Discriminant analysis in aerial images using fractal-based features

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Abstract
This paper presents a processing technique for computer assisted discriminant analysis in remote sensing applications. Local features extracted using Richardson's power law are investigated for their discriminatory power and a nonparametric classification scheme based on probability density function estimation is suggested. The capability to adjust false alarm rates and perform on-line learning is provided by this probabilistic approach. Case studies indicating the ability to discriminate between classes of objects in aerial images are presented. The technique can be used as a preprocessing aid in segmentation or in conjunction with morphological features in a more complete discrimination system.

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