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Remote Sensing Image Classification Using Attribute Filters Defined over the Tree of Shapes

Remote Sensing Image Classification Using Attribute Filters Defined over the Tree of Shapes


Titill: Remote Sensing Image Classification Using Attribute Filters Defined over the Tree of Shapes
Höfundur: Benediktsson, Jon Atli   orcid.org/0000-0003-0621-9647
Cavallaro, Gabriele
Dalla Mura, Mauro   orcid.org/0000-0002-9656-9087
Plaza, Antonio   orcid.org/0000-0002-9613-1659
Útgáfa: 2016
Tungumál: Enska
Umfang: 3899 - 3911
Háskóli/Stofnun: Háskóli Íslands
University of Iceland
Svið: Verkfræði- og náttúruvísindasvið (HÍ)
School of Engineering and Natural Sciences (UI)
Deild: Rafmagns- og tölvuverkfræðideild (HÍ)
Faculty of Electrical and Computer Engineering (UI)
ISSN: 0196-2892
Efnisorð: Gray-scale; Morphology; Remote sensing; Shape; Spatial resolution; Vegetation
URI: https://hdl.handle.net/20.500.11815/59

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Tilvitnun:

Gabriele Cavallaro, Mauro Dalla Mura, Jón Atli Benediktsson, Antonio Plaza. "Remote Sensing Image Classification Using Attribute Filters Defined over the Tree of Shapes." IEEE Transactions on Geoscience and Remote Sensing (Volume:54, Issue:7)

Útdráttur:

Remotely sensed images with very high spatial resolution provide a detailed representation of the surveyed scene with a geometrical resolution that, at the present, can be up to 30 cm (WorldView-3). A set of powerful image processing operators have been defined in the mathematical morphology framework. Among those, connected operators [e.g., attribute filters (AFs)] have proven their effectiveness in processing very high resolution images. AFs are based on attributes which can be efficiently implemented on tree-based image representations. In this paper, we considered the definition of min, max, direct, and subtractive filter rules for the computation of AFs over the tree-of-shapes representation. We study their performance on the classification of remotely sensed images. We compare the classification results over the tree of shapes with the results obtained when the same rules are applied on the component trees. The random forest is used as a baseline classifier, and the experiments are conducted using multispectral data sets acquired by QuickBird and IKONOS sensors over urban areas.

Athugasemdir:

Post-print. Lokaútgáfa höfunda

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