Improved Fukunaga–Koontz Transform with Compositional Kernel Combination for Hyperspectral Target Detection-Reference-Cited by-同舟云学术

Improved Fukunaga–Koontz Transform with Compositional Kernel Combination for Hyperspectral Target Detection

Published:2018-07-20 Issue:10 Volume:46 Page:1605-1615
ISSN:0255-660X
Container-title:Journal of the Indian Society of Remote Sensing
language:en
Short-container-title:J Indian Soc Remote Sens

Author:

Binol Hamidullah^ORCID

Publisher

Springer Science and Business Media LLC

Subject

Earth and Planetary Sciences (miscellaneous),Geography, Planning and Development

Link

http://link.springer.com/article/10.1007/s12524-018-0814-y/fulltext.html

Reference34 articles.

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3. Bach, F. R., Lanckriet, G. R. G., & Jordan, M. I. (2004). Multiple kernel learning, conic duality, and the SMO algorithm. In NIPS.

4. Bal, A., & Alam, M. S. (2005). Quadratic correlation filter-based target tracking in FLIR image sequences. Proceedings of SPIE Applications of Digital Image Processing XXVIII, 5909, 590906.

5. Binol, H., & Bal, A. (2016). Supervised target detection in hyperspectral images using one-class Fukunaga–Koontz transform. In Signal Processing, Sensor/Information Fusion, and Target Recognition XXV (vol. 9842). International Society for Optics and Photonics.

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