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

2020

B. Koirala, Zahiri, Z., and Scheunders, P., “A Machine Learning Framework for Estimating Leaf Biochemical Parameters From Its Spectral Reflectance and Transmission Measurements”, IEEE Transactions on Geoscience and Remote Sensing, vol. 58, no. 10, pp. 7393-7405, 2020.PDF icon final_version_leaf_parameter_estimation.pdf (2.66 MB)

2019

S. Mohd. Asaari, “Analysis of hyperspectral images for high-throughput plant phenotyping ”, 2019.
M. Shahrimie Asaari, Mertens, S., Dhondt, S., Inze, D., Wuyts, N., and Scheunders, P., “Analysis of hyperspectral images for detection of drought stress and recovery in maize plants in a high-throughput phenotyping platform”, Computers and Electronics in Agriculture, vol. 162, pp. 749-758, 2019.PDF icon shahrimie_2019.pdf (3.12 MB)
B. Koirala, Khodadadzadeh, M., Contreras, C., Zahiri, Z., Gloaguen, R., and Scheunders, P., “A supervised method for nonlinear hyperspectral unmixing”, Remote Sensing, vol. 11, no. 20 , 2019.PDF icon remotesensing-11-02458-v3.pdf (3.23 MB)
V. Andrejchenko, Liao, W., Philips, W., and Scheunders, P., “Decision Fusion Framework for Hyperspectral Image Classification Based on Markov and Conditional Random Fields”, Remote Sensing, vol. 11, 2019.PDF icon remotesensing-11-00624.pdf (1.5 MB)
R. Heylen, Andrejchenko, V., Zahiri, Z., Parente, M., and Scheunders, P., “Nonlinear hyperspectral unmixing with graphical models”, IEEE Transaction on Geoscience and Remote Sensing, vol. 57, no. 7, pp. 4844-4856, 2019.PDF icon published.pdf (3.15 MB)

2018

G. Liu, Nath, T., Guo, Z., Linneweber, G., Claeys, A., Li, J., Bengochea, M., De Backer, S., Weyn, B., Sneyders, M., Nicasy, H., Yu, P., Scheunders, P., and Hassan, B., “A simple computer vision pipeline reveals the effects of isolation on social interaction dynamics in Drosophila”, Plos Computational Biology, vol. 14, no. 8, p. e1006410, 2018.
B. Rasti, Scheunders, P., Ghesami, P., Licciardi, G., and Chanussot, J., “Noise reduction in hyperspectral imagery: overview and application”, Remote Sensing , vol. 10, no. 3, p. 482, 2018.
M. Shahrimie Asaari, Mishra, P., Mertens, S., Dhondt, S., Inze, D., Wuyts, N., and Scheunders, P., “Close-range hyperspectral image analysis for the early detection of plant stress responses in individual plants in a high-throughput phenotyping platform”, ISPRS Journal of Photogrammetry and Remote Sensing , vol. 138, pp. 121-138, 2018.

2017

A. J. Rebelo, Scheunders, P., Esler, K. J., and Meire, P., “Evaluating palmiet wetland decline: a comparison of three methods”, Remote Sensing Applications: Society and Environment, vol. 8, pp. 212-223, 2017.

Pages