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

2024

X. Tao, Koirala, B., Plaza, A., and Scheunders, P., “A New Dual-Feature Fusion Network for Enhanced Hyperspectral Unmixing”, IEEE Transactions on Geoscience and Remote Sensing, pp. 1-1, 2024.PDF icon Download paper (2.13 MB)
F. Schmidt, Koirala, B., and Andrieu, F., “Determination of volumetric abundance of intimate mixture using Bayesian MCMC”, IEEE Sensors Journal, pp. 1-1, 2024.PDF icon Download paper (2.8 MB)
B. Koirala and Scheunders, P., “An efficient method for water content estimation of building materials from spectral reflectance”, NDT & E International, vol. 147, p. 103214, 2024.PDF icon Download paper (7.21 MB)
B. Koirala, Rasti, B., Bnoulkacem, Z., and Scheunders, P., “Nonlinear Spectral Unmixing Using Bézier Surfaces”, IEEE Transactions on Geoscience and Remote Sensing, vol. 62, pp. 1-16, 2024.PDF icon Download paper (9.66 MB)
B. Koirala, Rasti, B., and Scheunders, P., “A Supervised Approach for Estimating Fractional Abundances of Binary Intimate Mixtures”, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, pp. 1-11, 2024.PDF icon Download paper (4.49 MB)

2023

G. Zhang, Scheunders, P., and Cerra, D., “Shadow-aware nonlinear spectral unmixing with spatial regularization”, IEEE Transactions on Geoscience and Remote Sensing, vol. 61, p. 5517516, 2023.PDF icon shadow-aware_nonlinear_spectral_unmixing_with_spatial_regularization.pdf (17.62 MB)
T. De Kerf, “Hyperspectral imaging for automated inspection of offshore wind infrastructure”, 2023.
K. Rafiezadeh Shahi, “Unsupervised machine/deep learning mapping (clustering) for single/multi-source remote sensing data ”, 2023.
B. Koirala, Rasti, B., Bnoulkacem, Z., Ribeiro, A. De Lima, Madriz, Y., Herrmann, E., Gestels, A., De Kerf, T., Lorenz, S., Fuchs, M., Janssens, K., Steenackers, G., Gloaguen, R., and Scheunders, P., “A Multisensor Hyperspectral Benchmark Dataset For Unmixing of Intimate Mixtures”, IEEE Sensors Journal, 2023.PDF icon a_multisensor_hyperspectral_benchmark_dataset_for_unmixing_of_intimate_mixtures_1_.pdf (22.13 MB)
B. Koirala, Mboga, N., Moelans, R., Knaeps, E., Sels, S., Winters, F., Samsonova, S., Vanlanduit, S., and Scheunders, P., “Study on the Potential of Oil Spill Monitoring in a Port Environment Using Optical Reflectance”, Remote Sensing, 2023.PDF icon remotesensing-15-04950_1.pdf (20.86 MB)

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