Detection of nonlinear mixtures using Gaussian processes: Application to hyperspectral imaging
作者： T. Imbiriba , J. C. M. Bermudez , J.-Y. Tourneret , C. Richard
DOI: 10.1109/ICASSP.2014.6855148
关键词:
摘要: This paper investigates the use of Gaussian processes to detect non-linearly mixed pixels in hyperspectral images. The proposed technique is independent nonlinear mixing mechanism, and therefore not restricted any prescribed model. observed reflectances are estimated using both least squares method a process. fitting errors two approaches combined test statistics for which it possible estimate detection threshold given required probability false alarm. detector compared robust nonlinearity recently synthetic data shown provide better performance. new also tested on real image.
archives-ouvertes.fr
cedric-richard.fr 参考文章(24)
Bernhard Schölkopf, Alexander J. Smola, Learning with Kernels The MIT Press. pp. 626- ,(2018) , 10.7551/MITPRESS/4175.001.0001
Christopher K I Williams, Carl Edward Rasmussen, Gaussian Processes for Machine Learning ,(2005)
Simon Haykin, Jose C. Principe, Weifeng Liu, Kernel Adaptive Filtering: A Comprehensive Introduction ,(2010)
Y. Altmann, N. Dobigeon, J-Y. Tourneret, J.C.M. Bermudez, A robust test for nonlinear mixture detection in hyperspectral images 2013 IEEE International Conference on Acoustics, Speech and Signal Processing. pp. 2149- 2153 ,(2013) , 10.1109/ICASSP.2013.6638034
José M. P. Nascimento, José M. Bioucas-Dias, Nonlinear mixture model for hyperspectral unmixing Proceedings SPIE 7477, Image and Signal Processing for Remote Sensing XV. ,vol. 7477, ,(2009) , 10.1117/12.830492
Cédric Richard, Paul Honeine, Jie Chen, Nonlinear Unmixing of Hyperspectral Data Based on a Linear-Mixture/Nonlinear-Fluctuation Model IEEE Transactions on Signal Processing. ,vol. 61, pp. 480- 492 ,(2013) , 10.1109/TSP.2012.2222390
Yoann Altmann, Nicolas Dobigeon, Jean-Yves Tourneret, Nonlinearity Detection in Hyperspectral Images Using a Polynomial Post-Nonlinear Mixing Model IEEE Transactions on Image Processing. ,vol. 22, pp. 1267- 1276 ,(2013) , 10.1109/TIP.2012.2210235
Jie Chen, Cedric Richard, Paul Honeine, Nonlinear Estimation of Material Abundances in Hyperspectral Images With $\ell_{1}$ -Norm Spatial Regularization IEEE Transactions on Geoscience and Remote Sensing. ,vol. 52, pp. 2654- 2665 ,(2014) , 10.1109/TGRS.2013.2264392
D. Landgrebe, Hyperspectral image data analysis IEEE Signal Processing Magazine. ,vol. 19, pp. 17- 28 ,(2002) , 10.1109/79.974718
Jose M. Bioucas-Dias, Antonio Plaza, Gustavo Camps-Valls, Paul Scheunders, Nasser Nasrabadi, Jocelyn Chanussot, Hyperspectral Remote Sensing Data Analysis and Future Challenges IEEE Geoscience and Remote Sensing Magazine. ,vol. 1, pp. 6- 36 ,(2013) , 10.1109/MGRS.2013.2244672