Low Level Parallelization of Nonlinear Diffusion Filtering Algorithms for Cluster Computing Environments

Object based image analysis has gained on the traditional per-pixel multi-spectral based approaches. The main pitfall of using anisotropic diffusion for creating a multi scale representation of a remotely sensed image remains the computational burden.Producing the coarser scales in a multi scale representation or, diffusing spatially large images involves significant time and resources. This paper proposes a fast approach for anisotropic diffusion that overcomes spatial size limitations by distributing the diffusion as individual sub-processes over several overlapping sub-images. The overlap areas are synchronized at specific diffusion time ensuring that the fast approximation does not deviate too much from its single process equivalent. The later shall be demonstrated for a medium sized image, which can be diffused using both schemes. In addition, we shall include experimental data for very large images that can not efficiently be processed using a sequential approach.

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Multi scale representation for remotely sensed images using fast anisotropic diffusion filtering

Vanhamel

Alrefaya

Sahli

2010

2010 IEEE International Geoscience and Remote Sensing Symposium

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Low Level Parallelization of Nonlinear Diffusion Filtering Algorithms for Cluster Computing Environments

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Multi scale representation for remotely sensed images using fast anisotropic diffusion filtering

Multi scale representation for remotely sensed images using fast anisotropic diffusion filtering

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