In this paper Synthetic Aperture Radar (SAR) images in X-band were analyzed in order to infer ground properties from data. The aim was to classify different zones in peri-urban forestries integrating information from different sources. In particular the X band is sensitive to the moisture content of the ground that can be therefore put into relation with the gray level of the image; moreover, the gray level is related to the smoothness and roughness of the ground. An integration of image segmentation and machine learning methods is studied to classify different zones of peri-urban forestries, such as trees canopies, lawns, water pounds, roads, etc., directly from the gray level signal properties. As case study the X-SAR data of a forest near Rome, the Castel Fusano area, are analyzed.
CITATION STYLE
Cafaro, B., Canale, S., De Santis, A., Iacoviello, D., Pirri, F., & Sagratella, S. (2013). Segmentation based pattern recognition for peri-urban areas in X band SAR images. In Lecture Notes in Computational Vision and Biomechanics (Vol. 8, pp. 275–289). Springer Netherlands. https://doi.org/10.1007/978-94-007-0726-9_15
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