Morphological Attribute Profiles for the Analysis of Very High Resolution Images

• Published in: IEEE transactions on geoscience and remote sensing Vol. 48; no. 10; pp. 3747 - 3762
• Main Authors: Dalla Mura, M, Atli Benediktsson, J, Waske, B, Bruzzone, L
• Format: Journal Article
• Language: English
• Published: New York, NY IEEE 01.10.2010; Institute of Electrical and Electronics Engineers; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Applied geophysics; Cities and towns; Classification; Computation; Data mining; Earth sciences; Earth, ocean, space; Exact sciences and technology; Feature extraction; Filters; Geometric accuracy; High resolution; Image analysis; Image resolution; Information filtering; Information filters; Internal geophysics; Layout; Lead; Mathematical models; mathematical morphology; morphological attribute profiles (APs); morphological profiles (MPs); Multilevel; object detection; remote sensing; Spatial resolution; Studies; Transformations; very high resolution (VHR) images; Trento Italy; Southern Europe; Italy; Europe; Trentino-Alto Adige Italy; morphological profiles (MPs); models; detection; maps; morphology; accuracy; remote sensing; object detection; classification; very high resolution (VHR) images; lead; transformations; morphological attribute profiles (APs); mathematical morphology; high resolution
• ISSN: 0196-2892; 1558-0644
• DOI: 10.1109/TGRS.2010.2048116

Morphological attribute profiles (APs) are defined as a generalization of the recently proposed morphological profiles (MPs). APs provide a multilevel characterization of an image created by the sequential application of morphological attribute filters that can be used to model different kinds of the structural information. According to the type of the attributes considered in the morphological attribute transformation, different parametric features can be modeled. The generation of APs, thanks to an efficient implementation, strongly reduces the computational load required for the computation of conventional MPs. Moreover, the characterization of the image with different attributes leads to a more complete description of the scene and to a more accurate modeling of the spatial information than with the use of conventional morphological filters based on a predefined structuring element. Here, the features extracted by the proposed operators were used for the classification of two very high resolution panchromatic images acquired by Quickbird on the city of Trento, Italy. The experimental analysis proved the usefulness of APs in modeling the spatial information present in the images. The classification maps obtained by considering different APs result in a better description of the scene (both in terms of thematic and geometric accuracy) than those obtained with an MP.