Share Email Print

Proceedings Paper

A novel transductive SVM for semisupervised classification of remote sensing images
Author(s): Mingmin Chi; Lorenzo Bruzzone
Format Member Price Non-Member Price
PDF $17.00 $21.00

Paper Abstract

This paper introduces a semisupervised classification method, which exploits both labeled and unlabeled samples, for addressing "ill-posed" problems with support vector machines (SVMs). The method is based on recent developments in statistical learning theory concerning transductive inference and in particular Transductive SVMs (TSVMs). We propose a novel modified TSVM classifier designed for the analysis of "ill-posed" remotesensing problems. In particular, the proposed technique: i) is based on a novel transductive procedure that exploits a weighting strategy for the unlabeled patterns based on a time-dependent criterion; ii) is developed also for multiclass cases; and iii) addresses the model-selection problem with lack of test/validation sets. Experimental results confirm the effectiveness of the proposed method on a set of "ill-posed" remote-sensing classification problems representing different operative conditions.

Paper Details

Date Published: 18 October 2005
PDF: 12 pages
Proc. SPIE 5982, Image and Signal Processing for Remote Sensing XI, 59820G (18 October 2005); doi: 10.1117/12.628862
Show Author Affiliations
Mingmin Chi, Univ. of Trento (Italy)
Lorenzo Bruzzone, Univ. of Trento (Italy)

Published in SPIE Proceedings Vol. 5982:
Image and Signal Processing for Remote Sensing XI
Lorenzo Bruzzone, Editor(s)

© SPIE. Terms of Use
Back to Top
Sign in to read the full article
Create a free SPIE account to get access to
premium articles and original research
Forgot your username?