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Remote sensing

Multispectral and hyperspectral satellite and aerial image processing and analysis

Boosting Hyperspectral Image Unmixing using Denoising: Four Scenarios

B. Rasti, Koirala, B., Scheunders, P., Ghamisi, P., and Gloaguen, R., “Boosting Hyperspectral Image Unmixing using Denoising: Four Scenarios”, in IGARSS 2021, International Geoscience and Remote Sensing Symposium, Brussels, Belgium, 2021.

Spectral Unmixing Using Deep Convolutional Encoder-Decoder

B. Rasti, Koirala, B., Scheunders, P., and Ghamisi, P., “Spectral Unmixing Using Deep Convolutional Encoder-Decoder”, in IGARSS 2021, International Geoscience and Remote Sensing Symposium, Brussels, Belgium, 2021.

When is the right time to apply denoising?

K. Rafiezadeh Sahi, Rasti, B., Ghamisi, P., Scheunders, P., and Gloaguen, R., “When is the right time to apply denoising?”, in IGARSS 2021, International Geoscience and Remote Sensing Symposium, Brussels, Belgium, 2021.

Destriping Hyperspectral Imagery By Adaptive Anisotropic Total Variation And Truncated Nuclear Norm

T. Hu, Liu, N., Li, W., Tao, R., Zhang, F., and Scheunders, P., “Destriping Hyperspectral Imagery By Adaptive Anisotropic Total Variation And Truncated Nuclear Norm”, in Workshop on Hyperspectral Imaging and Signal Processing: Evolution in Remote Sensing (WHISPERS), 2021.

A Multi-Sensor Subspace-Based Clustering Algorithm Using RGB and Hyperspectral Data

K. Rafiezadeh Sahi, Ghamisi, P., Jackisch, R., Rasti, B., Scheunders, P., and Gloaguen, R., “A Multi-Sensor Subspace-Based Clustering Algorithm Using RGB and Hyperspectral Data”, in Workshop on Hyperspectral Imaging and Signal Processing: Evolution in Remote Sensing (WHISPERS), 2021.

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., Koirala, B., 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, no. 106806, 2022.

Files:

1-s2.0-s0168169922001235-main.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, no. 5522815, 2022.

Files:

misicnet_ieee_tgrs_author_version.pdf

Identification of corrosion minerals usning shortwave infrared hyperspectral imaging

T. De Kerf, Pipintakos, G., Zahiri, Z., Vanlanduit, S., and Scheunders, P., “Identification of corrosion minerals usning shortwave infrared hyperspectral imaging”, Sensors, vol. 22, no. 1, p. 407, 2022.

Files:

sensors-22-00407-v2.pdf

Unsupervised Data Fusion with Deeper Perspective: A Novel Multi-Sensor Deep Clustering Algorithm

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.

Files:

mdc_jstars-final_version.pdf

Hyperspectral and multispectral image fusion using coupled non-negative tucker tensor decomposition

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.

Files:

remotesensing-13-02930.pdf

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