Remote Sensing

Remote Sensing is the research area in which earth's surface is studied, usually using the reflectance spectrum of the sun. Vision Lab has built expertise in the processing and analysis of multispectral and hyperspectral remote sensing images. Topics of research include the development of techniques for image denoising, restoration, fusion, segmentation, classification and spectral unmixing. Main application domains are vegetation monitoring for which we collaborate with the Teleprocessing group of VITO (Flemish Institute for Technological Research).

Journal publications

2016

A. Karami, R. Heylen, and P. Scheunders, "Hyperspectral Image Compression Optimized for Spectral Unmixing", IEEE Transactions on Geoscience and Remote Sensing, vol. pp, issue 99, 06/2016.
R. Heylen, and P. Scheunders, "A multilinear mixing model for nonlinear spectral unmixing", IEEE Transactions on Geoscience and Remote Sensing, vol. 54, issue 1, pp. 240-251, 2016.

2015

A. Karami, R. Heylen, and P. Scheunders, "Band-specific Shearlet-based Hyperspectral Image Noise Reduction", IEEE Transaction Geosciences and Remote Sensing , vol. 53, issue 9, no. 13, 03/2015.
R. Heylen, P. Scheunders, P. Gader, and A. Rangarajan, "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.
M A. Akhter, R. Heylen, and P. Scheunders, "A geometric matched filter for hyperspectral target detection and partial unmixing", IEEE Geoscience and Remote Sensing letters, vol. 12, no. 3, pp. 661-665, 2015.
L. Tits, R. Heylen, B. Somers, P. Scheunders, and P. Coppin, "A geometric unmixing concept for the selection of optimal binary endmember combinations", IEEE Geoscience and Remote Sensing letters, vol. 12, no. 1, pp. 82-86, 2015.

2014

D. Burazerovic, R. Heylen, D. Raymaekers, E. Knaeps, and P. Scheunders, "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, no. 8, 2014.
R. Heylen, and P. Scheunders, "A distance geometric framework for non-linear hyperspectral unmixing", IEEE-JSTARS, Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 7, no. 6, pp. 1879-1888, 2014.

2013

J. Bioucas-Dias, A. Plaza, G. Camps-Valls, P. Scheunders, N. Nasrabadi, and J. Chanussot, "Hyperspectral remote sensing data analysis and future challenges", IEEE Geoscience and Remote Sensing Magazine, vol. 1, issue 2, pp. 6-36, 2013.

Pages