HYPERSPECTRAL UNMIXING BASED VEGETATION DETECTION WITH SEGMENTATION

Date

2016-07-15

Author

Özdemir, Okan Bilge
Soydan, Hilal
Çetin, Yasemin
Duzgun, Sebnem

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This paper presents a vegetation detection application with semi-supervised target detection using hyperspectral unmixing and segmentation algorithms. The method firstly compares the known target spectral signature from a generic source such as a spectral library with each pixel of hyperspectral data cube employing Spectral Angle Mapper (SAM) algorithm. The pixel(s) with the best match are assumed to be the most likely target vegetation locations. The regions around these potential target locations are further analyzed via hyperspectral unmixing techniques to obtain the real spectra in the image. The abundance fractions are evaluated so as to compare the algorithm performance with those of other methods. As a post processing technique meanshift segmentation algorithm utilized.

Subject Keywords

Hyperspectral, Target detection, Vegetation detection, Segmentation, Unmixing

URI

https://hdl.handle.net/11511/57931

DOI

https://doi.org/10.1109/igarss.2016.7730825

Conference Name

36th IEEE International Geoscience and Remote Sensing Symposium (IGARSS)

Collections

Graduate School of Informatics, Conference / Seminar

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Citation Formats

O. B. Özdemir, H. Soydan, Y. Çetin, and S. Duzgun, “HYPERSPECTRAL UNMIXING BASED VEGETATION DETECTION WITH SEGMENTATION,” presented at the 36th IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Beijing, PEOPLES R CHINA, 2016, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/57931.