Share Email Print
cover

Journal of Applied Remote Sensing

Near-lossless spread spectrum watermarking for multispectral remote sensing images
Author(s): Farid Melgani; Redha Benzid; Francesco G.B. De Natale
Format Member Price Non-Member Price
PDF $20.00 $25.00
cover GOOD NEWS! Your organization subscribes to the SPIE Digital Library. You may be able to download this paper for free. Check Access

Paper Abstract

Watermarking represents a potentially effective tool for the protection and verification of ownership rights in remotely sensed imagery. Such data however cannot undergo critical quality degradation processes, for they must ensure accurate extraction of thematic and scientific information. In this paper, we propose to adapt a state-of-the-art spread spectrum method found to be particularly powerful for real world images to make the watermark insertion process near-lossless while preserving most of its effective robustness capability. This is done by embedding the watermark in the middle-frequency range of the discrete cosine transform (DCT) domain instead of the low-frequency range. The exact position is determined by a numerical root-finding method, targeted to achieve a userspecified minimum level of robustness against a given ensemble of attacks. In this way, an optimal trade-off can be obtained between quality and robustness. Thorough experimental tests over a multispectral remote sensing image show that the proposed method allows to trade a contained loss in robustness with a significant gain in image quality, quantified in terms of both peak signal-to-noise ratio (PSNR) and impact on classification performance. In addition, they reveal an interesting property of the watermark insertion shifting into the middle-frequency range, consisting of a sharp increase in robustness to the widely used cropping operation, which is considered the most critical attack in remote sensing imagery.

Paper Details

Date Published: 1 January 2007
PDF: 17 pages
J. Appl. Remote Sens. 1(1) 013501 doi: 10.1117/1.2535355
Published in: Journal of Applied Remote Sensing Volume 1, Issue 1
Show Author Affiliations
Farid Melgani, Univ. degli Studi di Trento (Italy)
Redha Benzid, Univ. of Batna (Algeria)
Francesco G.B. De Natale, Univ. degli Studi di Trento (Italy)


© SPIE. Terms of Use
Back to Top