The Evolution of the Morphological Profile: from Panchromatic to Hyperspectral Images

  • Mauro Dalla Mura
  • Jon Atli Benediktsson
  • Jocelyn Chanussot
  • Lorenzo Bruzzone
Chapter
Part of the Augmented Vision and Reality book series (Augment Vis Real, volume 3)

Abstract

Almost a decade has passed since the concept of morphological profile (MP) was defined for the analysis of panchromatic remote sensing images. From that time, the MP has largely proved to be a powerful tool able to model the spatial information (e.g., contextual relations) of the image by extracting structural features (e.g., size, geometry, etc.) from the objects present in the scene. The MP processes an input image with a sequence of progressively coarser filters. This leads to a stack of filtered images showing an increasing simplification of the scene. The evaluation of how the objects in the image interact with the filters gives information on the objects structural features. The great amount of contributions present in the literature that address the application of MP to many tasks (e.g., classification, object detection, segmentation, change detection, etc.) and to different types of images (e.g., panchromatic, multispectral, hyperspectral) proves how MP is still an effective and modern tool. Moreover, many variants, extensions and refinements of its definition have also appeared stating that the MP is still under continuous development. This chapter presents the MP from its early definition to the recent advances based on morphological attribute filters. The overview of many significant contributions that have appeared in this decade allows the reader to track the evolution of the MP from the analysis of panchromatic to hyperspectral images.

Keywords

Morphological profile Extended morphological profile Attribute profiles Attribute filters 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Mauro Dalla Mura
    • 1
    • 2
  • Jon Atli Benediktsson
    • 2
  • Jocelyn Chanussot
    • 3
  • Lorenzo Bruzzone
    • 1
  1. 1.Department of Information Engineering and Computer ScienceUniversity of TrentoPovo, TrentoItaly
  2. 2.Faculty of Electrical and Computer EngineeringUniversity of IcelandReykjavikIceland
  3. 3.GIPSA-Laboratory, Signal and Image DepartmentGrenoble Institute of Technology (INP)Grenoble CedexFrance

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