Abstract
Even though images with high and very high spatial resolution exhibit higher levels of detailed features, traditional image processing algorithms based on single pixel analysis are often not capable of extracting all their information. To solve this limitation, object-based image analysis approaches (OBIA) have been proposed in recent years.
One of the most important steps in the OBIA approach is the segmentation process; whose aim is grouping neighboring pixels according to some homogeneity criteria. Different segmentations will allow extracting different information from the same image in multiples scales. Thus, the major challenge is to determine the adequate scale segmentation that allows to characterize different objects or phenomena, in a single image.
In this work, an adaptation of SLIC algorithm to perform a hierarchical segmentation of the image is proposed. An evaluation method consisting of an objective function that considers the intra-variability and inter-heterogeneity of the object is implemented to select the optimal size of each region in the image. The preliminary results show that the proposed algorithm is capable to detect objects at different scale and represent in a single image, allowing a better comprehension of the land-cover, their objects and phenomena.
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Fonseca-Luengo, D., García-Pedrero, A., Lillo-Saavedra, M., Costumero, R., Menasalvas, E., Gonzalo-Martín, C. (2014). Optimal Scale in a Hierarchical Segmentation Method for Satellite Images. In: Kryszkiewicz, M., Cornelis, C., Ciucci, D., Medina-Moreno, J., Motoda, H., Raś, Z.W. (eds) Rough Sets and Intelligent Systems Paradigms. Lecture Notes in Computer Science(), vol 8537. Springer, Cham. https://doi.org/10.1007/978-3-319-08729-0_36
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DOI: https://doi.org/10.1007/978-3-319-08729-0_36
Publisher Name: Springer, Cham
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