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

2021

M. Zare, M. Helfroush, S., Kazemi, K., and Scheunders, P., “Hyperspectral and multispectral image fusion using coupled non-negative tucker tensor decomposition”, Remote Sensing, vol. 13, no. 2930, 2021.PDF icon remotesensing-13-02930.pdf (3.79 MB)
B. Koirala, Zahiri, Z., Lamberti, A., and Scheunders, P., “Robust supervised method for nonlinear spectral unmixing accounting for endmember variability”, IEEE Transactions on Geoscience and Remote Sensing, vol. 59, no. 9, pp. 7434-7448, 2021.PDF icon ieee_version.pdf (3.76 MB)
T. Hu, Li, W., Liu, N., Tao, R., Zhang, F., and Scheunders, P., “Hyperspectral Image Restoration Using Adaptive Anisotropy Total Variation and Nuclear Norms”, IEEE Transactions on Geoscience and Remote Sensing, vol. 59, no. 2, pp. 1516-1533, 2021.PDF icon tgrs_2020.pdf (5.71 MB)

2020

K. Rafiezadeh Sahi, Ghamisi, P., Rasti, B., Jackisch, R., Scheunders, P., and Gloaguen, R., “Data Fusion Using a Multi-Sensor Sparse-Based Clustering Algorithm”, Remote Sensing, vol. 12 (23), no. 4007, 2020.PDF icon remotesensing-12-04007-v2.pdf (21.88 MB)
B. Rasti, Koirala, B., Scheunders, P., and Ghamisi, P., “How Hyperspectral Image Unmixing and Denoising Can Boost Each Other”, Remote Sensing, vol. 12, no. 1728, 2020.PDF icon remotesensing-12-01728.pdf (2.27 MB)
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

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)

Pages