Target detection in hyperspectral images using basic thresholding classifier

Date

2017-05-18

Author

TOKSÖZ, Mehmet Altan
Ulusoy, İlkay

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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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In this letter, we propose target detector version of recently introduced basic thresholding classifier for hyperspectral images. The proposed technique is a sparsity-based low complexity detector which achieves high detection rates with very low false alarm rates and performs extremely rapidly. We also propose a new decision metric, background model, and spatial smoothing procedure in order to increase the detection probability further. Experiments show that the presented method outperforms the other state-of-the-art sparsity-based approaches.

Subject Keywords

Target detection, Hyperspectral images, Basic thresholding classifier, Background model, Decision metric

URI

https://hdl.handle.net/11511/37944

DOI

https://doi.org/10.1109/siu.2017.7960169

Collections

Department of Electrical and Electronics Engineering, Conference / Seminar

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Citation Formats

M. A. TOKSÖZ and İ. Ulusoy, “Target detection in hyperspectral images using basic thresholding classifier,” 2017, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/37944.