X-SAR SpotLigh images feature selection and water segmentation
| Autor: | Bruno Cafaro, Silvia Canale, Fiora Pirri |
|---|---|
| Rok vydání: | 2012 |
| Předmět: |
texture elements
Computer science Feature vector Feature extraction Scale-space segmentation Feature selection sar sensor x-sar spotligh images feature selection support vector machines cosmo-skymed satellites constellation Image texture 1-norm svm lakes Segmentation Computer vision machine learning rivers synthetic aperture radar water segmentation Segmentation-based object categorization business.industry Pattern recognition Image segmentation Artificial intelligence business |
| Zdroj: | 2012 IEEE International Conference on Imaging Systems and Techniques Proceedings. |
| DOI: | 10.1109/ist.2012.6295589 |
| Popis: | In this paper we address the feature selection problem for X-SAR images and further the segmentation of specific chosen classes. After defining a suitable feature space for X-SAR images we select the most significant ones via a supervised machine learning approach: the 1-norm SVM. The selected features will be used for segmentation purposes, in order to segment water areas from the background. We shall see that the most relevant features are based on texture elements. So the segmentation is texture based and achieved with variational calculus and level set methods. The work is mainly focused on urban park X-SAR SpotLight images, where lakes and rivers are often present. The images are collected with the COSMO-SkyMed satellites constellation, equipped with a SAR sensor. |
| Databáze: | OpenAIRE |
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