Contrast stretching based pansharpening by using weighted differential evolution algorithm

Civicioglu P., BEŞDOK E.

Expert Systems with Applications, vol.208, 2022 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 208
  • Publication Date: 2022
  • Doi Number: 10.1016/j.eswa.2022.118144
  • Journal Name: Expert Systems with Applications
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, PASCAL, Aerospace Database, Applied Science & Technology Source, Communication Abstracts, Computer & Applied Sciences, INSPEC, Metadex, Public Affairs Index, Civil Engineering Abstracts
  • Keywords: Linear contrast stretching, Pansharpening, Remote sensing, Image fusion, Weighted differential evolution algorithm, IMPULSIVE NOISE SUPPRESSION, SPECTRAL RESOLUTION IMAGES, FUSION TECHNIQUE, TRANSFORMATION, ENHANCEMENT, FILTER, ANFIS
  • Kayseri University Affiliated: No


© 2022 Elsevier LtdPansharpening techniques were developed to generate a super-resolution multispectral pansharpened image, PI, with the combination of a multispectral image carrying high-resolution spectral information with a panchromatic image carrying high resolution spatial information. For using energy resources and communication bandwidth efficiently, Earth Observation Satellites acquire multispectral images with lower spatial resolution when compared to panchromatic images. Contrast Stretching alters the range and statistical distribution of pixel values of an image to facilitate perception of image features and can be used to match histograms of distinct images. The Contrast Stretching Based Pansharpening method, CSP, has been introduced in this paper. CSP considers the pansharpening as a rescaling-based pixel-level image fusion problem in spatial domain. CSP uses the contrast stretching to generate two modified-multispectral images and one modified-panchromatic image, which are used to compute pansharpened image. The Weighted Differential Evolution Algorithm has been used to optimize the numerical values of internal parameters of CSP. The successes of CSP and 17 different pansharpening methods have been statistically compared by using three Test Image sets with different characteristics. Ten different image quality measures have been used in accuracy assessment analysis of the PIs generated by the related methods used in the Experiments. Statistical analysis exposed that CSP generates more pleasing PIs both quantitatively and qualitatively compared to the comparison methods employed in this paper.