Title: Synthetic aperture radar image compression based on multi-scale geometric transforms

Authors: Amel Bouchemha; Mohamed Cherif Nait-Hamoud; Noureddine Doghmane

Addresses: Department of Electrical Engineering, Faculty of Engineering Sciences, University of Tebessa, 12000, Algeria ' Department of Mathematics and Science Computing, University of Tebessa, 12000, Algeria ' Department of Electronics, Faculty of Engineering Sciences, University of Annaba, 23000, Algeria

Abstract: Image representation in separable orthogonal basis cannot take advantage of geometrical regularity contained in basic images. When, explored efficiently geometrical regularity improves image compression. In this paper, we propose to experiment and compare an adaptive multi-scale geometric decomposition for synthetic aperture radar (SAR) image compression, called multi-scale bandelet transform, and a non-adaptive multi-scale geometric representation called ridgelet transform. The second generation of bandelet transform adopted in this work, is constructed in discrete domain with bandeletisation of warped wavelet transform along the optimal direction of geometric flow that minimises the Lagrangian. We discuss the criteria and results to assess SAR image compression performances using wavelet, bandelet, and ridgelet transforms. Our experiments revealed that during the compression phase, the speckle noise is removed from the SAR images inducing further improvements of the coding efficiency. In order, to evaluate the robustness of bandelet transform, we have proposed a progressive compression scheme based on the second generation of bandelet transform combined to SPIHT encoder, which is generally integrated with the wavelet transform.

Keywords: SAR image compression; bandelet transform; geometrical flow; ridgelet transform; wavelet; SPIHT encoder.

DOI: 10.1504/IJIEI.2017.086610

International Journal of Intelligent Engineering Informatics, 2017 Vol.5 No.3, pp.225 - 241

Received: 31 May 2016
Accepted: 26 Aug 2016

Published online: 04 Sep 2017 *

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