000196283 001__ 196283
000196283 005__ 20190316235835.0
000196283 0247_ $$2doi$$a10.1109/JPROC.2012.2231951
000196283 022__ $$a1558-2256
000196283 037__ $$aARTICLE
000196283 245__ $$aActive Learning: Any Value for Classification of Remotely Sensed Data?
000196283 269__ $$a2013
000196283 260__ $$c2013
000196283 336__ $$aJournal Articles
000196283 520__ $$aActive learning, which has a strong impact on processing data prior to the classification phase, is an active research area within the machine learning community, and is now being extended for remote sensing applications. To be effective, classification must rely on the most informative pixels, while the training set should be as compact as possible. Active learning heuristics provide capability to select unlabeled data that are the “most informative” and to obtain the respective labels, contributing to both goals. Characteristics of remotely sensed image data provide both challenges and opportunities to exploit the potential advantages of active learning. We present an overview of active learning methods, then review the latest techniques proposed to cope with the problem of interactive sampling of training pixels for classification of remotely sensed data with support vector machines (SVMs). We discuss remote sensing specific approaches dealing with multisource and spatially and time-varying data, and provide examples for high-dimensional hyperspectral imagery.
000196283 6531_ $$aActive learning
000196283 6531_ $$aHyperspectral
000196283 700__ $$aCrawford, Melba M.
000196283 700__ $$0245927$$g150680$$aTuia, Devis
000196283 700__ $$aYang, Hsiuhan Lexie
000196283 773__ $$j101$$tProceedings of the IEEE$$k3$$q593-608
000196283 8564_ $$uhttps://infoscience.epfl.ch/record/196283/files/crawford-pieee-preprint.pdf$$zn/a$$s6213538$$yn/a
000196283 909C0 $$xU10244$$0252045$$pLASIG
000196283 909CO $$ooai:infoscience.tind.io:196283$$qGLOBAL_SET$$particle$$pENAC
000196283 917Z8 $$x150680
000196283 937__ $$aEPFL-ARTICLE-196283
000196283 973__ $$rNON-REVIEWED$$sPUBLISHED$$aEPFL
000196283 980__ $$aARTICLE