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

R. Luo, Liao, W., Zhang, H., Zhang, L., Pi, Y., Scheunders, P., and Philips, W., Fusion of Hyperspectral and LiDAR Data for Classification of Cloud-Shadow Mixed Remote Sensing Scene, IEEE-JSTARS, Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 10, no. 8, pp. 3768-3781, 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

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.

Pages