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

2009

Y. Zhang, S. De Backer, and P. Scheunders, "Noise-resistant wavelet-based Bayesian fusion of multispectral and hyperspectral images", IEEE Transactions on Geoscience and Remote Sensing, vol. 47, no. 11, pp. 3834-3843, 2009.
A. Duijster, P. Scheunders, and S. De Backer, "Wavelet-Based EM Algorithm for Multispectral-Image Restoration", IEEE Transactions on Geoscience and Remote Sensing, vol. 47, no. 11, pp. 3892-3898, November, 2009.

2008

S. De Backer, A. Pizurica, B. Huysmans, W. Philips, and P. Scheunders, "Denoising of Multicomponent Images Using Wavelet Least-Squares Estimators", Image and Vision Computing, vol. 26, no. 7, pp. 1038-1051, July, 2008.

2007

P. Scheunders, and S. De Backer, "Wavelet denoising of multicomponent images, using Gaussian Scale Mixture models and a noise-free image as priors", IEEE Transactions on Image Processing, vol. 16, no. 7, pp. 1865-1872, July, 2007.

2005

S. De Backer, P. Kempeneers, W. Debruyn, and P. Scheunders, "A Band Selection Technique for Spectral Classification", IEEE Geoscience and Remote Sensing Letters, vol. 2, no. 3, pp. 319-323, July, 2005.
P. Kempeneers, S. De Backer, W. Debruyn, and P. Scheunders, "Generic Wavelet-Based Hyperspectral Classification Applied to Vegetation Stress Detection", IEEE Transactions on Geoscience and Remote Sensing, vol. 43, pp. 610-614, March, 2005.

Pages