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  Recent trends in classification of remote sensing data: active and semisupervised machine learning paradigms

Bruzzone, L., & Persello, C. (2010). Recent trends in classification of remote sensing data: active and semisupervised machine learning paradigms. In IEEE International Geoscience and Remote Sensing Symposium (IGARSS 2010) (pp. 3720-3723). Piscataway, NJ, USA: IEEE.

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Item Permalink: https://hdl.handle.net/11858/00-001M-0000-0013-BF46-E Version Permalink: https://hdl.handle.net/21.11116/0000-0002-81C8-A
Genre: Conference Paper

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https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5651236 (Publisher version)
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 Creators:
Bruzzone, L, Author
Persello, C1, Author           
Affiliations:
1University of Trento, ou_persistent22              

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 Abstract: This paper addresses the recent trends in machine learning methods for the automatic classification of remote sensing (RS) images. In particular, we focus on two new paradigms: semisupervised and active learning. These two paradigms allow one to address classification problems in the critical conditions where the available labeled training samples are limited. These operational conditions are very usual in RS problems, due to the high cost and time associated with the collection of labeled samples. Semisupervised and active learning techniques allow one to enrich the initial training set information and to improve classification accuracy by exploiting unlabeled samples or requiring additional labeling phases from the user, respectively. The two aforementioned strategies are theoretically and experimentally analyzed considering SVM-based techniques in order to highlight advantages and disadvantages of both strategies.

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 Dates: 2010-07
 Publication Status: Issued
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: -
 Identifiers: DOI: 10.1109/IGARSS.2010.5651236
BibTex Citekey: BruzzoneP2010_2
 Degree: -

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Title: IEEE International Geoscience and Remote Sensing Symposium (IGARSS 2010)
Place of Event: Honolulu, HI, USA
Start-/End Date: 2010-07-25 - 2010-07-30

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Title: IEEE International Geoscience and Remote Sensing Symposium (IGARSS 2010)
Source Genre: Proceedings
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Publ. Info: Piscataway, NJ, USA : IEEE
Pages: - Volume / Issue: - Sequence Number: - Start / End Page: 3720 - 3723 Identifier: ISBN: 978-1-4244-9565-8