Pattern based map comparisons |
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| Authors: | Roger White |
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| Institution: | (1) Department of Geography, Memorial University, St. John’s, NL, Canada, A1B 3X9 |
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| Abstract: | Map comparison techniques based on a pixel-by-pixel comparison are useful for many purposes, but fail to reveal important aspects of map similarities and differences. In contrast, pattern based map comparison techniques address the question of structural similarity, although with these approaches the comparison problem becomes open ended, since there is an unlimited number of ways to characterise a pattern. Two types of pattern based technique are used here to analyse the test sets of maps. The first, a fuzzy polygon based matching technique, focuses on the meso-scale aspects of pattern. It is based on the areal intersection of land use polygons on the two maps being compared. The areal intersection, areal complement, and polygon size values are fuzzified into an appropriate set of categories, a set of fuzzy inference rules is applied to derive memberships in local matching categories, and finally the local matching category memberships are defuzzified to yield local matching values for each land use polygon on the reference map. The second approach, fractal analysis, captures macro-scale or global qualities of the maps. Two measures are calculated here: the radial dimension and the cluster size—frequency dimension. The polygon matching approach provides only limited insight when applied to the case of the map set representing differences in classification. It proves much more effective when applied to the problem of change detection, revealing areas where the pattern has changed and giving local measures of the degree of change. The approach is particularly useful in the case where there is a considerable degree of random change at the pixel level, as changes in the underlying pattern are extracted from the noise, while pixel based approaches largely detect the noise. |
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| Keywords: | Polygon matching Fuzzy comparison Pattern similarity Radial dimension Cluster size frequency dimension |
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