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
2011
“Fully constrained least-squares spectral unmixing by simplex projection”, IEEE Transactions on Geoscience and Remote Sensing, vol. 49, no. 11, pp. 4112-4122, 2011. PDF (1.11 MB) Matlab code (1.93 KB) ,
“Enhanced visualization of hyperspectral images”, IEEE Geoscience and Remote Sensing letters, vol. 8, pp. 869-873, 2011. ,
“Nonlinear spectral unmixing by geodesic simplex volume maximization”, IEEE Journal of Selected Topics in Signal Processing, vol. 5, pp. 534-542, 2011. ,
2010
“Habitat reporting of a heathland site: Classification probabilities as additional information, a case study”, Ecological Informatics, vol. 5, pp. 248 - 255, 2010. ,
2009
“Validation of the sigmoid wave curve fitting algorithm on a forest-tundra ecotone in the Northwest Territories, Canada”, Ecological Informatics, vol. 4, pp. 1-7, 2009. ,
“Wavelet-Based EM Algorithm for Multispectral-Image Restoration”, IEEE Transactions on Geoscience and Remote Sensing, vol. 47, pp. 3892-3898, 2009. ,
“Ecotones in vegetation ecology: methodology and definitions revisited”, Ecological Research, vol. 24, pp. 977-986, 2009. ,
“Noise-resistant wavelet-based Bayesian fusion of multispectral and hyperspectral images”, IEEE Transactions on Geoscience and Remote Sensing, vol. 47, pp. 3834-3843, 2009. ,
2008
“Denoising of Multicomponent Images Using Wavelet Least-Squares Estimators”, Image and Vision Computing, vol. 26, pp. 1038-1051, 2008. ,
“Estimating the ecotone width in patchy ecotones using a sigmoid wave approach”, Ecological Informatics, vol. 3, pp. 97-104, 2008. ,