English
 
User Manual Privacy Policy Disclaimer Contact us
  Advanced SearchBrowse

Item

ITEM ACTIONSEXPORT
  Semi-supervised Remote Sensing Image Classification via Maximum Entropy

Erkan, A., Camps-Valls, G., & Altun, Y. (2010). Semi-supervised Remote Sensing Image Classification via Maximum Entropy. In 2010 IEEE International Workshop on Machine Learning for Signal Processing (MLSP 2010) (pp. 313-318). Piscataway, NJ, USA: IEEE.

Item is

Basic

show hide
Item Permalink: http://hdl.handle.net/11858/00-001M-0000-0013-BE70-5 Version Permalink: http://hdl.handle.net/21.11116/0000-0002-80D0-1
Genre: Conference Paper

Files

show Files

Locators

show
hide
Description:
-

Creators

show
hide
 Creators:
Erkan, AN, Author              
Camps-Valls, G, Author              
Altun, Y1, 2, Author              
Affiliations:
1Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497795              
2Max Planck Institute for Biological Cybernetics, Max Planck Society, Spemannstrasse 38, 72076 Tübingen, DE, ou_1497794              

Content

show
hide
Free keywords: -
 Abstract: Remote sensing image segmentation requires multi-category classification typically with limited number of labeled training samples. While semi-supervised learning (SSL) has emerged as a sub-field of machine learning to tackle the scarcity of labeled samples, most SSL algorithms to date have had trade-offs in terms of scalability and/or applicability to multi-categorical data. In this paper, we evaluate semi-supervised logistic regression (SLR), a recent information theoretic semi-supervised algorithm, for remote sensing image classification problems. SLR is a probabilistic discriminative classifier and a specific instance of the generalized maximum entropy framework with a convex loss function. Moreover, the method is inherently multi-class and easy to implement. These characteristics make SLR a strong alternative to the widely used semi-supervised variants of SVM for the segmentation of remote sensing images. We demonstrate the competitiveness of SLR in multispectral, hyperspectral and radar image classifica tion.

Details

show
hide
Language(s):
 Dates: 2010-09
 Publication Status: Published in print
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: -
 Identifiers: DOI: 10.1109/MLSP.2010.5589199
BibTex Citekey: 6619
 Degree: -

Event

show
hide
Title: 2010 IEEE International Workshop on Machine Learning for Signal Processing (MLSP 2010)
Place of Event: Kittilä, Finland
Start-/End Date: 2010-08-29 - 2010-09-01

Legal Case

show

Project information

show

Source 1

show
hide
Title: 2010 IEEE International Workshop on Machine Learning for Signal Processing (MLSP 2010)
Source Genre: Proceedings
 Creator(s):
Affiliations:
Publ. Info: Piscataway, NJ, USA : IEEE
Pages: - Volume / Issue: - Sequence Number: - Start / End Page: 313 - 318 Identifier: ISBN: 978-1-4244-7877-4