Terrain image classification with SVM

Mu Song Chen, Chipan Hwang, Tze Yee Ho

研究成果: Conference contribution

摘要

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.

原文English
主出版物標題Advances in Swarm Intelligence - 4th International Conference, ICSI 2013, Proceedings
頁面89-97
頁數9
版本PART 2
DOIs
出版狀態Published - 2013 十月 7
事件4th International Conference on Advances in Swarm Intelligence, ICSI 2013 - Harbin, China
持續時間: 2012 六月 122012 六月 15

出版系列

名字Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
號碼PART 2
7929 LNCS
ISSN(列印)0302-9743
ISSN(電子)1611-3349

Other

Other4th International Conference on Advances in Swarm Intelligence, ICSI 2013
國家China
城市Harbin
期間12-06-1212-06-15

    指紋

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

引用此

Chen, M. S., Hwang, C., & Ho, T. Y. (2013). Terrain image classification with SVM. 於 Advances in Swarm Intelligence - 4th International Conference, ICSI 2013, Proceedings (PART 2 編輯, 頁 89-97). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); 卷 7929 LNCS, 編號 PART 2). https://doi.org/10.1007/978-3-642-38715-9-11