Clustering based background learning for hyperspectral anomaly detection

Clustering based background learning for hyperspectral anomaly detection

In real hyperspectral images, which have a complex background with multiple classes, separation of anomalous targets from background is a challenging problem. The Clustering based Background Learning (CBL) method is proposed for hyperspectral anomaly detection in this paper to deal with this difficu...

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Bibliographic Details
Main Authors: Mohamad Ebrahim Aghili, Maryam Imani, Hassan Ghassemian
Format: Article
Language:English
Published: Elsevier 2023-12-01
Series:Egyptian Journal of Remote Sensing and Space Sciences
Subjects:
Clustering
Background learning
Hyperspectral image
Multiplicative fusion
Anomaly detection
Online Access:http://www.sciencedirect.com/science/article/pii/S1110982323000406

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http://www.sciencedirect.com/science/article/pii/S1110982323000406

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