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Remote sensing

Multispectral and hyperspectral satellite and aerial image processing and analysis

Improving the efficiency of MESMA through geometric unmixing principles

L. Tits, Somers, B., Heylen, R., Scheunders, P., and Coppin, P., “Improving the efficiency of MESMA through geometric unmixing principles”, in SPIE Remote Sensing, Drseden, Germany, 23-26 September , 2013, vol. 8892.

Handling spectral variability with alternating angle minimization

R. Heylen, Scheunders, P., and Gader, P., “Handling spectral variability with alternating angle minimization”, in IEEE-WHISPERS 2013, Workshop on Hyperspectral Image and Signal Processing, Gainesville, Florida, June 25-28, 2013.

Multi-dimensional Pixel Purity Index

R. Heylen and Scheunders, P., “Multi-dimensional Pixel Purity Index”, in IEEE-WHISPERS 2013, Workshop on Hyperspectral Image and Signal Processing, Gainesville, Florida, June 25-28, 2013.

Spectral adaptation of hyperspectral flight lines using VHR contextual information

J. - P. Jacobs, Thoonen, G., Tuia, D., Camps-Valls, G., Kempeneers, P., and Scheunders, P., “Spectral adaptation of hyperspectral flight lines using VHR contextual information”, in Geoscience and Remote Sensing Symposium (IGARSS), 2014 IEEE International, Quebec City, QC, Canada, 2014, pp. 2953-2956.

Best Rank-r Tensor Selection Using Genetic Algorithm for Better Noise Reduction and Compression of Hyperspectral Images

A. Karami, M.Yazdi,, and Zolghadre, A., “Best Rank-r Tensor Selection Using Genetic Algorithm for Better Noise Reduction and Compression of Hyperspectral Images”, International Conference on Digital Information Management . 2010.

Hyperspectral Image Compression Based on Tucker Decomposition and Discrete Cosine Transform

A. Karami, M.Yazdi,, and Zolghadre, A., “Hyperspectral Image Compression Based on Tucker Decomposition and Discrete Cosine Transform”, International Conference on Image Processing Theory, Tools and Applications. 2010.

Hyperspectral Image Compression based on Tucker Decomposition and Wavelet Transform

A. Karami, M.Yazdi,, and Mercier, G., “Hyperspectral Image Compression based on Tucker Decomposition and Wavelet Transform”, WHISPERS . Lisbon, Portugal, 2011.

Hyperspectral Image Compression Using 3d Discrete Cosine Transform and Support Vector Machine Learning

A. Karami, Beheshti, S., and M.Yazdi, “Hyperspectral Image Compression Using 3d Discrete Cosine Transform and Support Vector Machine Learning”, International Conference on Information Science, Signal Processing and their Applications . Montreal, Canada, 2012.

Coordination of UPFC & SVC for Voltage Security Enhancement

A. Karami, M.Rashidinejad,, and Gharaveisi, A. A., “Coordination of UPFC & SVC for Voltage Security Enhancement”, WSEAS Transactions on Circuits and Systems, vol. 5, no. 1, 2006.

Compression and Noise Reduction of Hyperspectral Images Using Hybrid Genetic Algorithm and Nonnegative Tucker Decomposition

A. Karami, M.Yazdi,, and Zolghadre, A., “Compression and Noise Reduction of Hyperspectral Images Using Hybrid Genetic Algorithm and Nonnegative Tucker Decomposition”, International Journal of Information studies , vol. 2, no. 4, 2010.

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