An Approach for the Pan Sharpening of Very High Resolution Satellite Images Using a CIELab Color Based Component Substitution Algorithm

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Rahimzadeganasl A., Algancı U., Göksel Ç.

APPLIED SCIENCES-BASEL, cilt.9, sa.23, 2019 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 9 Sayı: 23
  • Basım Tarihi: 2019
  • Doi Numarası: 10.3390/app9235234
  • Dergi Adı: APPLIED SCIENCES-BASEL
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Anahtar Kelimeler: CIELab, component Substitution, Pan sharpening, Pleiades VHR Image, MULTIVARIATE REGRESSION, MULTISPECTRAL IMAGES, SPATIAL-RESOLUTION, TRANSFORM METHOD, DATA FUSION, QUALITY, SPACE, MS
  • İstanbul Teknik Üniversitesi Adresli: Evet

Özet

Recent very high spatial resolution (VHR) remote sensing satellites provide high spatial resolution panchromatic (Pan) images in addition to multispectral (MS) images. The pan sharpening process has a critical role in image processing tasks and geospatial information extraction from satellite images. In this research, CIELab color based component substitution Pan sharpening algorithm was proposed for Pan sharpening of the Pleiades VHR images. The proposed method was compared with the state-of-the-art Pan sharpening methods, such as IHS, EHLERS, NNDiffuse and GIHS. The selected study region included ten test sites, each of them representing complex landscapes with various land categories, to evaluate the performance of Pan sharpening methods in varying land surface characteristics. The spatial and spectral performance of the Pan sharpening methods were evaluated by eleven accuracy metrics and visual interpretation. The results of the evaluation indicated that proposed CIELab color-based method reached promising results and improved the spectral and spatial information preservation.