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


K. Rafiezadeh Sahi, Ghamisi, P., Rasti, B., Scheunders, P., and Gloaguen, R., Unsupervised Data Fusion with Deeper Perspective: A Novel Multi-Sensor Deep Clustering Algorithm, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing , vol. 15, pp. 284-296, 2022.PDF icon mdc_jstars-final_version.pdf (6.53 MB)
B. Rasti, Koirala, B., Scheunders, P., and Ghamisi, P., UnDIP: hyperspectral unmixing using deep image prior, IEEE Transactions on Geoscience and Remote Sensing, vol. 60, pp. 1-15, 2022.PDF icon manuscript.pdf (13 MB)


SUnCNN: Sparse unmixing using unsupervised convolutional neural network, IEEE Geoscience and Remote Sensing Letters, 2021.
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)