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Remote sensing

Multispectral and hyperspectral satellite and aerial image processing and analysis

HapkeCNN: Blind Nonlinear Unmixing for Intimate Mixtures Using Hapke Model and Convolutional Neural Network

B. Rasti, Koirala, B., and Scheunders, P., “HapkeCNN: Blind Nonlinear Unmixing for Intimate Mixtures Using Hapke Model and Convolutional Neural Network”, IEEE Transactions on Geoscience and Remote Sensing, vol. 60, pp. 1-15, 2022.

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Non-destructive analysis of plant physiological traits using hyperspectral imaging: A case study on drought stress

M. Shahrimie Asaari, Mertens, S., Verbraeken, L., Dhondt, S., Inze, D., Bikram, K., and Scheunders, P., “Non-destructive analysis of plant physiological traits using hyperspectral imaging: A case study on drought stress”, Computers and Electronics in Agriculture, vol. 195, p. 106806, 2022.

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non-destructive_analysis_of_plant_physiological_traits_using_hyperspectral.pdf

MiSiCNet: Minimum Simplex Convolutional Network for Deep Hyperspectral Unmixing

B. Rasti, Koirala, B., Scheunders, P., and Chanussot, J., “MiSiCNet: Minimum Simplex Convolutional Network for Deep Hyperspectral Unmixing”, IEEE Transactions on Geoscience and Remote Sensing, vol. 60, pp. 1-15, 2022.

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misicnet_ieee_tgrs_author_version.pdf

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.

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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.

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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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