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Remote Sens. 2014, 6(5), 3791-3821; doi:10.3390/rs6053791
Article

Data Transformation Functions for Expanded Search Spaces in Geographic Sample Supervised Segment Generation

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Received: 30 January 2014; in revised form: 28 March 2014 / Accepted: 16 April 2014 / Published: 28 April 2014
(This article belongs to the Special Issue Advances in Geographic Object-Based Image Analysis (GEOBIA))
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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.
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
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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MDPI and ACS Style

Fourie, C.; Schoepfer, E. Data Transformation Functions for Expanded Search Spaces in Geographic Sample Supervised Segment Generation. Remote Sens. 2014, 6, 3791-3821.

AMA Style

Fourie C, Schoepfer E. Data Transformation Functions for Expanded Search Spaces in Geographic Sample Supervised Segment Generation. Remote Sensing. 2014; 6(5):3791-3821.

Chicago/Turabian Style

Fourie, Christoff; Schoepfer, Elisabeth. 2014. "Data Transformation Functions for Expanded Search Spaces in Geographic Sample Supervised Segment Generation." Remote Sens. 6, no. 5: 3791-3821.


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