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Göktürk-2 ve Worldview-2 Uydu Görüntüleri için Görüntü Keskinleştirme Yöntemlerinin Değerlendirilmesi

Year 2019, , 874 - 885, 31.08.2019
https://doi.org/10.18185/erzifbed.495854

Abstract



Bu çalışmada, Göktürk-2 keskinleştirme
yapılmış görüntüsünün kırsal ve kentsel bölgelerdeki başarısının
değerlendirilmesi amaçlanmıştır. Çalışma alanı olarak Trabzon ilinin Sürmene
ilçesi seçilmiştir.Görüntü keskinleştirmesi için 5m konumsal çözünürlüklü
dört adet Multispektral(MS) banda ve bir adet 2.5 m konumsal çözünürlüklü
Pankromatik (PAN) banda sahip Göktürk-2 uydu görüntüsü ve 0.5 m konumsal
çözünürlüklü Worldview-2 PAN bandı kullanılmıştır. İlk olarak, görüntülerin
ön işlemesi için Göktürk-2 MS ve PAN görüntüleri, Worldview-2 PAN görüntüsüne
göre piksel altı Root Mean Square (RMS) değeriyle geometrik olarak
düzeltilmiştir. Ardından Göktürk-2 MS ile Göktürk-2 PAN görüntüleri, ve
Göktürk-2 MS ile Worldview-2 PAN görüntülerinin sekiz farklı yöntem ile keskinleştirme
işlemi gerçekleştirilmiştir. Görüntü keskinleştirme işlemi için  Ehler's, Gram–Schimdt (GS), Hyperspherical
Colour Sphere (HCS), High-Pass Filter (HPF), Intensity Hue Saturation (IHS),
Principal Component (PC), Color Normalized (CN) ve Wavelet tabanlı yöntemleri
kullanılmıştır. Keskinleştirme yapılmış görüntülerin kalitesi yaygın olarak
kullanılan ve, spektral ve konumsal kaliteyi ölçen metrikler ile
değerlendirilmiştir. Ayrıca doku bilgisini çıkaran Gabor filtresi de bu
çalışmada metrik olarak kullanılmıştır. 
Bunun yanında keskinleştirme yapılmış görüntülerin değerlendirilmesi
için bitki ve su indisleri çıkarılmıştır. Orijinal ve keskinleştirme yapılmış
görüntülerin indisleri arasındaki korelâsyon katsayıları hesaplatılmıştır.
Değerlendirme sonucuna göre, kentsel alan için CN , kırsal alanda ise HPF ve
HCS görüntü keskinleştirme yöntemleri ile elde edilen sonuçların  spektral ve konumsal anlamda daha az
bozulmaya sahip olduğu görülmüştür. 
Genel olarak kentsel ve kırsal alan için  CN yönteminin doku bilgisini daha iyi
yansıttığı ve indisler yönünden de daha yüksek korelâsyona sahip olduğu
gözlenmiştir.


References

  • Akar A, Gokalp E, Akar O ve Yılmaz V. 2017. "Improving classification accuracy of spectrally similar land covers in the rangeland and plateau areas with a combination of WorldView-2 and UAV images". Geocarto International, 32(9), 990-1003.
  • Blasch E ve Liu Z. 2011. "LANDSAT Satellite Image Fusion Metric Assessment, " Proceedings of the 2011 IEEE National Aerospace and Electronics Conference (NAECON), 20-22 July 2011, Dayton, OH, USA.
  • Debnath L. 2002. Wavelet Transforms and Their Applications, Birkhäuser, 2002 edition, ISBN-10: 0817642048.
  • ENVI. 2018. "CN Spectral Sharpening", Harris Geospatial Solutions, https://www.harrisgeospatial.com/docs/cnspectralsharpening.html, Erişim tarihi: 10.12.2018
  • Flusser J, Sroubek F ve Zitov´a B. 2007. "Image Fusion:Principles, Methods, and Applications". Tutorial EUSIPCO 2007 Lecture Notes, Institute of Information Theory and Automation Academy of Sciences of the Czech Republic.
  • Gao J. 2009. Digital Analysis of Remotely Sensed Imagery, The McGraw-Hill Companies, USA.
  • Hamamoto Y, Uchimura S, Watanabe M, Yasuda T, Mitani Y ve Tomita S. 1998. "A Gabor filter-based method for recognizing handwritten numerals".Pattern Recognition, 31 (4), 395–400.
  • Han SS, Li HT ve Gu HY. 2009."Study on image fusion for high spatial resolution remote sensing images". Sci Surveying Mapping. 5,60–62.
  • Klonus S ve Ehlers M. 2009. "Performance of evaluation methods in image fusion". 2009 12th International Conference on Information Fusion, Seattle, WA, USA ,6-9 July 2009.
  • Ma J, Ma Y ve Li C. 2019."Infrared and visible image fusion methods and applications: A survey". Information Fusion, 45 (2019) 153–178.
  • Masood S, Sharif M, Yasmin M, Shahid MA ve Rehman A. 2017."Image Fusion Methods: A Survey", Journal of Engineering Science and Technology Review, 10 (6), 186- 194.
  • Mather PM. 2004. Computer Processing of Remotely-Sensed Images: An Introduction, Third edition, Wiley, USA, ISBN 0-470-84918-5.
  • Petkov N ve Wieling M. 2012. "Gabor filter for image processing and computer vision", University of Groningen, Department of Computing Science, Intelligent Systemshttp://matlabserver.cs.rug.nl/edgedetectionweb/web/ edgedetection_params.html, 12 Mart 2012.
  • Risojevic V, Momi´c S ve Babi´c Z. 2011. "Gabor descriptors for aerial image classification", in Proc. ICANNGA, II, 6594 of LNCS, 51–60. Springer Berlin/ Heidelberg.
  • Sahu DK ve Parsai MP. 2012. " Different Image Fusion Techniques –A Critical Review", International Journal of Modern Engineering Research, 2(5), 4298-4301, 2012.
  • Schowengerdt RA. 1980. "Reconstruction of multispatial, multispectral image data using spatial frequency content". Photocrammetric Engineering And Remote Sensing, 46(10),1325–1334.
  • Shah VP, Younan NH ve King RL. 2008. "An Effictient Pan-Sharpening Method Via a Combined Adaptive PCA Approach and Contourlets." IEEE Transaction on Geoscience and Remote Sensing, 46 (5),1323-1335.
  • Strait M, Rahmani S, Markurjev D, ve Wittman T. 2008. "Evaluation of pan-sharpening methods". Technical Report, UCLA Department of Mathematics, Los Angeles, CA, USA.
  • Suthakar RJ, Esther JM, Annapoorani D ve Samuel FRS. 2014. "Study of Image Fusion- Techniques, Method and Applications", International Journal of Computer Science and Mobile Computing, 3(11), November 2014,469 – 476.
  • Teke M, San E ve Koç E. 2018. "Unsharp based Pansharpening of Göktürk-2 Satellite Imagery".26th Signal Processing and Communications Applications Conference (SIU), IEEE, Izmir, Turkey, 2-5 May 2018.
  • Teke M. 2016. "Satellite Image Processing Workflow for Rasat and Göktürk-2, Journal of Aeronautics and Space Technologies, 9(1), 1-13.
  • Tso B, Mather PM. 2009. Classification Methods For Remotely Sensed Data, Second Editon, Taylor & Francis Group, United States of America.
  • Yilmaz V ve Güngör O. 2013. " Performance Analysis On Image Fusion Methods", CaGIS/ASPRS Specialty Conference 2013, San Antonio, Texas, USA.
  • Yuhendra J ve Kuze H . 2011."Performance analyzing of high resolution pan-sharpening techniques: Increasing image quality for classification using supervised kernel support vector", Research Journal of Information Technology, 3 (1), 12-23.

Assessment of Image Fusion for Göktürk-2 and Worldview-2 Satellite Images

Year 2019, , 874 - 885, 31.08.2019
https://doi.org/10.18185/erzifbed.495854

Abstract



In this study, 
it is aimed assessment of success of Göktürk-2 fused image for urban
and rural regions. Sürmene distict of Trabzon were selected as study area.
For image fusion, Göktürk-2 space borne sensor, which captures data in four
Multispectral MS (5 m spatial resolution) bands and one Panchromatic
(PAN) (2.5 m spatial resolution) band,and Worldview-2 PAN (0.5 m spatial
resolution) band were used. Firstly, for the images pre-processing Göktürk-2
MS and PAN images were registered by using Worldview-2 PAN images as  sub-pixel RMS value. And then Göktürk-2 MS
and Göktürk-2 PAN images, and Göktürk-2 MS and Worldview-2 PAN images were
fused with the eight different methods. It was used Ehler's, Gram–Schimdt
(GS), Hyperspherical Colour Sphere (HCS), High-Pass Filter (HPF), Intensity
Hue Saturation (IHS), Principal Component (PC), Color Normalized (CN) and
Wavelet based methods for image fusion. Quality of fused images were assessed
commonly used metrics that measures the spectral and spatial quality. Also
Gabor filter that extracts  texture
information was used as metric. Furhermore, Vegetation and water indices were
exploited to assess the fused images. The correlation coefficient between the
indices of the original and the fused images was calculated. As a result of
evaluation, it was seen that for urban CN, 
for rural  HPF and HCS  fusion methods had less deterioration in
spectral and spatial terms. Generally, it has been observed that CN methods
better reflect texture information in and 
has a higher correlation in terms of indices urban and rural areas.


References

  • Akar A, Gokalp E, Akar O ve Yılmaz V. 2017. "Improving classification accuracy of spectrally similar land covers in the rangeland and plateau areas with a combination of WorldView-2 and UAV images". Geocarto International, 32(9), 990-1003.
  • Blasch E ve Liu Z. 2011. "LANDSAT Satellite Image Fusion Metric Assessment, " Proceedings of the 2011 IEEE National Aerospace and Electronics Conference (NAECON), 20-22 July 2011, Dayton, OH, USA.
  • Debnath L. 2002. Wavelet Transforms and Their Applications, Birkhäuser, 2002 edition, ISBN-10: 0817642048.
  • ENVI. 2018. "CN Spectral Sharpening", Harris Geospatial Solutions, https://www.harrisgeospatial.com/docs/cnspectralsharpening.html, Erişim tarihi: 10.12.2018
  • Flusser J, Sroubek F ve Zitov´a B. 2007. "Image Fusion:Principles, Methods, and Applications". Tutorial EUSIPCO 2007 Lecture Notes, Institute of Information Theory and Automation Academy of Sciences of the Czech Republic.
  • Gao J. 2009. Digital Analysis of Remotely Sensed Imagery, The McGraw-Hill Companies, USA.
  • Hamamoto Y, Uchimura S, Watanabe M, Yasuda T, Mitani Y ve Tomita S. 1998. "A Gabor filter-based method for recognizing handwritten numerals".Pattern Recognition, 31 (4), 395–400.
  • Han SS, Li HT ve Gu HY. 2009."Study on image fusion for high spatial resolution remote sensing images". Sci Surveying Mapping. 5,60–62.
  • Klonus S ve Ehlers M. 2009. "Performance of evaluation methods in image fusion". 2009 12th International Conference on Information Fusion, Seattle, WA, USA ,6-9 July 2009.
  • Ma J, Ma Y ve Li C. 2019."Infrared and visible image fusion methods and applications: A survey". Information Fusion, 45 (2019) 153–178.
  • Masood S, Sharif M, Yasmin M, Shahid MA ve Rehman A. 2017."Image Fusion Methods: A Survey", Journal of Engineering Science and Technology Review, 10 (6), 186- 194.
  • Mather PM. 2004. Computer Processing of Remotely-Sensed Images: An Introduction, Third edition, Wiley, USA, ISBN 0-470-84918-5.
  • Petkov N ve Wieling M. 2012. "Gabor filter for image processing and computer vision", University of Groningen, Department of Computing Science, Intelligent Systemshttp://matlabserver.cs.rug.nl/edgedetectionweb/web/ edgedetection_params.html, 12 Mart 2012.
  • Risojevic V, Momi´c S ve Babi´c Z. 2011. "Gabor descriptors for aerial image classification", in Proc. ICANNGA, II, 6594 of LNCS, 51–60. Springer Berlin/ Heidelberg.
  • Sahu DK ve Parsai MP. 2012. " Different Image Fusion Techniques –A Critical Review", International Journal of Modern Engineering Research, 2(5), 4298-4301, 2012.
  • Schowengerdt RA. 1980. "Reconstruction of multispatial, multispectral image data using spatial frequency content". Photocrammetric Engineering And Remote Sensing, 46(10),1325–1334.
  • Shah VP, Younan NH ve King RL. 2008. "An Effictient Pan-Sharpening Method Via a Combined Adaptive PCA Approach and Contourlets." IEEE Transaction on Geoscience and Remote Sensing, 46 (5),1323-1335.
  • Strait M, Rahmani S, Markurjev D, ve Wittman T. 2008. "Evaluation of pan-sharpening methods". Technical Report, UCLA Department of Mathematics, Los Angeles, CA, USA.
  • Suthakar RJ, Esther JM, Annapoorani D ve Samuel FRS. 2014. "Study of Image Fusion- Techniques, Method and Applications", International Journal of Computer Science and Mobile Computing, 3(11), November 2014,469 – 476.
  • Teke M, San E ve Koç E. 2018. "Unsharp based Pansharpening of Göktürk-2 Satellite Imagery".26th Signal Processing and Communications Applications Conference (SIU), IEEE, Izmir, Turkey, 2-5 May 2018.
  • Teke M. 2016. "Satellite Image Processing Workflow for Rasat and Göktürk-2, Journal of Aeronautics and Space Technologies, 9(1), 1-13.
  • Tso B, Mather PM. 2009. Classification Methods For Remotely Sensed Data, Second Editon, Taylor & Francis Group, United States of America.
  • Yilmaz V ve Güngör O. 2013. " Performance Analysis On Image Fusion Methods", CaGIS/ASPRS Specialty Conference 2013, San Antonio, Texas, USA.
  • Yuhendra J ve Kuze H . 2011."Performance analyzing of high resolution pan-sharpening techniques: Increasing image quality for classification using supervised kernel support vector", Research Journal of Information Technology, 3 (1), 12-23.
There are 24 citations in total.

Details

Primary Language Turkish
Subjects Engineering
Journal Section Makaleler
Authors

Özlem Akar 0000-0001-6381-4907

Publication Date August 31, 2019
Published in Issue Year 2019

Cite

APA Akar, Ö. (2019). Göktürk-2 ve Worldview-2 Uydu Görüntüleri için Görüntü Keskinleştirme Yöntemlerinin Değerlendirilmesi. Erzincan University Journal of Science and Technology, 12(2), 874-885. https://doi.org/10.18185/erzifbed.495854