Journal
IEEE GEOSCIENCE AND REMOTE SENSING LETTERS
Volume 12, Issue 6, Pages 1282-1286Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/LGRS.2015.2393255
Keywords
Automated parameterization; geographic object-based image analysis (GEOBIA); image segmentation; urban areas; WorldView-2
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Funding
- Department of Geography of the University of Toronto Mississauga
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Image segmentation is a key step in geographic object-based image analysis. Numerous segmentation techniques, e.g., watershed segmentation, mean-shift segmentation, and fractal net evolution algorithm, have been proposed and applied for various types of image analysis tasks. The majority of the segmentation algorithms require a user-defined parameter, namely, the scale parameter, to control the sizes of segments, yet the automation of the scale parameter remains a great challenge. Over the past few years, several automated parameterization methods, such as the estimation of scale parameters (ESP) tool, have been developed. However, few of the existing methods are able to enhance both intrasegment homogeneity and intersegment heterogeneity. In this letter, we proposed an energy function method that aimed at enhancing the characteristics of intrasegment homogeneity and intersegment heterogeneity, simultaneously, to identify the optimal segmentation scale for image segmentation. The intersegment heterogeneity was calculated as the weighted gradient from a segment to its neighbors by spectral angle, whereas the intrasegment homogeneity was quantified by the mean spectral angle within a segment. The performance of the proposed method was evaluated by applying it to a WorldView-2 multispectral image of Toronto, Canada, and comparing it with the local-peak-based method, which considered only the intrasegment homogeneity of an image. The scale parameter identified by the proposed method can better characterize the reference geo-objects over the entire image. The accuracy assessment result shows that the proposed method outperformed the existing technique by reducing the discrepancy by 17.9%.
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