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

Abstract

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.

Efficient Learning of Spatial Patterns with Multi-Scale Conditional Random Fields for Region-Based Classification

“…From the scale space tower, we select a scale, referred to as the localization scale, at which the diffusion filter has removed the noise without affecting or dislocating the edges of the salient features in the image, and we apply to it the multi-scale region adjacency graph (MSRAG) generation process described in Section 2.2. The localization scale is determined using a maximum correlation criterion proposed in [32].…”