Publication:
Improving Classification Accuracy of Machine Learning Techniques applied to Remotely Sensed Data

dc.contributor.author Milne, Linda en_US
dc.date.accessioned 2021-11-25T12:46:43Z
dc.date.available 2021-11-25T12:46:43Z
dc.date.issued 1998 en_US
dc.description.abstract Producing vegetation maps is one of a myriad of uses that remotely sensed data is being used for. Low error rate classifers can be obtained from the training data generated from surveyed sites and expert knowledge. However, when these classifers are applied to an entire remotely sensed image to produce a map they contain at least many generalisations and at worst gross errors. This is, in part, due to the limited nature of spectral information and limited amounts of training data. In this paper we investigate a technique, called reinforcement classifcation, to generate more accurate classifcations of remotely sensed images. We demonstrate reinforcement classifcation using C4.5 although it is general enough to be applied to any domain and classifcation scheme. en_US
dc.identifier.isbn 3-540-65138-1 en_US
dc.identifier.uri http://hdl.handle.net/1959.4/37655
dc.language English
dc.language.iso EN en_US
dc.publisher Springer en_US
dc.rights CC BY-NC-ND 3.0 en_US
dc.rights.uri https://creativecommons.org/licenses/by-nc-nd/3.0/au/ en_US
dc.source Legacy MARC en_US
dc.subject.other ensemble classification en_US
dc.subject.other C4.5 en_US
dc.subject.other remotely sensed data en_US
dc.subject.other small datasets en_US
dc.subject.other Image Processing (280203) en_US
dc.subject.other Neural Networks, Genetic Algorithms and Fuzzy Logic (280212) en_US
dc.subject.other Other Artificial Intelligence (280213) en_US
dc.title Improving Classification Accuracy of Machine Learning Techniques applied to Remotely Sensed Data en_US
dc.type Conference Paper en
dcterms.accessRights metadata only access
dspace.entity.type Publication en_US
unsw.accessRights.uri http://purl.org/coar/access_right/c_14cb
unsw.description.publisherStatement The original publication is available at www.springerlink.com en_US
unsw.relation.faculty Engineering
unsw.relation.ispartofconferenceLocation Brisbane, Australia en_US
unsw.relation.ispartofconferenceName 11th Australian Joint Conference on Artificial Intelligence, AI’98 en_US
unsw.relation.ispartofconferenceProceedingsTitle Proceedings of the 11th Australian Joint Conference on Artificial Intelligence, AI’98 en_US
unsw.relation.ispartofconferenceYear 1998 en_US
unsw.relation.ispartofpagefrompageto 26-37 en_US
unsw.relation.originalPublicationAffiliation Milne, Linda, Computer Science & Engineering, Faculty of Engineering, UNSW en_US
unsw.relation.school School of Computer Science and Engineering *
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