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

2022

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)

2021

B. Rasti and Koirala, B., “SUnCNN: Sparse Unmixing Using Unsupervised Convolutional Neural Network”, IEEE Geoscience and Remote Sensing Letters, vol. 19, pp. 1-5, 2021.PDF icon ieee_grsl_sundip.pdf (2.34 MB)
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.PDF icon remotesensing-13-02930.pdf (3.79 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, 2021.PDF icon manuscript.pdf (13 MB)
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)

2020

B. Koirala, “ Development of advanced hyperspectral unmixing methods ”, 2020.
V. Andrejchenko, “Hyperspectral image mixture analysis using notions of sparsity, nonlinearity and decision fusion ”, 2020.
K. Rafiezadeh Sahi, Ghamisi, P., Rasti, B., Jackisch, R., Scheunders, P., and Gloaguen, R., “Data Fusion Using a Multi-Sensor Sparse-Based Clustering Algorithm”, Remote Sensing, vol. 12 (23), no. 4007, 2020.PDF icon remotesensing-12-04007-v2.pdf (21.88 MB)
B. Rasti, Koirala, B., Scheunders, P., and Ghamisi, P., “How Hyperspectral Image Unmixing and Denoising Can Boost Each Other”, Remote Sensing, vol. 12, no. 1728, 2020.PDF icon remotesensing-12-01728.pdf (2.27 MB)

Pages