The comparison index: A tool for assessing the accuracy of image segmentation

Möller, M., Lymburner, L., and Volk, M. (2007) The comparison index: A tool for assessing the accuracy of image segmentation. International Journal of Applied Earth Observation and Geoinformation, 9 (3). pp. 311-321.

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Abstract

Segmentation algorithms applied to remote sensing data provide valuable information about the size, distribution and context of landscape objects at a range of scales. However, there is a need for well-defined and robust validation tools to assessing the reliability of segmentation results. Such tools are required to assess whether image segments are based on ‘real’ objects, such as field boundaries, or on artefacts of the image segmentation algorithm. These tools can be used to improve the reliability of any land-use/land-cover classifications or landscape analyses that is based on the image segments.

The validation algorithm developed in this paper aims to: (a) localize and quantify segmentation inaccuracies; and (b) allow the assessment of segmentation results on the whole. The first aim is achieved using object metrics that enable the quantification of topological and geometric object differences. The second aim is achieved by combining these object metrics into a ‘Comparison Index’, which allows a relative comparison of different segmentation results. The approach demonstrates how the Comparison Index CI can be used to guide trial-and-error techniques, enabling the identification of a segmentation scale H that is close to optimal. Once this scale has been identified a more detailed examination of the CI–H- diagrams can be used to identify precisely what H value and associated parameter settings will yield the most accurate image segmentation results.

The procedure is applied to segmented Landsat scenes in an agricultural area in Saxony-Anhalt, Germany. The segmentations were generated using the ‘Fractal Net Evolution Approach’, which is implemented in the eCognition software.

Item ID: 2462
Item Type: Article (Research - C1)
ISSN: 1872-826X
Keywords: segmentation; Landsat; field detection; validation; accuracy standard; object metric
Additional Information:

Date Deposited: 21 Jul 2009 03:12
FoR Codes: 01 MATHEMATICAL SCIENCES > 0102 Applied Mathematics > 010202 Biological Mathematics @ 50%
05 ENVIRONMENTAL SCIENCES > 0502 Environmental Science and Management > 050209 Natural Resource Management @ 50%
SEO Codes: 96 ENVIRONMENT > 9605 Ecosystem Assessment and Management > 960599 Ecosystem Assessment and Management not elsewhere classified @ 100%
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