Journal ArticleDOI
Composite kernels for hyperspectral image classification
Gustau Camps-Valls,Luis Gómez-Chova,Jordi Muñoz-Marí,Joan Vila-Francés,Javier Calpe-Maravilla +4 more
TLDR
This framework of composite kernels demonstrates enhanced classification accuracy as compared to traditional approaches that take into account the spectral information only, flexibility to balance between the spatial and spectral information in the classifier, and computational efficiency.Abstract:
This letter presents a framework of composite kernel machines for enhanced classification of hyperspectral images. This novel method exploits the properties of Mercer's kernels to construct a family of composite kernels that easily combine spatial and spectral information. This framework of composite kernels demonstrates: 1) enhanced classification accuracy as compared to traditional approaches that take into account the spectral information only: 2) flexibility to balance between the spatial and spectral information in the classifier; and 3) computational efficiency. In addition, the proposed family of kernel classifiers opens a wide field for future developments in which spatial and spectral information can be easily integrated.read more
Citations
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Journal ArticleDOI
Support vector machines in remote sensing: A review
TL;DR: This paper reviews remote sensing implementations of support vector machines (SVMs), a promising machine learning methodology that is particularly appealing in the remote sensing field due to their ability to generalize well even with limited training samples.
Journal ArticleDOI
Hyperspectral Remote Sensing Data Analysis and Future Challenges
Jose M. Bioucas-Dias,Antonio Plaza,Gustau Camps-Valls,Paul Scheunders,Nasser M. Nasrabadi,Jocelyn Chanussot +5 more
TL;DR: A tutorial/overview cross section of some relevant hyperspectral data analysis methods and algorithms, organized in six main topics: data fusion, unmixing, classification, target detection, physical parameter retrieval, and fast computing.
Journal ArticleDOI
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.
Journal ArticleDOI
Advances in Spectral-Spatial Classification of Hyperspectral Images
TL;DR: Recent advances in spectral-spatial classification of hyperspectral images are presented in this paper and several techniques are investigated for combining both spatial and spectral information.
Journal ArticleDOI
Hyperspectral Image Classification Using Dictionary-Based Sparse Representation
TL;DR: Experimental results show that the proposed sparsity-based algorithm for the classification of hyperspectral imagery outperforms the classical supervised classifier support vector machines in most cases.
References
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BookDOI
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
TL;DR: Learning with Kernels provides an introduction to SVMs and related kernel methods that provide all of the concepts necessary to enable a reader equipped with some basic mathematical knowledge to enter the world of machine learning using theoretically well-founded yet easy-to-use kernel algorithms.
Book
An Introduction to Support Vector Machines
TL;DR: This book is the first comprehensive introduction to Support Vector Machines, a new generation learning system based on recent advances in statistical learning theory, and introduces Bayesian analysis of learning and relates SVMs to Gaussian Processes and other kernel based learning methods.
Journal ArticleDOI
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.
Journal Article
Learning with kernels : Support vector machines, regularization, optimization, and beyond
Journal ArticleDOI
On the mean accuracy of statistical pattern recognizers
TL;DR: The overall mean recognition probability (mean accuracy) of a pattern classifier is calculated and numerically plotted as a function of the pattern measurement complexity n and design data set size m, using the well-known probabilistic model of a two-class, discrete-measurement pattern environment.
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