Araştırma Makalesi
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Image Fusion with Metaheuristic Cuckoo Search Algorithm

Yıl 2020, Cilt: 1 Sayı: 1, 1 - 12, 31.03.2020

Öz

Component substitution-based fusion methods are among the most widespread image fusion methods in the literature. Despite the fact that these methods are very successful in enhancing the spatial detail quality, they tend to deteriorate the spectral quality of the input multispectral images. The main reason for this is that they are not so successful in optimizing the intensity component produced from the input multispectral bands. In this study, an image fusion method which utilizes metaheuristic cuckoo search algorithm was proposed to optimize the intensity component used in fusion process. The proposed method was applied on the Gram-Schmidt (GS) method, one of the most widely-used component substitution-based image fusion methods. The colour preservation performance of the proposed method was qualitatively and quantitatively compared not only against that of the High-Pass Filtering (HPF) method, but also against those of popular component substitution-based methods Multiplicative (MCV), Brovey (BRV), Principal Component Analysis (PCA), Ehlers (EHL), Modified Intensity-Hue-Saturation (MIHS), Synthetic Variable Ratio (SVR) and original GS in two test sites. The results showed that the proposed method was successful in optimizing the intensity component and therefore preserved the colour content of the input multispectral image more successfully than other methods used.

Kaynakça

  • Alparone, L., Aiazzi, B., Baronti, S., Garzelli, A., & Nencini, F. (2006). Information-theoretic image fusion assessment without reference. ESA-EUSC 2006.
  • Bir Yazılımcının Günlüğü, (2019, Aralık 20). https://biryazilimciningunlugu.wordpress.com/2017/05/16/metasezgisel algoritmalar/.
  • Cheng, M. Y., & Prayogo, D. (2014). Symbiotic organisms search: a new metaheuristic optimization algorithm. Computers & Structures, 139, 98-112. doi:10.1016/j.compstruc.2014.03.007.
  • Dorigo, M., & Stützle, T. (2004). Ant Colony Optimization. Bradford Company.
  • Ehlers, M. (2004, October). Spectral characteristics preserving image fusion based on Fourier domain filtering. In Remote sensing for environmental monitoring, GIS applications, and geology IV (Vol. 5574, pp. 1-13). International Society for Optics and Photonics. doi:10.1117/12.565160.
  • Garzelli, A., & Nencini, F. (2006a, July). Fusion of panchromatic and multispectral images by genetic algorithms. In 2006 IEEE International Symposium on Geoscience and Remote Sensing (pp. 3810-3813). IEEE. doi:10.1109/IGARSS.2006.976.
  • Garzelli, A., & Nencini, F. (2006b). PAN‐sharpening of very high resolution multispectral images using genetic algorithms. International Journal of Remote Sensing, 27(15), 3273-3292. doi:10.1080/01431160600554991.
  • Garzelli, A., Nencini, F., & Capobianco, L. (2007). Optimal MMSE pan sharpening of very high resolution multispectral images. IEEE Transactions on Geoscience and Remote Sensing, 46(1), 228-236. doi:10.1109/TGRS.2007.907604.
  • Ghahremani, M., Liu, Y., Yuen, P., & Behera, A. (2019). Remote sensing image fusion via compressive sensing. ISPRS journal of photogrammetry and remote sensing, 152, 34-48. doi:10.1016/j.isprsjprs.2019.04.001.
  • Ghassemian, H. (2016). A review of remote sensing image fusion methods. Information Fusion, 32, 75-89. doi:10.1016/j.inffus.2016.03.003.
  • Gogineni, R., & Chaturvedi, A. (2018). Sparsity inspired pan-sharpening technique using multi-scale learned dictionary. ISPRS Journal of Photogrammetry and Remote Sensing, 146, 360-372. doi:10.1016/j.isprsjprs.2018.10.009.
  • Holland, H. (1975). Adaptation in Natural and Artificial Systems. Ann Arbor: University of Michigan Press.
  • Jagalingam, P., & Hegde, A. V. (2015). A review of quality metrics for fused image. Aquatic Procedia, 4(Icwrcoe), 133-142. doi:10.1016/j.aqpro.2015.02.019.
  • Karaboga, D., & Basturk, B. (2007). A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm. Journal of global optimization, 39(3), 459-471. doi: 0.1007/s10898-007-9149-x.
  • Klonus, S., & Ehlers, M. (2007). Image fusion using the Ehlers spectral characteristics preservation algorithm. GIScience & Remote Sensing, 44(2), 93-116. doi:10.2747/1548-1603.44.2.93.
  • Kwarteng, P., & Chavez, A. (1989). Extracting spectral contrast in Landsat Thematic Mapper image data using selective principal component analysis. Photogrammetric Engineering & Remote Sensing, 55(339-348), 1.
  • Laben, C. A., & Brower, B. V. (2000). U.S. Patent No. 6,011,875. Washington, DC: U.S. Patent and Trademark Office.
  • Lari, S. N., & Yazdi, M. (2016). Improved IHS pan-sharpening method based on adaptive injection of à trous wavelet decomposition. International Journal of Signal Processing, Image Processing and Pattern Recognition, 9(3), 291-308. doi:10.14257/ijsip.2016.9.3.26.
  • Liu, J., Huang, J., Liu, S., Li, H., Zhou, Q., & Liu, J. (2015). Human visual system consistent quality assessment for remote sensing image fusion. ISPRS Journal of Photogrammetry and Remote Sensing, 105, 79-90. doi:10.1016/j.isprsjprs.2014.12.018.
  • Mareli, M., & Twala, B. (2018). An adaptive Cuckoo search algorithm for optimisation. Applied computing and informatics, 14(2), 107-115. doi:10.1016/j.aci.2017.09.001.
  • Masoudi, R., & Kabiri, P. (2014). New intensity-hue-saturation pan-sharpening method based on texture analysis and genetic algorithm-adaption. Journal of Applied Remote Sensing, 8(1), 083640. doi:10.1117/1.JRS.8.083640.
  • Maurer, T. (2013). How to pan-sharpen images using the Gram-Schmidt pan-sharpen method-a recipe. International archives of the photogrammetry, remote sensing and spatial information sciences, 1, W1, 239-244.
  • Niazi, M., N., S., Mokhtar Zade, M., & Saeed Zadeh, F. (2016). A Novel IHS-GA Fusion Method Based on Enhancement Vegetated Area. Journal of Geomatics Science and Technology, 6(1), 235-248.
  • Pham, D. T., Ghanbarzadeh, A., Koç, E., Otri, S., Rahim, S., & Zaidi, M. (2006). The bees algorithm—a novel tool for complex optimisation problems. In Intelligent production machines and systems (pp. 454-459). Elsevier Science Ltd.
  • Pohl, C., & van Genderen, J. (2016). Remote sensing image fusion: A practical guide. Crc Press.
  • Serifoglu Yilmaz, C., Yilmaz, V., Gungor, O., & Shan, J. (2019). Metaheuristic pansharpening based on symbiotic organisms search optimization. ISPRS Journal of Photogrammetry and Remote Sensing, 158, 167-187. doi:10.1016/j.isprsjprs.2019.10.014.
  • Wald, L. (2002). Data fusion: definitions and architectures: fusion of images of different spatial resolutions. Presses des MINES.
  • Wald, L., Ranchin, T., & Mangolini, M. (1997). Fusion of satellite images of different spatial resolutions: Assessing the quality of resulting images. Photogrammetric Engineering & Remote Sensing, 63, 691-699.
  • Wang, Z., & Bovik, A. C. (2002). A universal image quality index. IEEE signal processing letters, 9(3), 81-84. doi:10.1109/97.995823.
  • Wang, Z., & Li, Q. (2011). Information content weighting for perceptual image quality assessment. IEEE Transactions on image processing, 20(5), 1185-1198. doi:10.1109/TIP.2010.2092435.
  • Wang, Z., Bovik, A. C., Sheikh, H. R., & Simoncelli, E. P. (2004). Image quality assessment: from error visibility to structural similarity. IEEE Transactions on Image Processing, 13(4), 600-612. doi:10.1109/TIP.2003.819861.
  • Xing, Y., Wang, M., Yang, S., & Jiao, L. (2018). Pan-sharpening via deep metric learning. ISPRS Journal of Photogrammetry and Remote Sensing, 145, 165-183. doi: 10.1016/j.isprsjprs.2018.01.016.
  • Yang, X. S. (2009, October). Firefly algorithms for multimodal optimization. In International symposium on stochastic algorithms (pp. 169-178). Springer, Berlin, Heidelberg.
  • Yang, X. S. (2014). Nature-inspired optimization algorithms. 1st Edition, Elsevier.
  • Yang, X. S., & Deb, S. (2009, December). Cuckoo search via Lévy flights. In 2009 World congress on nature & biologically inspired computing (NaBIC) (pp. 210-214).
  • Yang, X. S., & Deb, S. (2010). Engineering optimisation by cuckoo search. International Journal of Mathematical Modelling and Numerical Optimisation, 1(4), 330-343.
  • Yilmaz, V., & Gungor, O. (2016). Determining the optimum image fusion method for better interpretation of the surface of the Earth. Norsk Geografisk Tidsskrift-Norwegian Journal of Geography, 70(2), 69-81. doi: 10.1080/00291951.2015.1126761.
  • Yilmaz, V., Serifoglu Yilmaz, C., & Gungor, O. (2019). Genetic algorithm-based synthetic variable ratio ımage fusion. Geocarto International, (just-accepted), 1-17. doi: 10.1080/10106049.2019.1629649.
  • Yilmaz, V., Serifoglu Yilmaz, C., Güngör, O., & Shan, J. (2020). A genetic algorithm solution to the gram-schmidt image fusion. International Journal of Remote Sensing, 41(4), 1458-1485. doi: 10.1080/01431161.2019.1667553.
  • Zeybekoğlu, U. (2017). Metasezgisel optimizasyon yöntemlerin performanslarının basit bir su dağıtım şebekesi kullanılarak araştırılması, Karadeniz Fen Bilimleri Dergisi, 7(2), 57-67. doi: 10.31466/kfbd.338197.

Metasezgisel Guguk Kuşu Arama Algoritması ile Görüntü Kaynaştırma

Yıl 2020, Cilt: 1 Sayı: 1, 1 - 12, 31.03.2020

Öz

Bileşen değişimi tabanlı görüntü kaynaştırma yöntemleri literatürde en fazla kullanılan görüntü kaynaştırma yöntemleri arasındadır. Bu yöntemler konumsal detayları iyileştirmedeki başarılarına rağmen girdi çok bantlı görüntülerin renk yapısını bozma eğilimindedirler. Bu durumun temel nedeni bu yöntemlerin girdi çok bantlı görüntülerin bantlarından elde ettikleri yoğunluk bileşenini optimize edememeleridir. Bu çalışmada, yoğunluk bileşeninin optimize edilebilmesi için metasezgisel guguk kuşu arama algoritmasından yararlanan bir görüntü kaynaştırma yöntemi önerilmiştir. Önerilen yaklaşım en yaygın kullanılan bileşen değişimi tabanlı kaynaştırma yöntemlerinden biri olan Gram-Schmidt (GS) yöntemi üzerinde uygulanmıştır. Önerilen yöntemin renk koruma performansı Yüksek Geçirgen Filtreleme (HPF) yönteminin yanı sıra popüler bileşen değişimi tabanlı yöntemlerden olan Multiplicative (MCV), Brovey (BRV), Temel Bileşenler Analizi (PCA), Ehlers (EHL), Modifiye edilmiş Intensity-Hue-Saturation (MIHS), Synthetic Variable Ratio (SVR) ve orijinal GS yöntemlerinin renk koruma performansları ile iki test bölgesinde niteliksel ve niceliksel olarak karşılaştırılmıştır. Elde edilen sonuçlar önerilen yöntemin yoğunluk bileşenini başarılı bir şekilde optimize ettiğini ve bu nedenle de girdi çok bantlı görüntünün renk içeriğini kullanılan diğer yöntemlere oranla daha başarılı bir şekilde koruduğunu göstermiştir.

Kaynakça

  • Alparone, L., Aiazzi, B., Baronti, S., Garzelli, A., & Nencini, F. (2006). Information-theoretic image fusion assessment without reference. ESA-EUSC 2006.
  • Bir Yazılımcının Günlüğü, (2019, Aralık 20). https://biryazilimciningunlugu.wordpress.com/2017/05/16/metasezgisel algoritmalar/.
  • Cheng, M. Y., & Prayogo, D. (2014). Symbiotic organisms search: a new metaheuristic optimization algorithm. Computers & Structures, 139, 98-112. doi:10.1016/j.compstruc.2014.03.007.
  • Dorigo, M., & Stützle, T. (2004). Ant Colony Optimization. Bradford Company.
  • Ehlers, M. (2004, October). Spectral characteristics preserving image fusion based on Fourier domain filtering. In Remote sensing for environmental monitoring, GIS applications, and geology IV (Vol. 5574, pp. 1-13). International Society for Optics and Photonics. doi:10.1117/12.565160.
  • Garzelli, A., & Nencini, F. (2006a, July). Fusion of panchromatic and multispectral images by genetic algorithms. In 2006 IEEE International Symposium on Geoscience and Remote Sensing (pp. 3810-3813). IEEE. doi:10.1109/IGARSS.2006.976.
  • Garzelli, A., & Nencini, F. (2006b). PAN‐sharpening of very high resolution multispectral images using genetic algorithms. International Journal of Remote Sensing, 27(15), 3273-3292. doi:10.1080/01431160600554991.
  • Garzelli, A., Nencini, F., & Capobianco, L. (2007). Optimal MMSE pan sharpening of very high resolution multispectral images. IEEE Transactions on Geoscience and Remote Sensing, 46(1), 228-236. doi:10.1109/TGRS.2007.907604.
  • Ghahremani, M., Liu, Y., Yuen, P., & Behera, A. (2019). Remote sensing image fusion via compressive sensing. ISPRS journal of photogrammetry and remote sensing, 152, 34-48. doi:10.1016/j.isprsjprs.2019.04.001.
  • Ghassemian, H. (2016). A review of remote sensing image fusion methods. Information Fusion, 32, 75-89. doi:10.1016/j.inffus.2016.03.003.
  • Gogineni, R., & Chaturvedi, A. (2018). Sparsity inspired pan-sharpening technique using multi-scale learned dictionary. ISPRS Journal of Photogrammetry and Remote Sensing, 146, 360-372. doi:10.1016/j.isprsjprs.2018.10.009.
  • Holland, H. (1975). Adaptation in Natural and Artificial Systems. Ann Arbor: University of Michigan Press.
  • Jagalingam, P., & Hegde, A. V. (2015). A review of quality metrics for fused image. Aquatic Procedia, 4(Icwrcoe), 133-142. doi:10.1016/j.aqpro.2015.02.019.
  • Karaboga, D., & Basturk, B. (2007). A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm. Journal of global optimization, 39(3), 459-471. doi: 0.1007/s10898-007-9149-x.
  • Klonus, S., & Ehlers, M. (2007). Image fusion using the Ehlers spectral characteristics preservation algorithm. GIScience & Remote Sensing, 44(2), 93-116. doi:10.2747/1548-1603.44.2.93.
  • Kwarteng, P., & Chavez, A. (1989). Extracting spectral contrast in Landsat Thematic Mapper image data using selective principal component analysis. Photogrammetric Engineering & Remote Sensing, 55(339-348), 1.
  • Laben, C. A., & Brower, B. V. (2000). U.S. Patent No. 6,011,875. Washington, DC: U.S. Patent and Trademark Office.
  • Lari, S. N., & Yazdi, M. (2016). Improved IHS pan-sharpening method based on adaptive injection of à trous wavelet decomposition. International Journal of Signal Processing, Image Processing and Pattern Recognition, 9(3), 291-308. doi:10.14257/ijsip.2016.9.3.26.
  • Liu, J., Huang, J., Liu, S., Li, H., Zhou, Q., & Liu, J. (2015). Human visual system consistent quality assessment for remote sensing image fusion. ISPRS Journal of Photogrammetry and Remote Sensing, 105, 79-90. doi:10.1016/j.isprsjprs.2014.12.018.
  • Mareli, M., & Twala, B. (2018). An adaptive Cuckoo search algorithm for optimisation. Applied computing and informatics, 14(2), 107-115. doi:10.1016/j.aci.2017.09.001.
  • Masoudi, R., & Kabiri, P. (2014). New intensity-hue-saturation pan-sharpening method based on texture analysis and genetic algorithm-adaption. Journal of Applied Remote Sensing, 8(1), 083640. doi:10.1117/1.JRS.8.083640.
  • Maurer, T. (2013). How to pan-sharpen images using the Gram-Schmidt pan-sharpen method-a recipe. International archives of the photogrammetry, remote sensing and spatial information sciences, 1, W1, 239-244.
  • Niazi, M., N., S., Mokhtar Zade, M., & Saeed Zadeh, F. (2016). A Novel IHS-GA Fusion Method Based on Enhancement Vegetated Area. Journal of Geomatics Science and Technology, 6(1), 235-248.
  • Pham, D. T., Ghanbarzadeh, A., Koç, E., Otri, S., Rahim, S., & Zaidi, M. (2006). The bees algorithm—a novel tool for complex optimisation problems. In Intelligent production machines and systems (pp. 454-459). Elsevier Science Ltd.
  • Pohl, C., & van Genderen, J. (2016). Remote sensing image fusion: A practical guide. Crc Press.
  • Serifoglu Yilmaz, C., Yilmaz, V., Gungor, O., & Shan, J. (2019). Metaheuristic pansharpening based on symbiotic organisms search optimization. ISPRS Journal of Photogrammetry and Remote Sensing, 158, 167-187. doi:10.1016/j.isprsjprs.2019.10.014.
  • Wald, L. (2002). Data fusion: definitions and architectures: fusion of images of different spatial resolutions. Presses des MINES.
  • Wald, L., Ranchin, T., & Mangolini, M. (1997). Fusion of satellite images of different spatial resolutions: Assessing the quality of resulting images. Photogrammetric Engineering & Remote Sensing, 63, 691-699.
  • Wang, Z., & Bovik, A. C. (2002). A universal image quality index. IEEE signal processing letters, 9(3), 81-84. doi:10.1109/97.995823.
  • Wang, Z., & Li, Q. (2011). Information content weighting for perceptual image quality assessment. IEEE Transactions on image processing, 20(5), 1185-1198. doi:10.1109/TIP.2010.2092435.
  • Wang, Z., Bovik, A. C., Sheikh, H. R., & Simoncelli, E. P. (2004). Image quality assessment: from error visibility to structural similarity. IEEE Transactions on Image Processing, 13(4), 600-612. doi:10.1109/TIP.2003.819861.
  • Xing, Y., Wang, M., Yang, S., & Jiao, L. (2018). Pan-sharpening via deep metric learning. ISPRS Journal of Photogrammetry and Remote Sensing, 145, 165-183. doi: 10.1016/j.isprsjprs.2018.01.016.
  • Yang, X. S. (2009, October). Firefly algorithms for multimodal optimization. In International symposium on stochastic algorithms (pp. 169-178). Springer, Berlin, Heidelberg.
  • Yang, X. S. (2014). Nature-inspired optimization algorithms. 1st Edition, Elsevier.
  • Yang, X. S., & Deb, S. (2009, December). Cuckoo search via Lévy flights. In 2009 World congress on nature & biologically inspired computing (NaBIC) (pp. 210-214).
  • Yang, X. S., & Deb, S. (2010). Engineering optimisation by cuckoo search. International Journal of Mathematical Modelling and Numerical Optimisation, 1(4), 330-343.
  • Yilmaz, V., & Gungor, O. (2016). Determining the optimum image fusion method for better interpretation of the surface of the Earth. Norsk Geografisk Tidsskrift-Norwegian Journal of Geography, 70(2), 69-81. doi: 10.1080/00291951.2015.1126761.
  • Yilmaz, V., Serifoglu Yilmaz, C., & Gungor, O. (2019). Genetic algorithm-based synthetic variable ratio ımage fusion. Geocarto International, (just-accepted), 1-17. doi: 10.1080/10106049.2019.1629649.
  • Yilmaz, V., Serifoglu Yilmaz, C., Güngör, O., & Shan, J. (2020). A genetic algorithm solution to the gram-schmidt image fusion. International Journal of Remote Sensing, 41(4), 1458-1485. doi: 10.1080/01431161.2019.1667553.
  • Zeybekoğlu, U. (2017). Metasezgisel optimizasyon yöntemlerin performanslarının basit bir su dağıtım şebekesi kullanılarak araştırılması, Karadeniz Fen Bilimleri Dergisi, 7(2), 57-67. doi: 10.31466/kfbd.338197.
Toplam 40 adet kaynakça vardır.

Ayrıntılar

Birincil Dil Türkçe
Konular Fotogrametri ve Uzaktan Algılama
Bölüm Araştırma Makaleleri
Yazarlar

Volkan Yılmaz 0000-0003-0685-8369

Yayımlanma Tarihi 31 Mart 2020
Gönderilme Tarihi 28 Aralık 2019
Kabul Tarihi 11 Şubat 2020
Yayımlandığı Sayı Yıl 2020 Cilt: 1 Sayı: 1

Kaynak Göster

APA Yılmaz, V. (2020). Metasezgisel Guguk Kuşu Arama Algoritması ile Görüntü Kaynaştırma. Türk Uzaktan Algılama Ve CBS Dergisi, 1(1), 1-12.

Creative Commons License
Turkish Journal of Remote Sensing and GIS (Türk Uzaktan Algılama ve CBS Dergisi), Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License ile lisanlanmıştır.