IKONOS uydu görüntüleri ile yeni bir görüntü kaynaştırma yöntemi

Uzaktan algılamada, çok bantlı renkli uydu görüntülerinin konumsal çözünürlüklerinin aynı bölgeye ait daha iyi konumsal çözünürlüğe sahip pankromatik görüntülerle iyileştirilmesi işlemine görüntü kaynaştırma denilmektedir. Pankromatik görüntüdeki konumsal detay çok bantlı görüntüye aktarılırsa ve çok bantlı görüntüdeki spektral içerik orijinal görüntüdekine uygun olarak saklanırsa, görüntü kaynaştırma yöntemi başarılı olarak kabul edilir. Bu çalışmada, konumsal çözünürlük anlamında iyileştirilen ve spektral açıdan da geliştirilen çok bantlı görüntüler üretmeyi amaçlayan yeni bir görüntü kaynaştırma yöntemi önerilmektedir. Önerilen yöntem öncelikle bir ara görüntü oluşturmaktadır. Bu ara görüntü her bandı pankromatik görüntünün yüksek frekanslı kısmını tam olarak içeren görüntüler arasında orijinal çok bantlı görüntüye, tanımlanmış bir yarı içsel çarpıma göre, en yakın olan görüntüdür. Bu ara görüntü ve orijinal çok bantlı görüntünün dışbükey lineer toplamına belirli fonksiyonlar uygulanarak, kaynaşmış görüntü oluşturulmaktadır. Bu fonksiyonlar, orijinal görüntülerin yerel standart sapmalarına bağlıdır. Metodun performansını test etmek için, IKONOS uydu görüntüleri Brovey, IHS, PCA, dalgacık dönüşümü ve önerilen yöntem kullanılarak kaynaştırılmıştır. Görsel ve nicel değerlendirme sonuçları göstermektedir ki, önerilen yöntem hem konumsal hem de spektral olarak, dalgacık dönüşümü tabanlı yöntemler kadar iyi sonuçlar vermekte, kaynaştırılmış ürünlerde konumsal detayın iyileştirilmesi ve spektral içeriğin korunması birlikte ele alındığında ise daha iyi performans gösterdiği görülmektedir. Yöntem, daha uygun fonksiyonlar bulunarak daha da geliştirilme potansiyeline sahiptir.

A novel image fusion method using IKONOS satellite images

In satellite remote sensing, spatial resolutions of multispectral images over a particular region can be enhanced using better spatial resolution panchromatic images for the same region by a process called image fusion, or more generally data fusion. A fusion method is considered successful, if the spatial detail of the panchromatic image is transferred into the multispectral image and the spectral content of the original multispectral image is preserved in the fused product. This research proposes a novel image fusion algorithm which takes aim at producing both spatially enhanced and spectrally appealing fused multispectral images. In the proposed method, first an intermediary image is created using original panchromatic and multispectral images. This intermediary image contains the high frequency content of the panchromatic source image such that it is the one closest to the given multispectral source image upsampled by a natural semi inner product defined. The final fused image is obtained by applying a function which performs convex linear combination of the intermediary image and the upsampled multispectral image. The function used depends on the local standard deviations of the source images.To test the performance of the method, the images from IKONOS sensor are fused using the Brovey, IHS, PCA, wavelet transform based methods, and the proposed method. Both visual and quantitative evaluation results indicate that the proposed method yields to both spectrally and spatially appealing results as the wavelet transform based method, and it gives a better performance when both spatial detail enhancement and spectral content preservation in the fused products are considered. It is also obvious that the method has a potential to get better results if a better fitting, more complex function is found.

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  • Chang C., (1999), Spectral information divergence for hyperspectral image analysis, Proc. Geosci. Remote Sens. Symp.’un İçinde, Cilt.1, ss.509-511.
  • Chavez P.S., Kwarteng A.Y., (1989), Extracting spectral contrast in landsat thematic mapper image data using selective principal component analysis, Photogrammetric Engineering and Remote Sensing, 55(3), 339–348.
  • Chibani Y., Houacine A., (2002), The joint use of IHS transform and redundant wavelet decomposition for fusing multispectral and panchromatic images, International Journal of Remote Sensing, 23(18), 3821–3833.
  • Choi M., Kim R.Y., Nam M.R., Kim H.O., (2005), Fusion of multispectral and panchromatic satellite images using the curvelet transform, IEEE Geoscience and Remote Sensing Letters, 2(2), 136–140.
  • Choi M., Kim H., Cho N.I., Kim H.O., (2008), An improved intensity-hue-saturation method for ikonos image fusion, International Journal of Remote Sensing.
  • Cliché G., Bonn F., Teillet P., (1985), Integration of the SPOT panchromatic channel into its multispectral mode for image sharpness enhancement, Photogrammetric Engineering & Remote Sensing, 51(3), 311–316.
  • González-Audícana M., Saleta J.L., Catalan R.G., Garcia R., (2004), Fusion of multispectral and panchromatic images using improved IHS and PCA mergers based on wavelet decomposition, IEEE Transactions on Geoscience and Remote Sensing, 42(6), 1291-1299.
  • González-Audícana M., Otazu X., Fors O., Seco A., (2005), Comparison between Mallat’s and the a trous discrete wavelet transfom-based algorithms for the fusion of multispectral and panchromatic images, International Journal of Remote Sensing, 26(3), 595–614.
  • González-Audícana M., Otazu X., Fors O., Alvarez-Mozos J., (2006), A low computational-cost method to fuse IKONOS images using the spectral response function of its sensors, IEEE Transactions on Geoscience and Remote Sensing, 44(6), 1683- 1691.
  • Gonzalez R.C., Woods R.E, (1992), Digital Image Processing, Addison-Wesley, Reading, MA.
  • Güngör O., (2008), Multi Sensor Multi Resolution Image Fusion, Doktora Tezi, Purdue University.
  • Klemas V., (2011), Remote sensing techniques for studying coastal ecosystems: An overview, Journal of Coastal Research, 27(1), 2–17.
  • Knipling E.B., (1970), Physical and physiological basis for the reflectance of visible and near-infrared radiation from vegetation, Remote Sensing of Environment, 1, 155–159.
  • Loarie L.S., Joppa L.N., Pimm S.L., (2007), Satellites miss environmental priorities, Trends in Ecology & Evolution, 22(12), 630-632.
  • Pohl C., van Genderen J.L., (1998), Multisensor image fusion in remote sensing: Concepts, methods and applications, Int. J. Remote Sensing, 19(5), 823-854.
  • Schneider, K., Farge M., (2006), Wavelets: Mathematical theory, In: Encyclopedia of Mathematical Physics, (Françoise J.P., Naber G., Tsun T.S., Ed.), Academic Press, Oxford, ss.426-438.
  • Wald L., (1999), Some terms of reference in data fusion, IEEE Transaction on Geoscience and Remote Sensing, 37(3), 1190–1193.
  • Wald L., (2000), Quality of high resolution synthesized images: Is there a simple criterion? Proc. Int. Conf. Fusion Earth Data' nın İçinde.
  • Wang Z., Bovik A.C., Sheikh H.R., Simoncelli E.P., (2004), Image quality assessment: From error measurement to structural similarity, IEEE Trans. Image Process., 13(4), 600–612.
  • Yang J., Zhang J., Li H., Sun Y., Pu P., (2010), Pixel level fusion methods for remote sensing images: A current review, International Archives of Photogrammetry and Remote Sensing (IAPRS), XXXVIII(7B).
  • Zhang J., (2008), Generalized model for remotely sensed data pixel-level fusion, The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XXXVII(B7), 1051–1056.
  • Zhang J., (2010), Multi-source remote sensing data fusion: Status and trends, International Journal of Image and Data Fusion, 1, 5–24.
  • Zhang, Y., (2002), Problems in the fusion of commercial high resolution satellite images as well as Landsat 7 images and initial solutions, International Archives of Photogrammetry and Remote Sensing (IAPRS), 34(4).
  • Zhou J., Civco D.L., Silander J.A., (1998), A wavelet transform method to merge Landsat TM and SPOT panchromatic data, International Journal of Remote Sensing, 19(4), 743-757.
  • Url-1, HYPERION Spectral Coverage, USGS EO-I Website, http:// eo1.usgs.gov/sensors/hyperioncoverage, [Erişim March 2012 ].
  • Url-2, Trabzon Province, English Wikipedia, en.wikipedia.org/ wiki/Trabzon_Province, [Erişim May 2012].