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Article: Detecting the nature of change in an urban environment: A comparison of machine learning algorithms

TitleDetecting the nature of change in an urban environment: A comparison of machine learning algorithms
Authors
Issue Date2001
PublisherAmerican Society for Photogrammetry and Remote Sensing. The Journal's web site is located at http://www.asprs.org/publications/pers
Citation
Photogrammetric Engineering And Remote Sensing, 2001, v. 67 n. 2, p. 213-225 How to Cite?
AbstractThe performance of difference machine learning algorithms for detecting nature of change was compared. To alleviate the problem of obtaining enough training data, simulated training data were generated from single-date images. A one-pass classification with four machine learning algorithms, namely, Multi-Layer Perceptrons (MLP), Learning Vector Quantization (LVQ), Decision Tree Classifiers (DTC), and the Maximum- Likelihood Classifier (MLC), were tested. Recognition rates, ease of use, and degree of automation of the four algorithms were assessed. The results showed that the incorporation of cross-combined simulated training data enhanced the detection of nature of change. Compared to conventional post-classification comparison methods, LVQ and DTC did better in terms of overall accuracy. In terms of average accuracy of the change classes, LVQ was the best performer. DTC was the easiest to use and the most robust in training. MLP procedures were the most difficult to replicate.
Persistent Identifierhttp://hdl.handle.net/10722/118210
ISSN
2015 Impact Factor: 1.288
2015 SCImago Journal Rankings: 0.830
References

 

DC FieldValueLanguage
dc.contributor.authorChan, JCWen_HK
dc.contributor.authorChan, KPen_HK
dc.contributor.authorYeh, AGOen_HK
dc.date.accessioned2010-09-26T07:54:23Z-
dc.date.available2010-09-26T07:54:23Z-
dc.date.issued2001en_HK
dc.identifier.citationPhotogrammetric Engineering And Remote Sensing, 2001, v. 67 n. 2, p. 213-225en_HK
dc.identifier.issn0099-1112en_HK
dc.identifier.urihttp://hdl.handle.net/10722/118210-
dc.description.abstractThe performance of difference machine learning algorithms for detecting nature of change was compared. To alleviate the problem of obtaining enough training data, simulated training data were generated from single-date images. A one-pass classification with four machine learning algorithms, namely, Multi-Layer Perceptrons (MLP), Learning Vector Quantization (LVQ), Decision Tree Classifiers (DTC), and the Maximum- Likelihood Classifier (MLC), were tested. Recognition rates, ease of use, and degree of automation of the four algorithms were assessed. The results showed that the incorporation of cross-combined simulated training data enhanced the detection of nature of change. Compared to conventional post-classification comparison methods, LVQ and DTC did better in terms of overall accuracy. In terms of average accuracy of the change classes, LVQ was the best performer. DTC was the easiest to use and the most robust in training. MLP procedures were the most difficult to replicate.en_HK
dc.languageengen_HK
dc.publisherAmerican Society for Photogrammetry and Remote Sensing. The Journal's web site is located at http://www.asprs.org/publications/persen_HK
dc.relation.ispartofPhotogrammetric Engineering and Remote Sensingen_HK
dc.titleDetecting the nature of change in an urban environment: A comparison of machine learning algorithmsen_HK
dc.typeArticleen_HK
dc.identifier.openurlhttp://library.hku.hk:4550/resserv?sid=HKU:IR&issn=0099-1112&volume=&spage=213&epage=225&date=2001&atitle=Detecting+the+Nature+of+Change+in+an+Urban+Environment:+A+Comparison+of+Machine+Learning+Algorithmsen_HK
dc.identifier.emailYeh, AGO: hdxugoy@hkucc.hku.hken_HK
dc.identifier.authorityYeh, AGO=rp01033en_HK
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.scopuseid_2-s2.0-0035134388en_HK
dc.identifier.hkuros58483en_HK
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-0035134388&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.volume67en_HK
dc.identifier.issue2en_HK
dc.identifier.spage213en_HK
dc.identifier.epage225en_HK
dc.publisher.placeUnited Statesen_HK
dc.identifier.scopusauthoridChan, JCW=8840429000en_HK
dc.identifier.scopusauthoridChan, KP=7406032278en_HK
dc.identifier.scopusauthoridYeh, AGO=7103069369en_HK

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