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

Gabriele Cavallaro (1) , Mauro Dalla Mura (2) , Jon Atli Benediktsson (1) , Antonio Plaza (3)

1 University of Iceland [Reykjavik]
2 GIPSA-SIGMAPHY - GIPSA - Signal Images Physique
3 Hyperspectral Computing Laboratory (Caceres, Spain)

—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) have proven their effectiveness in processing very high resolution images. Attribute filters are based on attributes which can be efficiently implemented on tree-based image representations. In this work, we considered the definition of min, max, direct and subtractive filter rules for the computation of attribute filters 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.

Mots clés: Index Terms—Remote sensing; Classification; Mathematical Morphology; Tree of Shapes

Domaines: Traitement du signal et de l'image [eess.SP]

Gabriele Cavallaro, Mauro Dalla Mura, Jon Atli Benediktsson, Antonio Plaza. Remote Sensing Image Classification Using Attribute Filters Defined over the Tree of Shapes. IEEE Transactions on Geoscience and Remote Sensing, 2016, 54 (7), pp.3899-3911. ⟨10.1109/TGRS.2016.2530690⟩. ⟨hal-01272019⟩