6533b7dbfe1ef96bd127024a
RESEARCH PRODUCT
Remote sensing image segmentation by active queries
Jordi Munoz-mariGustau Camps-vallsDevis Tuiasubject
Active learningComputer scienceActive learning (machine learning)SvmMultispectral image0211 other engineering and technologies02 engineering and technologyMultispectral imageryClusteringMultispectral pattern recognitionArtificial Intelligence0202 electrical engineering electronic engineering information engineeringSegmentationCluster analysis021101 geological & geomatics engineeringRetrievalPixelbusiness.industryLinkageHyperspectral imagingPattern recognitionRemote sensingSupport vector machineMultiscale image segmentationHyperspectral imageryPixel ClassificationSignal Processing020201 artificial intelligence & image processingHyperspectral Data ClassificationComputer Vision and Pattern RecognitionArtificial intelligencebusinessAlgorithmsSoftwareModeldescription
Active learning deals with developing methods that select examples that may express data characteristics in a compact way. For remote sensing image segmentation, the selected samples are the most informative pixels in the image so that classifiers trained with reduced active datasets become faster and more robust. Strategies for intelligent sampling have been proposed with model-based heuristics aiming at the search of the most informative pixels to optimize model's performance. Unlike standard methods that concentrate on model optimization, here we propose a method inspired in the cluster assumption that holds in most of the remote sensing data. Starting from a complete hierarchical description of the data, the proposed strategy aims at sampling and labeling pixels in order to discover the data partitioning that best matches with the user's expected classes. Thus, the method combines active supervised and unsupervised clustering with a smart prune-and-label strategy. The proposed method is successfully evaluated in two challenging remote sensing scenarios: hyperspectral and very high spatial resolution (VHR) multispectral images segmentation. (C) 2011 Elsevier Ltd. All rights reserved.
| year | journal | country | edition | language |
|---|---|---|---|---|
| 2012-06-01 |