Abstract
The paper focuses on the adaptive segmentation of aerial images for aerial surveillance. The adaptive segmentation is achieved by the cooperation of on-line model modification and model based image segmentation through RBF neural network classifier. The on-line model modification allows the RBF classifier to adapt to the changes of geographical features on the aerial images. In addition, the Gabor filtering method for feature extraction is proposed in this experiment to discriminate between geographical features for better image segmentation.
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Baik, S.W., Ahn, S.M., Lee, J.W., Win, K.K. (2003). Adaptive Segmentation of Remote-Sensing Images for Aerial Surveillance. In: Petkov, N., Westenberg, M.A. (eds) Computer Analysis of Images and Patterns. CAIP 2003. Lecture Notes in Computer Science, vol 2756. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-45179-2_67
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DOI: https://doi.org/10.1007/978-3-540-45179-2_67
Publisher Name: Springer, Berlin, Heidelberg
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