Land-cover Classification in SAR Images using Dictionary Learning

Date

2015-09-24

Author

Aktas, Gizem
Bak, Cagdas
Nar, Fatih
Sen, Nigar

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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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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.

Subject Keywords

SAR, Land-cover classification, Dictionary learning, Sparse representation, Superpixel

URI

https://hdl.handle.net/11511/67440

DOI

https://doi.org/10.1117/12.2195773

Collections

Department of Computer Engineering, Conference / Seminar

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Citation Formats

G. Aktas, C. Bak, F. Nar, and N. Sen, “Land-cover Classification in SAR Images using Dictionary Learning,” 2015, vol. 9642, p. 0, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/67440.