Investigation of unsupervised and supervised hyperspectral anomaly detection

Mazharul Hossain; Aaron Robinson; Lan Wang; Chrysanthe Preza

doi:10.1117/12.3029916

3 October 2024 Investigation of unsupervised and supervised hyperspectral anomaly detection

Mazharul Hossain, Aaron Robinson, Lan Wang, Chrysanthe Preza

Proceedings Volume 13138, Applications of Machine Learning 2024; 1313817 (2024) https://doi.org/10.1117/12.3029916
Event: Optical Engineering + Applications, 2024, San Diego, California, United States

Conference Poster

Abstract

Hyperspectral sensing is a valuable tool for detecting anomalies and distinguishing between materials in a scene. Hyperspectral anomaly detection (HS-AD) helps characterize the captured scenes and separates them into anomaly and background classes. It is vital in agriculture, environment, and military applications such as RSTA (reconnaissance, surveillance, and target acquisition) missions. We previously designed an equal voting ensemble of hyperspectral unmixing and three unsupervised HS-AD algorithms. We later utilized a supervised classifier to determine the weights of a voting ensemble, creating a hybrid of heterogeneous unsupervised HS-AD algorithms with a supervised classifier in a model stacking, which improved detection accuracy. However, supervised classification methods usually fail to detect novel or unknown patterns that substantially deviate from those seen previously. In this work, we evaluate our technique and other supervised and unsupervised methods using general hyperspectral data to provide new insights.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Mazharul Hossain, Aaron Robinson, Lan Wang, and Chrysanthe Preza "Investigation of unsupervised and supervised hyperspectral anomaly detection", Proc. SPIE 13138, Applications of Machine Learning 2024, 1313817 (3 October 2024); https://doi.org/10.1117/12.3029916

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