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(Reference retrieved automatically from Web of Science through information on FAPESP grant and its corresponding number as mentioned in the publication by the authors.)

Inducing Contextual Classifications With Kernel Functions Into Support Vector Machines

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Author(s):
Negri, Rogerio Galante [1] ; da Silva, Erivaldo Antonio [2] ; Casaca, Wallace [3]
Total Authors: 3
Affiliation:
[1] UNESP, Inst Ciencia & Tecnol, BR-12247004 Sao Jose Dos Campos - Brazil
[2] UNESP, Fac Ciencia & Tecnol, BR-19060900 Presidente Prudente - Brazil
[3] Univ Estadual Paulista, Campus Expt Rosana, BR-19274000 Sao Paulo - Brazil
Total Affiliations: 3
Document type: Journal article
Source: IEEE Geoscience and Remote Sensing Letters; v. 15, n. 6, p. 962-966, JUN 2018.
Web of Science Citations: 2
Abstract

Kernel functions have revolutionized theory and practice in the field of pattern recognition, especially to perform image classification. Besides giving rise to nonlinear variants of the well-known support vector machine (SVM), these functions have also been successfully used to classify nonvectorial data (e.g., graphs and collection of sets), in which customized metrics are created to precisely measure the similarity among such contextual data entities. This letter introduces two cantext-inspired kernel functions as new SVM-driven methods for remote sensing image classification. In contrast to the existing SVM-based approaches that assume only multiattribute vectors as representative features in a high-dimensional space, the proposed models formally establish comparisons between the entire sets of context-given data, thus employing these contextual measurements to drive the classification. More precisely, stochastic distances as well as hypothesis tests are conveniently handled and ``kernelized{''} to build our models. A complete battery of experiments involving both remote sensing and real-world images is conducted to validate the performance of the proposed kernels against various well-established SVM-based methods. (AU)

FAPESP's process: 13/07375-0 - CeMEAI - Center for Mathematical Sciences Applied to Industry
Grantee:Francisco Louzada Neto
Support Opportunities: Research Grants - Research, Innovation and Dissemination Centers - RIDC
FAPESP's process: 17/03595-6 - Development of methodology for the detection of slope streaks on Mars surface
Grantee:Erivaldo Antonio da Silva
Support Opportunities: Regular Research Grants
FAPESP's process: 14/08822-2 - Methodology development for the extraction of cartographic features from digital images of the surfaces of the planets Earth, Mars and Mercury
Grantee:Erivaldo Antonio da Silva
Support Opportunities: Regular Research Grants
FAPESP's process: 14/14830-8 - Study and development of new Kernel functions with applications on remote sensing image classification
Grantee:Rogério Galante Negri
Support Opportunities: Regular Research Grants