Mr. Travis L. Maxwell Profile

Travis L. Maxwell

at Univ of New Brunswick

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Proceedings Article | 16 September 2005 Paper

A fuzzy approach to supervised segmentation parameter selection for object-based classification

Travis Maxwell, Yun Zhang

Proceedings Volume 5909, 59091O (2005) https://doi.org/10.1117/12.614435

KEYWORDS: Image segmentation, Fuzzy logic, Image classification, Sensors, Fractal analysis, Spatial resolution, Visualization, Satellites, Earth observing sensors, Image analysis

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Today's very high spatial resolution satellite sensors, such as QuickBird and IKONOS, pose additional problems to the land cover classification task as a consequence of the data's high spectral variability. To address this challenge, the object-based approach to classification demonstrates considerable promise. However, the success of the object-oriented approach remains highly dependent on the successful segmentation of the image. Image segmentation using the Fractal Net Evolution approach has been very successful by exhibiting visually convincing results at a variety of scales. However, this segmentation approach relies heavily on user experience in combination with a trial and error approach to determine the appropriate parameters to achieve a successful segmentation. This paper proposes a fuzzy approach to supervised segmentation parameter selection. Fuzzy Logic is a powerful tool given its ability to manage vague input and produce a definite output. This property, combined with its flexible and empirical nature, make this control methodology ideally suited to this task. This paper will serve to introduce the techniques of image segmentation using Fractal Net Evolution as background for the development of the proposed fuzzy methodology. The proposed system optimizes the selection of parameters by producing the most advantageous segmentation in a very time efficient manner. Results are presented and evaluated in the context of efficiency and visual conformity to the training objects. Testing demonstrates that this approach demonstrates significant promise to improve the object-based classification workflow and provides recommendations for future research.

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