Signature Based Vegetation Detection on Hyperspectral Images

Date

2015-05-19

Author

Özdemir, Okan Bilge
Soydan, Hilal
Çetin, Yasemin
Düzgün, Hafize Şebnem

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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 study, the contribution of utilizing hyperspectral unmixing algorithms on signature based target detection algorithms is studied. Spectral Angle Mapper (SAM), Spectral Matched Filter (SMF) and Adaptive Cosine Estimator (ACE) algorithms are selected as target detection methods and the performance change related to the target spectral acquisition is evaluated. The spectral signature of the desired target, corn, is acquired from ASD hyperspectral library as well as from the hypespectral unmixing endmembers with a minimum angular distance to ASD signature. It is seen that the performance of the corn detection has increased significantly with the utilization of the closest endmember extracted from the hyperspectral data cube. Among all methods, SAM has been designated as the most successful method based on the Receiver Operating Characteristics (ROC) curves.

Subject Keywords

Spectral Angle Mapper, Matched Filter, Adaptive Cosine, Target Detection, Hyperspectral

URI

https://hdl.handle.net/11511/53570

Collections

Graduate School of Informatics, Conference / Seminar

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Citation Formats

O. B. Özdemir, H. Soydan, Y. Çetin, and H. Ş. Düzgün, “Signature Based Vegetation Detection on Hyperspectral Images,” 2015, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/53570.