Lorenzo Bruzzone
University of Trento
774 Papers
3K Citations
Lorenzo Bruzzone is an academic researcher from University of Trento. The author has contributed to research in topics: Computer science & Change detection. The author has an hindex of 86, co-authored 699 publications. Previous affiliations of Lorenzo Bruzzone include Technical University of Berlin & Purdue University.
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Papers
Classification of hyperspectral remote sensing images with support vector machines
Farid Melgani,Lorenzo Bruzzone +1 more
TL;DR: This paper addresses the problem of the classification of hyperspectral remote sensing images by support vector machines by understanding and assessing the potentialities of SVM classifiers in hyperdimensional feature spaces and concludes that SVMs are a valid and effective alternative to conventional pattern recognition approaches.
Recent Advances in Techniques for Hyperspectral Image Processing
Antonio Plaza,Jon Atli Benediktsson,Joseph W. Boardman,J. Brazile,Lorenzo Bruzzone,Gustavo Camps-Valls,Jocelyn Chanussot,Mathieu Fauvel,Mathieu Fauvel,Paolo Gamba,Anthony J. Gualtieri,Mattia Marconcini,James C. Tilton,G. Trianni +13 more
TL;DR: A seminal view on recent advances in techniques for hyperspectral image processing, focusing on the design of techniques able to deal with the high-dimensional nature of the data, and to integrate the spa- tial and spectral information.
1.7K
Kernel-based methods for hyperspectral image classification
TL;DR: This paper assesses performance of regularized radial basis function neural networks (Reg-RBFNN), standard support vector machines (SVMs), kernel Fisher discriminant (KFD) analysis, and regularized AdaBoost (reg-AB) in the context of hyperspectral image classification.
1.6K
Automatic analysis of the difference image for unsupervised change detection
Lorenzo Bruzzone,D.F. Prieto +1 more
TL;DR: The authors propose two automatic techniques (based on the Bayes theory) for the analysis of the difference image that allow an automatic selection of the decision threshold that minimizes the overall change detection error probability under the assumption that pixels in the difference picture are independent of one another.
ADJUST: An automatic EEG artifact detector based on the joint use of spatial and temporal features.
TL;DR: A completely automatic algorithm (ADJUST) that identifies artifacted independent components by combining stereotyped artifact-specific spatial and temporal features is proposed that provides a fast, efficient, and automatic way to use ICA for artifact removal.