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Learning a Family of Detectors via Multiplicative Kernels Read chapter Read first chapter

2013 | OriginalPaper | Chapter

Segmentation Based Pattern Recognition for Peri-Urban Areas in X Band SAR Images

Authors : Bruno Cafaro, Silvia Canale, Alberto De Santis, Daniela Iacoviello, Fiora Pirri, Simone Sagratella

Published in: Topics in Medical Image Processing and Computational Vision

Publisher: Springer Netherlands

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Abstract

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.

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Metadata
Title
Segmentation Based Pattern Recognition for Peri-Urban Areas in X Band SAR Images
Authors
Bruno Cafaro
Silvia Canale
Alberto De Santis
Daniela Iacoviello
Fiora Pirri
Simone Sagratella
Copyright Year
2013
Publisher
Springer Netherlands
Book
Topics in Medical Image Processing and Computational Vision

Print ISBN: 978-94-007-0725-2

Electronic ISBN: 978-94-007-0726-9

Copyright Year: 2013

DOI
https://doi.org/10.1007/978-94-007-0726-9_15