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Çok bantlı görüntülerde pan-keskinleştirme üzerine bir inceleme

Yıl 2021, Cilt: 11 Sayı: 4, 1340 - 1357, 15.10.2021
https://doi.org/10.17714/gumusfenbil.972014

Öz

Uzaktan algılama uyduları, algılayıcılarındaki teknik kısıtlamalardan dolayı hem uzamsal detay kalitesi hem de spektral kalitesi yüksek görüntüler üretememektedir. Bu durum, kullanıcıları yüksek uzamsal çözünürlüklü çok bantlı görüntüler elde edebilme konusunda yeni arayışlar içine sokmaktadır. Pan-keskinleştirme işlemi bu probleme etkin bir çözüm sunmaktadır. Pan-keskinleştirme, yüksek uzamsal çözünürlüklü bir pankromatik görüntünün uzamsal detaylarının, yüksek spektral çözünürlüklü çok bantlı bir görüntüye aktarılarak uzamsal çözünürlüğü yüksek çok bantlı bir görüntü üretilmesi işlemidir. Literatürde pan-keskinleştirme için oldukça fazla sayıda yöntem geliştirilmiştir. Bu yöntemlerin her birinin kendine has avantaj ve dezavantajları vardır. Bu durum, kullanıcıları hangi durumda hangi yöntemin kullanılması gerektiği hususunda tereddüte düşürmektedir. Genel amacı, literatürdeki çeşitli konvansiyonel ve gelişmiş pan-keskinleştirme yöntemleri hakkında teorik bilgiler vermek ve bu yöntemlerin hangi durumlarda kullanılabileceği hususunda analistlere yol göstermek olan bu çalışmanın, pan-keskinleştirme hakkında iyi bir rehber olacağı kanaatindeyiz. Çalışmada, ayrıca pan-keskinleştirilmiş görüntülerin spektral ve uzamsal detay kalitelerinin görsel ve sayısal olarak nasıl değerlendirilebileceği hakkında da bilgiler verilmiştir.

Kaynakça

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A review on pansharpening of multispectral images

Yıl 2021, Cilt: 11 Sayı: 4, 1340 - 1357, 15.10.2021
https://doi.org/10.17714/gumusfenbil.972014

Öz

Remote sensing satellites cannot produce images of high spatial detail quality and spectral quality due to technical limitations in their sensors, which forces users to find alternative ways to produce such images. Pan-sharpening offers an effective solution to this problem. Pan-sharpening aims to transfer the spatial details of a high-resolution panchromatic image into a high spectral resolution image, producing a multispectral image of high spatial resolution. A wide variety of pansharpening methods have been proposed in the litreture. Each pansharpening method has its own advantages and disadvantages. This situation makes users hesitant about which method should be used under what situation. We believe that this study, whose primary objective is to provide theoretical information about various conventional and state-of-the-art pan-sharpening methods in the literature, and to guide the analysts as to which pansharpening methods should be used under what circumstances, will be a good pan-sharpening guide. This study also provides information on how the spatial and spectral quality of pan-sharpened images may be evaluated qualitatively and quantitatively.

Kaynakça

  • Addesso, P., Conte, R., Longo, M., Restaino, R. and Vivone, G. (2012). A pansharpening algorithm based on genetic optimization of Morphological Filters. International Geoscience and Remote Sensing Symposium, Munich, Germany.
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  • Serifoglu Yilmaz, C., Yilmaz, V. and Güngör, O. (2020). On the use of the SOS metaheuristic algorithm in hybrid image fusion methods to achieve optimum spectral fidelity. International Journal of Remote Sensing, 41(10), 3993-4021. https://doi.org/10.1080/01431161.2019.1711244.
  • Serifoglu Yilmaz, C., Yilmaz, V., Gungor, O. and Shan, J. (2019). Metaheuristic pansharpening based on symbiotic organisms search optimization. ISPRS Journal of Photogrammetry and Remote Sensing, 158, 167-187. https://doi.org/10.1016/j.isprsjprs.2019.10.014.
  • Shi, Y. (2018). A new pansharpening algorithm using morphological lifting transform. IEEE 3rd International Conference on Signal and Image Processing (ICSIP) (pp. 250-254), Shenzhen, China. https://doi.org/10.1109/SIPROCESS.2018.8600445.
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  • Strait, M., Rahmani, S. and Markurjev, D. (2008). Evaluation of Pan-Sharpening Methods. UCLA Department of Mathematics.
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  • Vivone, G., Alparone, L., Chanussot, J., Dalla Mura, M., Garzelli, A., Licciardi, G. A., Restaino, R. and Wald, L. (2015). A critical comparison among pansharpening algorithms. IEEE Transactions on Geoscience and Remote Sensing, 53(5), 2565-2586. https://doi.org/10.1109/TGRS.2014.2361734.
  • Vivone, G., Restaino, R., Dalla Mura, M., Licciardi, G. and Chanussot, J. (2013). Contrast and error-based fusion schemes for multispectral image pansharpening. IEEE Geoscience and Remote Sensing Letters, 11(5), 930-934. https://doi.org/10.1109/LGRS.2013.2281996.
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  • Yilmaz, V., Serifoglu Yilmaz, C., Güngör, O. and Shan, J. (2020). A genetic algorithm solution to the gram-schmidt image fusion. International Journal of Remote Sensing, 41(4), 1458-1485. https://doi.org/10.1080/01431161.2019.1667553.
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Toplam 89 adet kaynakça vardır.

Ayrıntılar

Birincil Dil Türkçe
Konular Mühendislik
Bölüm Derlemeler
Yazarlar

Çiğdem Şerifoğlu Yılmaz 0000-0002-9738-5124

Volkan Yılmaz 0000-0003-0685-8369

Oğuz Güngör 0000-0002-3280-5466

Yayımlanma Tarihi 15 Ekim 2021
Gönderilme Tarihi 15 Temmuz 2021
Kabul Tarihi 2 Ekim 2021
Yayımlandığı Sayı Yıl 2021 Cilt: 11 Sayı: 4

Kaynak Göster

APA Şerifoğlu Yılmaz, Ç., Yılmaz, V., & Güngör, O. (2021). Çok bantlı görüntülerde pan-keskinleştirme üzerine bir inceleme. Gümüşhane Üniversitesi Fen Bilimleri Dergisi, 11(4), 1340-1357. https://doi.org/10.17714/gumusfenbil.972014