Data Transformation Functions for Expanded Search Spaces in Geographic Sample Supervised Segment GenerationReport as inadecuate




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German Remote Sensing Data Center DFD, German Aerospace Center DLR, 82234 Oberpfaffenhofen, Germany





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Abstract Sample supervised image analysis, in particular sample supervised segment generation, shows promise as a methodological avenue applicable within Geographic Object-Based Image Analysis GEOBIA. Segmentation is acknowledged as a constituent component within typically expansive image analysis processes. A general extension to the basic formulation of an empirical discrepancy measure directed segmentation algorithm parameter tuning approach is proposed. An expanded search landscape is defined, consisting not only of the segmentation algorithm parameters, but also of low-level, parameterized image processing functions. Such higher dimensional search landscapes potentially allow for achieving better segmentation accuracies. The proposed method is tested with a range of low-level image transformation functions and two segmentation algorithms. The general effectiveness of such an approach is demonstrated compared to a variant only optimising segmentation algorithm parameters. Further, it is shown that the resultant search landscapes obtained from combining mid- and low-level image processing parameter domains, in our problem contexts, are sufficiently complex to warrant the use of population based stochastic search methods. Interdependencies of these two parameter domains are also demonstrated, necessitating simultaneous optimization. View Full-Text

Keywords: geographic object-based image analysis; segmentation; data transformations; sample supervised; spatial metrics; metaheuristics geographic object-based image analysis; segmentation; data transformations; sample supervised; spatial metrics; metaheuristics





Author: Christoff Fourie * and Elisabeth Schoepfer

Source: http://mdpi.com/



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