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

2011

R. Heylen, Burazerovic, D., and Scheunders, P., Fully constrained least-squares spectral unmixing by simplex projection, IEEE Transactions on Geoscience and Remote Sensing, vol. 49, no. 11, pp. 4112-4122, 2011.PDF icon PDF (1.11 MB)Package icon Matlab code (1.93 KB)
Z. Mahmood and Scheunders, P., Enhanced visualization of hyperspectral images, IEEE Geoscience and Remote Sensing letters, vol. 8, pp. 869-873, 2011.
R. Heylen, Burazerovic, D., and Scheunders, P., Nonlinear spectral unmixing by geodesic simplex volume maximization, IEEE Journal of Selected Topics in Signal Processing, vol. 5, pp. 534-542, 2011.

2010

2009

Y. Zhang, De Backer, S., and Scheunders, P., Noise-resistant wavelet-based Bayesian fusion of multispectral and hyperspectral images, IEEE Transactions on Geoscience and Remote Sensing, vol. 47, pp. 3834-3843, 2009.
A. Duijster, Scheunders, P., and De Backer, S., Wavelet-Based EM Algorithm for Multispectral-Image Restoration, IEEE Transactions on Geoscience and Remote Sensing, vol. 47, pp. 3892-3898, 2009.

2008

Pages