Háskóli ÍslandsUniversity of IcelandBenediktsson, Jon AtliCavallaro, GabrieleDalla Mura, MauroPlaza, Antonio2016-08-112016-08-1120162015Gabriele 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)0196-2892https://hdl.handle.net/20.500.11815/59Post-print. Lokaútgáfa höfundaRemotely 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.3899 - 3911eninfo:eu-repo/semantics/openAccessGray-scaleMorphologyRemote sensingShapeSpatial resolutionVegetationinfo:eu-repo/semantics/articleIEEE Transactions on Geoscience and Remote Sensing