Proceedings, XXth congress: Proceedings, XXth congress

Altan, M. Orhan

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Multivolume work

Persistent identifier:: 1663674213

Title:: Proceedings, XXth congress

Sub title:: Istanbul, 12 - 23 July 2004

Year of publication:: 2004

Place of publication:: Istanbul

Publisher of the original:: [Verlag nicht ermittelbar]

Identifier (digital):: 1663674213

Language:: English

Additional Notes:: Erscheinungsdatum des Originals ist aus dem Copyrightjahr ermittelt.; Auch bezeichnet als XXth International Congress for Photogrammetry and Remote Sensing

Editor:: Altan, M. Orhan

Corporations:: International Society for Photogrammetry and Remote Sensing, Congress, 20., 2004, Istanbul; International Society for Photogrammetry and Remote Sensing, Commission Primary Data Acquisition

Adapter:: International Society for Photogrammetry and Remote Sensing, Congress, 20., 2004, Istanbul; International Society for Photogrammetry and Remote Sensing, Commission Primary Data Acquisition

Founder of work:: International Society for Photogrammetry and Remote Sensing, Congress, 20., 2004, Istanbul; International Society for Photogrammetry and Remote Sensing, Commission Primary Data Acquisition

Other corporate:: International Society for Photogrammetry and Remote Sensing, Congress, 20., 2004, Istanbul; International Society for Photogrammetry and Remote Sensing, Commission Primary Data Acquisition

Document type:: Multivolume work

Volume

Persistent identifier:: 166368779X

Title:: Proceedings, XXth congress

Scope:: IV, 226 Seiten

Year of publication:: 2004

Place of publication:: Istanbul

Publisher of the original:: [Verlag nicht ermittelbar]

Identifier (digital):: 166368779X

Illustration:: Illustrationen, Diagramme

Signature of the source:: ZS 312(35,B8)

Language:: English

Additional Notes:: Erscheinungsdatum des Originals ist aus dem Copyrightjahr ermittelt.

Usage licence:: Attribution 4.0 International (CC BY 4.0)

Editor:: Altan, M. Orhan

Corporations:: International Society for Photogrammetry and Remote Sensing, Congress, 20., 2004, Istanbul; International Society for Photogrammetry and Remote Sensing

Adapter:: International Society for Photogrammetry and Remote Sensing, Congress, 20., 2004, Istanbul; International Society for Photogrammetry and Remote Sensing

Founder of work:: International Society for Photogrammetry and Remote Sensing, Congress, 20., 2004, Istanbul; International Society for Photogrammetry and Remote Sensing

Other corporate:: International Society for Photogrammetry and Remote Sensing, Congress, 20., 2004, Istanbul; International Society for Photogrammetry and Remote Sensing

Publisher of the digital copy:: Technische Informationsbibliothek Hannover

Place of publication of the digital copy:: Hannover

Year of publication of the original:: 2019

Document type:: Volume

Collection:: Earth sciences

Chapter

Title:: An Adaptive Content-Based Localized Watermarking Algorithm for Remote Sensing Image Xianmin Wang, Zequn Guan, Chenhan Wu

Document type:: Multivolume work

Structure type:: Chapter

International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol XXXV, Part B-YF. Istanbul 2004 1) making three-level DWT of the original remote sensing sub-image (with the size of D XD) f: A A; 2) establishing the low-frequency coefficient (LL3) matrix C" in the wavelet domain A of the remote sensing sub-image A; 3) choosing a feature vector D={ d, Pec * from the low-frequency coefficient matrix; 4) according to formula (1), embedding the watermark W-(W, ) into (d ,) and obtaining the new feature vector D={dk;e B; 5) reconstructing the watermarked remote sensing sub-image B with the new transform field matrix D y Bp» In addition, we should notice that for different remote sensing sub-images, the watermark bits embedded in them are also different, namely the embedded watermark bits are based on the special content of each sub-image and different from each other. 3. Choice of localized watermarks In order to guarantee robustness and security of watermarking algorithm, a nature choice is to combine localized watermarks with the feature of remote sensing sub-images. To the feature vector of a remote sensing sub-image D={ d y »» generally the corresponding watermarking space F ,, C F s orthogonal to D p W g can be found: Fy -(W: 9, d,w,70 kel, 2, ~K) (2) k namely, to any vector W={ W, } €F ,, we have D! w-o (3) We can see that there are countless watermarks meeting W-(W,)€F;,, however, in fact the establishment of watermarking space F,, is a controlled optimized process, namely W= arg MIN D' W| (4) wef" when D is the feature vector of the original remote sensing sub-image. 194 The ending condition of optimization is (5) ID'w| «e and Ó (O »0 ) is the beforehand defined threshold in the optimizing process. Here we choose 0-1 0:3, Repeating the above process for each remote sensing sub-image which corresponds to each feature point, we can obtain the watermarked remote sensing image 4. Detection Frame of Localized Watermarks We exploited the Neymann-Pearson criterion to detect watermarks. We made edge-detection to the watermarked remote sensing image, chose the angle points in the edge image as the candidate points, then detected watermarks in each D X D sub-image, in the center of which is the candidate points. As long as in one sub-image corresponding to some feature point, the watermark can be detected, we think there be localized watermarks in the image. 5. Results of Simulative Experiments In this paper, we exploited MATLAB to simulate the experiments and made experiments to a 600 X 800 partial SPOTS image of Shanghai. In order to evaluate robustness of the algorithm, during the experiments we exploited the testing software of watermark attacks-StirMark. The testing method is similar to COX method (Cox I J, 1997a). Firstly we exploited db8 wavelet and thinning algorithm of mathematic morphology to extract the edges of the original remote sensing image. The size of those local areas has an important influence on the robustness of watermarks: smaller areas can make watermarks better resist cropping, but also decrease robustness of the local watermarks; and bigger areas can increase robustness of the local watermarks but would result in no complete watermark-extracting areas in the remaining partial remote sensing image after cropping. In the experiment, we chose the maximum number of feature points as 20 and the size of the area corresponding with each feature point as 96 X 96. Then to each 96 X96 remote sensing sub-image we take the same process as follows: making three-level DWT of the remote sensing sub-image by bior2.6 wavelet, establishing the low-frequency matrix c" (12X12) in DWT transform field, obtaining the feature vector D — (d k), which consisted of the anterior 12 coefficients in C * which had the maximum breadths (or the maximum angles) (not including the direct current weight), then according to formula (5) , making optimized repetition to work out the localized watermark W={ W, } which is a 12X 1 two-value vector orthogonal to D = {dk}, namely W={ W, } € { 1, -1}, i= 1, 2, »-", 12. From the experimental results, we can see because the watermarking

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