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Proceedings Paper

Land-cover classification in SAR images using dictionary learning
Author(s): Gizem Aktaş; Çağdaş Bak; Fatih Nar; Nigar Şen
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Paper Abstract

Land-cover classification in Synthetic Aperture Radar (SAR) images has significance in both civil and military remote sensing applications. Accurate classification is a challenging problem due to variety of natural and man-made objects, seasonal changes at acquisition time, and diversity of image reconstruction algorithms.. In this study, Feature Preserving Despeckling (FPD), which is an edge preserving total variation based speckle reduction method, is applied as a preprocessing step. To handle the mentioned challenges, a novel feature extraction schema combined with a super-pixel segmentation and dictionary learning based classification is proposed. Computational complexity is another issue to handle in processing of high dimensional SAR images. Computational complexity of the proposed method is linearly proportional to the size of the image since it does not require a sliding window that accesses the pixels multiple times. Accuracy of the proposed method is validated on the dataset composed of TerraSAR-X high resolutions spot mode SAR images.

Paper Details

Date Published: 15 October 2015
PDF: 14 pages
Proc. SPIE 9642, SAR Image Analysis, Modeling, and Techniques XV, 964205 (15 October 2015); doi: 10.1117/12.2195773
Show Author Affiliations
Gizem Aktaş, SDT Space and Defence Technologies (Turkey)
Çağdaş Bak, SDT Space and Defence Technologies (Turkey)
Fatih Nar, SDT Space and Defence Technologies (Turkey)
Nigar Şen, SDT Space and Defence Technologies (Turkey)


Published in SPIE Proceedings Vol. 9642:
SAR Image Analysis, Modeling, and Techniques XV
Claudia Notarnicola; Simonetta Paloscia; Nazzareno Pierdicca, Editor(s)

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