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Qualitative assessment of geostatistical and non-geostatistical fusion techniques: a case study on landsat 8 images

  • Harpreet Singh
  • Prabhakar Alok VermaEmail author
  • Sameer Saran
Article

Abstract

Broadly satellite images are available in two categories (1) high spectral resolution but less spatial resolution (2) high spatial resolution but less spectral resolution. But in certain applications, images with high spatial as well as high spectral resolution are required. To meet such kind of requirement, Image fusion is widely accepted and increasingly being used. In this study satellite image fusion is done using geostatistical methods (cokriging, regression kriging) and non-geostatistical methods (intensity hue saturation, principal component analysis). The study is focused on performing qualitative assessment of selected image fusion techniques. In this study, the primary variable is RGB bands of Landsat 8 Operational Land Imager (OLI) and the panchromatic band is chosen as the second variable. The output of these selected methods is compared to access spectral and spatial quality. Spectral quality is accessed by finding the correlation between the primary variable and the output, however spatial quality is accessed via texture analysis method named entropy. Overall assessment of loss of correlation, luminance distortion, and contrast distortion is done using Image quality index. Correlation index of regression kriging and PCA are comparable whereas entropy and image quality index of fused output is highest in case of regression kriging. Hence regression kriging can be concluded as the best fusion technique out of the compared techniques.

Keywords

Geostatistical Cokriging Regression kriging Kriging Landsat OLI PCA IHS 

Notes

Compliance with ethical standards

Conflict of interest

On behalf of all authors, the corresponding author states that there is no conflict of interest.

Supplementary material

41324_2018_235_MOESM1_ESM.pdf (102 kb)
Supplementary material 1 (PDF 101 kb)

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Copyright information

© Korean Spatial Information Society 2019

Authors and Affiliations

  1. 1.Geoinformatics DepartmentIndian Institute of Remote SensingDehradunIndia

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