Remote Sensing

Remote Sensing is the research area in which the 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. Recently, research is shifted to more close-range applications, for which we have our own acquisition equipement (spectrometer 400-2500 nm; VNIR camera 400-900 nm and SWIR camera 1000-1700 nm). Applications are material characterization (corrosion, soil moisture, powder material composition).

Journal publications

2017

B. Haest, Vanden Borre, J., Spanhove, T., Thoonen, G., Delalieux, S., Kooistra, L., Mücher, C. A., Paelinckx, D., Scheunders, P., and Kempeneers, P., “Habitat mapping and quality assessment of NATURA 2000 Heatland using airborne imaging spectroscopy”, Remote Sensing, vol. 9, no. 3, 2017.
M. Brackx, Van Wittenberghe, S., Verhelst, J., Scheunders, P., and Samson, R., “Hyperspectral leaf reflectance of Carpines betulus L. saplings for urban air quality estimation”, Environmental Pollution, vol. 220, pp. 159-167, 2017.
R. Heylen, Parente, M., and Scheunders, P., “Estimation of the number of endmembers in a hyperspectral image via the hubness phenomenon”, IEEE Transactions on Geoscience and Remote Sensing, vol. 55, no. 4, pp. 2191-2200, 2017.

2016

W. Guo, Hung, C. - C., Scheunders, P., Kuo, B. - C., Lan, Y., and Ma, Z., “Advanced techniques for computational and information sciences”, Mathematical Problems in Engineering, vol. 2016, 2016.
R. Heylen, Zare, A., Gader, P., and Scheunders, P., “Hyperspectral unmixing with endmember variability via alternating angle minimization”, IEEE Transactions on Geoscience and Remote Sensing, vol. 54, no. 8, pp. 4983-4993, 2016.
Z. H. Nezhad, Karami, A., Heylen, R., and Scheunders, P., “Fusion of Hyperspectral and Multispectral Images Using Spectral Unmixing and Sparse Coding”, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 9, no. 6, pp. 2377-2389, 2016.
A. Karami, Heylen, R., and Scheunders, P., “Hyperspectral Image Compression Optimized for Spectral Unmixing”, IEEE Transactions on Geoscience and Remote Sensing, vol. pp, no. 99, 2016.
G. Thoonen, Nys, B., Haegen, V. Y., Roy, D. G., and Scheunders, P., “Automatic forensic analysis of automotive paints using optical microscopy”, Forensic Science International, vol. 259, pp. 210-220, 2016.
R. Heylen and Scheunders, P., “A multilinear mixing model for nonlinear spectral unmixing”, IEEE Transactions on Geoscience and Remote Sensing, vol. 54, no. 1, pp. 240-251, 2016.

2015

A. Karami, Heylen, R., and Scheunders, P., “Band-specific Shearlet-based Hyperspectral Image Noise Reduction”, IEEE Transaction Geosciences and Remote Sensing , vol. 53, no. 9, 2015.

Pages