Improving specific class mapping from remotely sensed data by Cost-Sensitive learning

Bibliographic Details
Main Author: Silva, Joel
Publication Date: 2017
Other Authors: Bacao, Fernando, Dieng, Maguette, Foody, Giles M., Caetano, Mario
Format: Article
Language: eng
Source: Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
Download full: https://doi.org/10.1080/01431161.2017.1292073
Summary: Silva, J., Bacao, F., Dieng, M., Foody, G. M., & Caetano, M. (2017). Improving specific class mapping from remotely sensed data by Cost-Sensitive learning. International Journal Of Remote Sensing, 38(11), 3294-3316. https://doi.org/10.1080/01431161.2017.1292073
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spelling Improving specific class mapping from remotely sensed data by Cost-Sensitive learningEarth and Planetary Sciences(all)SDG 15 - Life on LandSilva, J., Bacao, F., Dieng, M., Foody, G. M., & Caetano, M. (2017). Improving specific class mapping from remotely sensed data by Cost-Sensitive learning. International Journal Of Remote Sensing, 38(11), 3294-3316. https://doi.org/10.1080/01431161.2017.1292073In many remote-sensing projects, one is usually interested in a small number of land-cover classes present in a study area and not in all the land-cover classes that make-up the landscape. Previous studies in supervised classification of satellite images have tackled specific class mapping problem by isolating the classes of interest and combining all other classes into one large class, usually called others, and by developing a binary classifier to discriminate the class of interest from the others. Here, this approach is called focused approach. The strength of the focused approach is to decompose the original multi-class supervised classification problem into a binary classification problem, focusing the process on the discrimination of the class of interest. Previous studies have shown that this method is able to discriminate more accurately the classes of interest when compared with the standard multi-class supervised approach. However, it may be susceptible to data imbalance problems present in the training data set, since the classes of interest are often a small part of the training set. A result the classification may be biased towards the largest classes and, thus, be sub-optimal for the discrimination of the classes of interest. This study presents a way to minimize the effects of data imbalance problems in specific class mapping using cost-sensitive learning. In this approach errors committed in theminority class are treated as being costlier than errors committed in the majority class. Cost-sensitive approaches are typically implemented by weighting training data points accordingly to their importance to the analysis. By changing the weight of individual data points, it is possible to shift theweight from the larger classes to the smaller ones, balancing the data set. To illustrate the use of the cost-sensitive approach to map specific classes of interest, a series of experiments with weighted support vector machines classifier and Landsat Thematic Mapper data were conducted to discriminate two types of mangrove forest (high-mangrove and low-mangrove) in Saloum estuary, Senegal, a United Nations Educational, Scientific and Cultural Organisation World Heritage site. Results suggest an increase in overall classification accuracy with the use of cost-sensitive method (97.3%) over the standard multi-class (94.3%) and the focused approach (91.0%). In particular, cost-sensitive method yielded higher sensitivity and specificity values on the discrimination of the classes of interest when compared with the standard multi-class and focused approaches.NOVA Information Management School (NOVA IMS)Information Management Research Center (MagIC) - NOVA Information Management SchoolRUNSilva, JoelBacao, FernandoDieng, MaguetteFoody, Giles M.Caetano, Mario2019-09-27T23:04:25Z2017-06-032017-06-03T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/article23application/pdfhttps://doi.org/10.1080/01431161.2017.1292073eng0143-1161PURE: 13193510http://www.scopus.com/inward/record.url?scp=85028874290&partnerID=8YFLogxKhttps://doi.org/10.1080/01431161.2017.1292073info:eu-repo/semantics/openAccessreponame:Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)instname:FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologiainstacron:RCAAP2024-05-22T17:41:15Zoai:run.unl.pt:10362/82530Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireinfo@rcaap.ptopendoar:https://opendoar.ac.uk/repository/71602025-05-28T17:12:29.372800Repositórios Científicos de Acesso Aberto de Portugal (RCAAP) - FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologiafalse
dc.title.none.fl_str_mv Improving specific class mapping from remotely sensed data by Cost-Sensitive learning
title Improving specific class mapping from remotely sensed data by Cost-Sensitive learning
spellingShingle Improving specific class mapping from remotely sensed data by Cost-Sensitive learning
Silva, Joel
Earth and Planetary Sciences(all)
SDG 15 - Life on Land
title_short Improving specific class mapping from remotely sensed data by Cost-Sensitive learning
title_full Improving specific class mapping from remotely sensed data by Cost-Sensitive learning
title_fullStr Improving specific class mapping from remotely sensed data by Cost-Sensitive learning
title_full_unstemmed Improving specific class mapping from remotely sensed data by Cost-Sensitive learning
title_sort Improving specific class mapping from remotely sensed data by Cost-Sensitive learning
author Silva, Joel
author_facet Silva, Joel
Bacao, Fernando
Dieng, Maguette
Foody, Giles M.
Caetano, Mario
author_role author
author2 Bacao, Fernando
Dieng, Maguette
Foody, Giles M.
Caetano, Mario
author2_role author
author
author
author
dc.contributor.none.fl_str_mv NOVA Information Management School (NOVA IMS)
Information Management Research Center (MagIC) - NOVA Information Management School
RUN
dc.contributor.author.fl_str_mv Silva, Joel
Bacao, Fernando
Dieng, Maguette
Foody, Giles M.
Caetano, Mario
dc.subject.por.fl_str_mv Earth and Planetary Sciences(all)
SDG 15 - Life on Land
topic Earth and Planetary Sciences(all)
SDG 15 - Life on Land
description Silva, J., Bacao, F., Dieng, M., Foody, G. M., & Caetano, M. (2017). Improving specific class mapping from remotely sensed data by Cost-Sensitive learning. International Journal Of Remote Sensing, 38(11), 3294-3316. https://doi.org/10.1080/01431161.2017.1292073
publishDate 2017
dc.date.none.fl_str_mv 2017-06-03
2017-06-03T00:00:00Z
2019-09-27T23:04:25Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
format article
status_str publishedVersion
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url https://doi.org/10.1080/01431161.2017.1292073
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 0143-1161
PURE: 13193510
http://www.scopus.com/inward/record.url?scp=85028874290&partnerID=8YFLogxK
https://doi.org/10.1080/01431161.2017.1292073
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv 23
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dc.source.none.fl_str_mv reponame:Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
instname:FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologia
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collection Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
repository.name.fl_str_mv Repositórios Científicos de Acesso Aberto de Portugal (RCAAP) - FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologia
repository.mail.fl_str_mv info@rcaap.pt
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