Optimal tiling of heterogeneous images

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Abstract
A novel information theoretical approach has been developed, implemented and tested to approximate large, heterogeneous images with maps of varying spatial resolution and predefinedcomplexity. The goal of the approximation is the derivation of units in the spatial domain rather than determining classes in the attribute (spectral) domain. The proposed procedure is a regular, hierarchical tiling. The value of each tile is the local mean and the parti tion leads to a map represented by a region quadtree with minimum Kullback-divergence from the original image. Kullback-divergence is a non-parametric measure of dissimilarity of two spatial distributions and is applied here because of several advantageous properties. This tiling procedure can be also viewed as data compression, and it optimizes information loss under constraint on the spatial arrangement and number of tiles. The methodology is illustrated by the sampling design of an environmental soil mapping project of the salt-affected rangeland in Hortobagy, north-east Hungary.

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