767/) (2023)
- [HyperspectralViTs: General Hyperspectral Models for On-board Remote Sensing](https://mlsanity.com/paper/2410.17248/) (2024)
- [Generalized Unsupervised Clustering of Hyperspectral Images of Geological Targets in the Near Infrared](https://mlsanity.com/paper/2106.13315/) (2021)
- [Local Background Estimation for Improved Gas Plume Identification in Hyperspectral Images](https://mlsanity.com/paper/2401.13068/) (2024)
- [Hyperspectral Image Analysis in Single-Modal and Multimodal setting using Deep Learning Techniques](https://mlsanity.com/paper/2403.01546/) (2024)
- [Remote sensing framework for geological mapping via stacked autoencoders and clustering](https://mlsanity.com/paper/2404.02180/) (2024)
- [Vision-Language Guided Hyperspectral Object Tracking via Semantics Fusion and Contextual Template Updating](https://mlsanity.com/paper/2606.09167/) (2026)

## Abstract (arXiv)

Hyperspectral sensors enable the study of the chemical properties of scene materials remotely for the purpose of identification, detection, and chemical composition analysis of objects in the environment. Hence, hyperspectral images captured from earth observing satellites and aircraft have been increasingly important in agriculture, environmental monitoring, urban planning, mining, and defense. Machine learning algorithms due to their outstanding predictive power have become a key tool for modern hyperspectral image analysis. Therefore, a solid understanding of machine learning techniques have become essential for remote sensing researchers and practitioners. This paper reviews and compares recent machine learning-based hyperspectral image analysis methods published in literature. We organize the methods by the image analysis task and by the type of machine learning algorithm, and present a two-way mapping between the image analysis tasks and the types of machine learning algorithms that can be applied to them. The paper is comprehensive in coverage of both hyperspectral image analysis tasks and machine learning algorithms. The image analysis tasks considered are land cover classification, target detection, unmixing, and physical parameter estimation. The machine learning algorithms covered are Gaussian models, linear regression, logistic regression, support vector machines, Gaussian mixture model, latent linear models, sparse linear models, Gaussian mixture models, ensemble learning, directed graphical models, undirected graphical models, clustering, Gaussian processes, Dirichlet processes, and deep learning. We also discuss the open challenges in the field of hyperspectral image analysis and explore possible future directions.