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

2014

R. Heylen and Scheunders, P., “A distance geometric framework for non-linear hyperspectral unmixing”, IEEE-JSTARS, Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 7, pp. 1879-1888, 2014.

2013

Z. Mahmood, “Hyperspectral data fusion for classification and visualization in remote sensing”, 2013.
J. Bioucas-Dias, Plaza, A., Camps-Valls, G., Scheunders, P., Nasrabadi, N., and Chanussot, J., “Hyperspectral remote sensing data analysis and future challenges”, IEEE Geoscience and Remote Sensing Magazine, vol. 1, no. 2, pp. 6-36, 2013.
D. Burazerovic, Geens, B., Heylen, R., Sterckx, S., and Scheunders, P., “Detecting the adjacency effect in hyperspectral imagery with spectral unmixing techniques”, IEEE JSTARS, Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 6, no. 3, pp. 1070-1078, 2013.
R. Heylen and Scheunders, P., “Hyperspectral intrinsic dimensionality estimation with nearest-neighbor distance ratio's”, IEEE JSTARS, Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 6, no. 2, pp. 570-579, 2013.
Z. Mahmood, Akhter, M. A., Thoonen, G., and Scheunders, P., “Contextual Subpixel Mapping of Hyperspectral Images Making Use of a High Resolution Color Image”, IEEE JSTARS, Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 6, no. 2, pp. 779 - 791, 2013.
R. Heylen and Scheunders, P., “Multi-dimensional pixel purity index for convex hull estimation and endmember extraction”, IEEE Transactions on Geoscience and Remote Sensing, vol. 51, no. 7, pp. 4059-4069, 2013.
W. Liao, Pizurica, A., Scheunders, P., Philips, W., and Pi, Y., “Semi-supervised local discriminant analysis for feature extraction in hyperspectral images”, IEEE Transactions on Geoscience and Remote Sensing, vol. 51, no. 1, pp. 184-198, 2013.
G. Thoonen, Spanhove, T., Vanden Borre, J., and Scheunders, P., “Classification of heathland vegetation in a hierarchical contextual framework”, International Journal of Remote Sensing, vol. 34, no. 1, pp. 96 - 111, 2013.

2012

G. Thoonen, “Contextual classification of hyperspectral remote sensing images - Application in vegetation monitoring”, University of Antwerp, Antwerp, Belgium, 2012.PDF icon Download thesis (17.15 MB)

Pages