An Efficient Gabor Feature-Based Multi-task Joint Support Vector Machines Framework for Hyperspectral Image Classification View Full Text


Ontology type: schema:Chapter     


Chapter Info

DATE

2016

AUTHORS

Sen Jia , Bin Deng

ABSTRACT

In this paper, a novel multi-task learning (MTL) framework for a series of Gabor features via joint probabilistic outputs of support vector machines (SVM), abbreviated as GF-MTJSVM, has been proposed for Hyperspectral image (HSI) classification. Specifically, we firstly use a series of Gabor wavelet filters with different scales and frequencies to extract spectral-spatial-combined features from the HSI data. Then, we apply these Gabor features into the multi-task learning framework via joint probabilistic outputs of SVM. Experimental results on two widely used real HSI data indicate that the proposed GF-MTJSVM approach outperforms several well-known classification methods. More... »

PAGES

14-25

References to SciGraph publications

Book

TITLE

Pattern Recognition

ISBN

978-981-10-3004-8
978-981-10-3005-5

Author Affiliations

From Grant

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/978-981-10-3005-5_2

DOI

http://dx.doi.org/10.1007/978-981-10-3005-5_2

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1040346722


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