# Hyperspectral Image Analysis with Subspace Learning-based One-Class Classification

arXiv: https://arxiv.org/abs/2304.09730 · HTML: https://mlsanity.com/paper/2304.09730/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 19 references that are in the map.
- First posted on arXiv 19 Apr 2023.

## Builds on (most cited first, 4 of 4)

- [Subspace Support Vector Data Description](https://mlsanity.com/paper/1802.03989/) (2018)
- [Ellipsoidal Subspace Support Vector Data Description](https://mlsanity.com/paper/2003.09504/) (2020)
- [Graph-Embedded Subspace Support Vector Data Description](https://mlsanity.com/paper/2104.14370/) (2021)
- [SRL-SOA: Self-Representation Learning with Sparse 1D-Operational Autoencoder for Hyperspectral Image Band Selection](https://mlsanity.com/paper/2202.09918/) (2022)

## Built on by (most cited first, 4 of 4)

- [Newton Method-based Subspace Support Vector Data Description](https://mlsanity.com/paper/2309.13960/) (2023)
- [Credit Card Fraud Detection with Subspace Learning-based One-Class Classification](https://mlsanity.com/paper/2309.14880/) (2023)
- [Anomaly Detection in Smart Power Grids with Graph-Regularized MS-SVDD: a Multimodal Subspace Learning Approach](https://mlsanity.com/paper/2502.15793/) (2025)
- [To Vaccinate or not to Vaccinate? Analyzing mathbbX Power over the Pandemic](https://mlsanity.com/paper/2503.02563/) (2025)

## Abstract (arXiv)

Hyperspectral image (HSI) classification is an important task in many applications, such as environmental monitoring, medical imaging, and land use/land cover (LULC) classification. Due to the significant amount of spectral information from recent HSI sensors, analyzing the acquired images is challenging using traditional Machine Learning (ML) methods. As the number of frequency bands increases, the required number of training samples increases exponentially to achieve a reasonable classification accuracy, also known as the curse of dimensionality. Therefore, separate band selection or dimensionality reduction techniques are often applied before performing any classification task over HSI data. In this study, we investigate recently proposed subspace learning methods for one-class classification (OCC). These methods map high-dimensional data to a lower-dimensional feature space that is optimized for one-class classification. In this way, there is no separate dimensionality reduction or feature selection procedure needed in the proposed classification framework. Moreover, one-class classifiers have the ability to learn a data description from the category of a single class only. Considering the imbalanced labels of the LULC classification problem and rich spectral information (high number of dimensions), the proposed classification approach is well-suited for HSI data. Overall, this is a pioneer study focusing on subspace learning-based one-class classification for HSI data. We analyze the performance of the proposed subspace learning one-class classifiers in the proposed pipeline. Our experiments validate that the proposed approach helps tackle the curse of dimensionality along with the imbalanced nature of HSI data.