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

Sertac Kilickaya, Mete Ahishali, Fahad Sohrab, Turker Ince, Moncef Gabbouj

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review


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.
Original languageEnglish
Title of host publication2023 PhotonIcs & Electromagnetics Research Symposium (PIERS)
Subtitle of host publicationProceedings
ISBN (Electronic)979-8-3503-1284-3
Publication statusPublished - 28 Aug 2023
Publication typeA4 Article in conference proceedings
EventPhotonIcs & Electromagnetics Research Symposium (PIERS) - Prague, Czech Republic
Duration: 3 Jul 20236 Jul 2023

Publication series

NameProgress in Electromagnetics Research Symposium
ISSN (Electronic)1559-9450


ConferencePhotonIcs & Electromagnetics Research Symposium (PIERS)
Country/TerritoryCzech Republic

Publication forum classification

  • Publication forum level 1


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