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Target detection in hyperspectral images using basic thresholding classifier
Date
2017-05-18
Author
TOKSÖZ, Mehmet Altan
Ulusoy, İlkay
Metadata
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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