Active Learning in the Spatial Domain for Remote Sensing Image Classification

Active learning (AL) algorithms have been proven useful in reducing the number of required training samples for remote sensing applications; however, most methods query samples pointwise without considering spatial constraints on their distribution. This may often lead to a spatially dispersed distr...

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Bibliographic Details
Published in:	IEEE transactions on geoscience and remote sensing Vol. 52; no. 5; pp. 2492 - 2507
Main Authors:	Stumpf, Andre, Lachiche, Nicolas, Malet, Jean-Philippe, Kerle, Norman, Puissant, Anne
Format:	Journal Article
Language:	English
Published:	New York, NY IEEE 01.05.2014 Institute of Electrical and Electronics Engineers The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:	Active learning (AL) Algorithms Applied geophysics Assessments batch-mode class imbalance Computer Science Earth sciences Earth, ocean, space Entropy Exact sciences and technology ground truth uncertainty image classification Internal geophysics landslide inventory mapping Landslides Landslides & mudslides Radio frequency Remote sensing Sampling Sensitivity analysis Signal and Image Processing Spatial distribution spatial information Terrain factors Training Training data Uncertainty class imbalance image classification landslide inventory mapping Active learning (AL) ground truth uncertainty batch-mode spatial information algorithms ground truth sensitivity analysis Space remote sensing sampling satellite measurements classification inventory trees spatial distribution interpretation landslides imagery uncertainties errors
ISSN:	0196-2892, 1558-0644
Online Access:	Get full text
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