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

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)
Y. Zhang, Duijster, A., and Scheunders, P., “A Bayesian Restoration Approach for Hyperspectral Images”, IEEE Transactions on Geoscience and Remote Sensing, vol. 50, no. 9, pp. 3453-3462, 2012.
R. Heylen and Scheunders, P., “Calculation of geodesic distances in non-linear mixing models: demonstration on the generalized bilinear model”, IEEE Geoscience and Remote Sensing letters, vol. 9, no. 4, pp. 644-648, 2012.
G. Thoonen, Hufkens, K., Vanden Borre, J., Spanhove, T., and Scheunders, P., “Accuracy assessment of contextual classification results for vegetation mapping”, International Journal of Applied Earth Observation and Geoinformation, vol. 15, pp. 7 - 15, 2012.
G. Thoonen, Mahmood, Z., Peeters, S., and Scheunders, P., “Multisource classification of color and hyperspectral images using color attribute profiles and composite decision fusion”, Selected Topics in Applied Earth Observations and Remote Sensing, IEEE Journal of, vol. 5, pp. 510 - 521, 2012.

Pages