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).
People
Journal publications
2021
“UnDIP: hyperspectral unmixing using deep image prior”, IEEE Transactions on Geoscience and Remote Sensing, vol. 60, pp. 1-15, 2021. manuscript.pdf (13 MB) ,
“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. ieee_version.pdf (3.76 MB) ,
“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. tgrs_2020.pdf (5.71 MB) ,
2020
“Hyperspectral image mixture analysis using notions of sparsity, nonlinearity and decision fusion ”, 2020. ,
“Data Fusion Using a Multi-Sensor Sparse-Based Clustering Algorithm”, Remote Sensing, vol. 12 (23), no. 4007, 2020. remotesensing-12-04007-v2.pdf (21.88 MB) ,
“How Hyperspectral Image Unmixing and Denoising Can Boost Each Other”, Remote Sensing, vol. 12, no. 1728, 2020. remotesensing-12-01728.pdf (2.27 MB) ,
“A Machine Learning Framework for Estimating Leaf Biochemical Parameters From Its Spectral Reflectance and Transmission Measurements”, IEEE Transactions on Geoscience and Remote Sensing, vol. 58, no. 10, pp. 7393-7405, 2020. final_version_leaf_parameter_estimation.pdf (2.66 MB) ,
2019
“Analysis of hyperspectral images for detection of drought stress and recovery in maize plants in a high-throughput phenotyping platform”, Computers and Electronics in Agriculture, vol. 162, pp. 749-758, 2019. shahrimie_2019.pdf (3.12 MB) ,