Log in

Remote sensing

Multispectral and hyperspectral satellite and aerial image processing and analysis

Automated Social behaviour Recognition At Low Resolution

N. Tanmay, Liu, G., Hassan, B., Weyn, B., De Backer, S., and Scheunders, P., “Automated Social behaviour Recognition At Low Resolution”, in ICPR14, International Conference on Pattern Recognition, Stockholm, Sweden, 2014.

Hyperspectral Image Noise Reduction and its Effect on Spectral Unmixing

A. Karami, Heylen, R., and Scheunders, P., “Hyperspectral Image Noise Reduction and its Effect on Spectral Unmixing”, in IEEE-Whispers 2014, Workshop on Hyperspectral Image and Signal Processing, Lausanne, Suisse, 2014.

Geometric matched filter for hyperspectral partial unmixing

M. A. Akhter, Heylen, R., and Scheunders, P., “Geometric matched filter for hyperspectral partial unmixing”, in IEEE-Whispers 2014, Workshop on Hyperspectral Image and Signal Processing, Lausanne, Suisse, 2014.

Nonlinear unmixing by using non-Euclidean metrics in a linear unmixing chain

R. Heylen, Scheunders, P., Rangarajan, A., and Gader, P., “Nonlinear unmixing by using non-Euclidean metrics in a linear unmixing chain”, in IEEE-Whispers 2014, Workshop on Hyperspectral Image and Signal Processing, Lausanne, Suisse, 2014.

Nonlinear unmixing by using different metrics in a linear unmixing chain

R. Heylen, Scheunders, P., Gader, P., and Rangarajan, A., “Nonlinear unmixing by using different metrics in a linear unmixing chain”, IEEE-JSTARS, Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2015.

A geometric matched filter for hyperspectral target detection and partial unmixing

M. A. Akhter, Heylen, R., and Scheunders, P., “A geometric matched filter for hyperspectral target detection and partial unmixing”, IEEE Geoscience and Remote Sensing letters, vol. 12, pp. 661-665, 2015.

A spectral-unmixing approach to estimate water-mass concentrations in case-II waters

D. Burazerovic, Heylen, R., Raymaekers, D., Knaeps, E., and Scheunders, P., “A spectral-unmixing approach to estimate water-mass concentrations in case-II waters”, IEEE-JSTARS, Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 7, 2014.

A geometric unmixing concept for the selection of optimal binary endmember combinations

L. Tits, Heylen, R., Somers, B., Scheunders, P., and Coppin, P., “A geometric unmixing concept for the selection of optimal binary endmember combinations”, IEEE Geoscience and Remote Sensing letters, vol. 12, pp. 82-86, 2015.

A distance geometric framework for non-linear hyperspectral unmixing

R. Heylen and Scheunders, P., “A distance geometric framework for non-linear hyperspectral unmixing”, IEEE-JSTARS, Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 7, pp. 1879-1888, 2014.

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.

Pages