XXII ISPRS Congress 2012: Technical Commission VII

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Multivolume work

Persistent identifier:: 1663813779

Title:: XXII ISPRS Congress 2012

Sub title:: Melbourne, Australia, 25 August-1 September 2012

Year of publication:: 2013

Place of publication:: Red Hook, NY

Publisher of the original:: Curran Associates, Inc.

Identifier (digital):: 1663813779

Language:: English

Additional Notes:: Kongress-Thema: Imaging a sustainable future

Corporations:: International Society for Photogrammetry and Remote Sensing, Congress, 22., 2012, Melbourne; International Society for Photogrammetry and Remote Sensing

Adapter:: International Society for Photogrammetry and Remote Sensing, Congress, 22., 2012, Melbourne; International Society for Photogrammetry and Remote Sensing

Founder of work:: International Society for Photogrammetry and Remote Sensing, Congress, 22., 2012, Melbourne; International Society for Photogrammetry and Remote Sensing

Other corporate:: International Society for Photogrammetry and Remote Sensing, Congress, 22., 2012, Melbourne; International Society for Photogrammetry and Remote Sensing

Document type:: Multivolume work

Volume

Persistent identifier:: 1663821976

Title:: Technical Commission VII

Scope:: 546 Seiten

Year of publication:: 2013

Place of publication:: Red Hook, NY

Publisher of the original:: Curran Associates, Inc.

Identifier (digital):: 1663821976

Illustration:: Illustrationen, Diagramme

Signature of the source:: ZS 312(39,B7)

Language:: English

Additional Notes:: Erscheinungsdatum des Originals ist ermittelt.; Literaturangaben

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

Corporations:: International Society for Photogrammetry and Remote Sensing, Congress, 22., 2012, Melbourne; International Society for Photogrammetry and Remote Sensing

Adapter:: International Society for Photogrammetry and Remote Sensing, Congress, 22., 2012, Melbourne; International Society for Photogrammetry and Remote Sensing

Founder of work:: International Society for Photogrammetry and Remote Sensing, Congress, 22., 2012, Melbourne; International Society for Photogrammetry and Remote Sensing

Other corporate:: International Society for Photogrammetry and Remote Sensing, Congress, 22., 2012, Melbourne; 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:: [VII/6: REMOTE SENSING DATA FUSION]

Document type:: Multivolume work

Structure type:: Chapter

Chapter

Title:: TEXTURE ANALYSIS BASED FUSION EXPERIMENTS USING HIGH-RESOLUTION SAR AND OPTICAL IMAGERY Shuhe Zhao, Yunxiao Luo, Hongkui Zhou, Qiao Xue, An Wang

Document type:: Multivolume work

Structure type:: Chapter

tion in multi- re intensive 3) 1e of images similarity of better fusion. (4) mages before e mean value CM, they are ween them is erent moving iral measures | measures of ame linear or | — (g)). The dow size of Homogeneity n negative to ist with other ncy, but the ogeneity and vindow sizes, respectively. ved the same 'e correlation | others all wed that the ; between the ndancy. of an image, om Fig.2 (h) creasing with at the size of n to calculate as applied to o replace the ectral image. by GLCM, and correlations between them, which has been analyzed above. The textural measure of Entropy based fusion has more spatial information, and less spectral distortion. Homogeneity based fusion has the least information, and Second moment based fusion has the largest Spectral distortions. So when we fuse textural measures with images, it is important to choose the textural measures. Then the evaluation index, Shannon entropy, sharpness, spectral distortion and correlation coefficient were applied to evaluate the fusion results. From Table 1, we can see that the information abundance of fusion with single textural measure are all increased. That means it is reasonable to fuse texture with images. But the results are close to each other. That is because they are all obtained International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XXXIX-B7, 2012 XXII ISPRS Congress, 25 August — 01 September 2012, Melbourne, Australia "exture measures Sharpness Shannon Spectral correlation entropy distortions coefficient Mean 7.3447 1.1949 25.0928 0.7536 Variance 6.2519 1.1270 24.5272 0.7387 Homogeneity 6.9448 0.9866 27.9070 0.7809 Contrast 6.2741 1.1281 24.4223 0.7399 Dissimilarity 7.5511 1.1983 24.6145 0.7624 Entropy 8.2487 1.2574 24.9710 0.7811 Second moment 8.2528 1.0632 33.3779 0.7151 Original QuickBird image 10.6940 0.9332 --- -— Table 1. Evaluation of PCA fusion based on texture analysis 5. CONCLUSION The results of the procedure proposed in this letter show that it is possible to extract some kind of texture information high satellite SAR images and fuse them with some methods. In this study, texture has been extracted by GLCM after analysis, and fusion performed simply by PCA at pixel level. The results are not so satisfactory. That [^ qvYTICYYCETEY * + * + : d set ilzaae doo De ah sai ac RM vod ; coves tittle + * Gs as té € 2 sat Ss E E ¥ ia s egt S 3 s Currelatiu of Mean = * 3 on d ; + i 3. i = M 28 FF à m Bk Fe: yp Bisintant = te 2 s y > } X. E "i A 3 Correlation af Variance E w GEE s xa* Window Site > 3 Seonnd Moment x* 5 2n + + 3 + + hy i $a x a s 1 BR AR 7 E * sent » pe Sau at Window Size „1 28 war: £2 it. QbRatjésqa oo 3J*ex43443 4 + > QU (b) ir 380000008 * » Wr .... . eod asas „se ........ * nll ESL = 3 3 £ : > EFFRITQUTEEEE 5 a N rd * Sek € 3 325k 3 24 : © 8 É € à E % 3 atn $ 24 $39 Correlatiun of Déssimilarity = 5 i. ig Pa = 3 + 4 t Bu : Wo wq c ES 284 ET Correlation of Canteast 3 CU = orcelatiog of Canteas 348 Vii si © * NN iut © Jado She 50 i i 1 x i reed En à 4 * = “ » Li A [s Te $i * i E "mI E SI Window Size N ; PH 4 + * AB (d) (e) ssh * E. t à Eo + 3 82) — Correlation of Second Moawat T S 3 Windo $ = Snap 8$ w Sire 8 | eee queas “sh 2 id ^ $8 Psegponeut 9 à re â a Qmm 42 Ntsestard Des tation x Tsnoussans Cerretation Coefficirnt Correlation Coelficient t6 x is because GLCM is based on gray value statistics, and SAR image after filtering is inevitably blurred. Some texture information would be removed. In the following work, other methods (like Wavelet based) to extract texture should be proposed to improve to obtain accuracy texture, and other fusion methods can also be used. x * “er, i Bow * * T 14 BEE of! ES E Medi s $33 x 3 p * imos Sire (h) * + + s: RE EN £8 esi BZ té A 22 Ub Muss > 9e Vaname U4 i Contes *oc Dsssiilsnity iR. Varo + Correlation of Homogeneity M 2 57 Sezotil Meunzcat 48 (c) té a: A P ax b EL inii E x she" : + a Qn 34 Fl GC a:ba* ga + eed. t + + " wa x au ie & ~ ET x, = * 83H A Window Size * Sa : i » 4#* S 3 » & 3 at s * + A8 25 = Corvefation of Émropy * * tk

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