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Efficient implementation of morphological index for building/shadow extraction from remotely sensed images

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Abstract

Morphological building index (MBI) and morphological shadow index (MSI) are recently developed techniques that aim at automatically detect buildings/shadows using high-resolution remotely sensed imagery. The traditional mathematical morphology operations are usually time-consuming as they are based on the consideration of a wide range of image-object properties, such as brightness, contrast, shapes, sizes, and in the application of series of repeated transformations (e.g., classical opening and closing operators). In the case of MBI and MSI, the computational complexity is also increased due to the use of multiscale and multidirectional morphological operators. In this paper, we provide a computationally efficient implementation of MBI and MSI algorithms which is specifically developed for commodity graphic processing units using NVIDIA CUDA. We perform the evaluation of the parallel version of the algorithms using two different NVIDIA architectures and three widely used hyperspectral data sets. Experimental results show that the computational burden introduced when considering multidirectional morphological operators can be almost completely removed by the developed implementations.

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Acknowledgments

The authors gratefully acknowledge the Associate Editor and the Anonymous Reviewers for their detailed and highly constructive criticisms, which greatly helped us to improve the quality and presentation of our manuscript.

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Authors and Affiliations

  1. Hyperspectral Computing Laboratory, Department of Computer Technology and Communications, Avenida de la Universidad s/n, 10003, Cáceres, Spain

    Luis Ignacio Jiménez, Javier Plaza & Antonio Plaza

Authors
  1. Luis Ignacio Jiménez
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  2. Javier Plaza
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  3. Antonio Plaza
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Corresponding author

Correspondence to Javier Plaza.

Additional information

This work has been supported by Junta de Extremadura (decreto 297/2014, ayudas para la realización de actividades de investigación y desarrollo tecnológico, de divulgación y de transferencia de conocimiento por los Grupos de Investigación de Extremadura, Ref. GR15005). This work was supported by the computing facilities of Extremadura Research Centre for Advanced Technologies (CETA-CIEMAT), funded by the European Regional Development Fund (ERDF). CETA-CIEMAT belongs to CIEMAT and the Government of Spain.

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Jiménez, L.I., Plaza, J. & Plaza, A. Efficient implementation of morphological index for building/shadow extraction from remotely sensed images. J Supercomput 73, 482–494 (2017). https://doi.org/10.1007/s11227-016-1890-9

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  • DOI: https://doi.org/10.1007/s11227-016-1890-9

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