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Remote sensing

Multispectral and hyperspectral satellite and aerial image processing and analysis

Quantitative detection of corrosion minerals in carbon steel using shortwave infrared hyperspectral imaging

T. De Kerf, Gestels, A., Janssens, K., Steenackers, G., Scheunders, P., and Vanlanduit, S., “Quantitative detection of corrosion minerals in carbon steel using shortwave infrared hyperspectral imaging”, RSC Advances, vol. 12, pp. 32775-32783, 2022.

Shadow-aware nonlinear spectral unmixing with spatial regularization

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.

Files:

shadow-aware_nonlinear_spectral_unmixing_with_spatial_regularization.pdf

Exploring the Potential of Hyperspectral Imaging to Estimate the Moisture Content in Natural and Historical Stones

D. Ali Chaghdo, Koirala, B., Cristina, L., Hayen, R., and Scheunders, P., “Exploring the Potential of Hyperspectral Imaging to Estimate the Moisture Content in Natural and Historical Stones”, in 2024 14th Workshop on Hyperspectral Imaging and Signal Processing: Evolution in Remote Sensing (WHISPERS), 2024, pp. 1-5.

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exploring_the_potential_of_hyperspectral_imaging_to_estimate_the_moisture_content_in_natural_and_historical_stones.pdf

Improving Spectral Unmixing Performance by Frequency Component Reduction

Z. Bnoulkacem, Koirala, B., and Scheunders, P., “Improving Spectral Unmixing Performance by Frequency Component Reduction”, in 2024 14th Workshop on Hyperspectral Imaging and Signal Processing: Evolution in Remote Sensing (WHISPERS), 2024, pp. 1-5.

Files:

improving_spectral_unmixing_performance_by_frequency_component_reduction.pdf

Validation of abundance determination of granular mixture using radiative transfer and Bayesian MCMC

F. Schmidt, Koirala, B., and Andrieu, F., “Validation of abundance determination of granular mixture using radiative transfer and Bayesian MCMC”, EPSC2024. 2024.

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exhibit_17_validation_of_abundance_determination_of_granular_mixture_using_radiative_transfer_and_bayesian_mcmc.pdf

Building forest and rice monitoring systems using artificial intelligence and multi-time series data analysis

A. - T. Nguyen, “Building forest and rice monitoring systems using artificial intelligence and multi-time series data analysis”, Viettel Group, Hanoi, Vietnam, 2019.

A New Dual-Feature Fusion Network for Enhanced Hyperspectral Unmixing

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.

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Determination of volumetric abundance of intimate mixture using Bayesian MCMC

F. Schmidt, Koirala, B., and Andrieu, F., “Determination of volumetric abundance of intimate mixture using Bayesian MCMC”, IEEE Sensors Journal, pp. 1-1, 2024.

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Hyperspectral data fusion for classification and visualization in remote sensing

Z. Mahmood, “Hyperspectral data fusion for classification and visualization in remote sensing”, 2013.

Advances in unmixing of hyperspectral remote sensing imagery

D. Burazerovic, “Advances in unmixing of hyperspectral remote sensing imagery”, 2014.

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