Terrain image classification with SVM

Mu Song Chen, Chipan Hwang, Tze Yee Ho

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Remote sensing is an important tool in a variety of scientific researches which can help to study and solve many practical environmental problems. Classification of remote sensing image, however, is usually complex in many respects that a lot of different ground objects show mixture distributions in space and change with temporal variations. Therefore, automatic classification of land covers is of practical significance to the exploration of desired information. Recently, support vector machine (SVM) has shown its capability in solving multi-class classification for different ground objects. In this paper, the extension of SVM to its online version is employed for terrain image classification. An illustration of online SVM learning and classification on San Francisco Bay area is also presented to demonstrate its applicability.

Original languageEnglish
Title of host publicationAdvances in Swarm Intelligence - 4th International Conference, ICSI 2013, Proceedings
Pages89-97
Number of pages9
EditionPART 2
DOIs
Publication statusPublished - 2013 Oct 7
Event4th International Conference on Advances in Swarm Intelligence, ICSI 2013 - Harbin, China
Duration: 2012 Jun 122012 Jun 15

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 2
Volume7929 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other4th International Conference on Advances in Swarm Intelligence, ICSI 2013
CountryChina
CityHarbin
Period12-06-1212-06-15

Fingerprint

Image classification
Image Classification
Support vector machines
Support Vector Machine
Remote sensing
Mixture Distribution
Multi-class Classification
Land Cover
Remote Sensing Image
Remote Sensing
Machine Learning
Learning systems
Demonstrate
Object

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

Cite this

Chen, M. S., Hwang, C., & Ho, T. Y. (2013). Terrain image classification with SVM. In Advances in Swarm Intelligence - 4th International Conference, ICSI 2013, Proceedings (PART 2 ed., pp. 89-97). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 7929 LNCS, No. PART 2). https://doi.org/10.1007/978-3-642-38715-9-11
Chen, Mu Song ; Hwang, Chipan ; Ho, Tze Yee. / Terrain image classification with SVM. Advances in Swarm Intelligence - 4th International Conference, ICSI 2013, Proceedings. PART 2. ed. 2013. pp. 89-97 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 2).
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Chen, MS, Hwang, C & Ho, TY 2013, Terrain image classification with SVM. in Advances in Swarm Intelligence - 4th International Conference, ICSI 2013, Proceedings. PART 2 edn, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), no. PART 2, vol. 7929 LNCS, pp. 89-97, 4th International Conference on Advances in Swarm Intelligence, ICSI 2013, Harbin, China, 12-06-12. https://doi.org/10.1007/978-3-642-38715-9-11

Terrain image classification with SVM. / Chen, Mu Song; Hwang, Chipan; Ho, Tze Yee.

Advances in Swarm Intelligence - 4th International Conference, ICSI 2013, Proceedings. PART 2. ed. 2013. p. 89-97 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 7929 LNCS, No. PART 2).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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Chen MS, Hwang C, Ho TY. Terrain image classification with SVM. In Advances in Swarm Intelligence - 4th International Conference, ICSI 2013, Proceedings. PART 2 ed. 2013. p. 89-97. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 2). https://doi.org/10.1007/978-3-642-38715-9-11