Varying weighted spatial quality assessment for high resolution satellite image pan-sharpening

S. Mehravar, F. Dadrass Javan*, F. Samadzadegan, A. Toosi, A. Moghimi, R. Khatami, A. Stein

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

1 Citation (Scopus)
51 Downloads (Pure)

Abstract

This paper focuses on spatial quality assessment of pan-sharpened imagery that contains valuable information of input images. Its aim is to show that fusion functions respond differently to different types of landscapes. It compares a quality assessment of an object-level procedure with that of a conventional pixel-level-based procedure which assigns uniform quality scores to all image pixels of pan-sharpened images. To do so, after performing a series of pan-sharpening evaluations, a weighted procedure for spatial quality assessments of pan-sharpening products, allocating spatially varying weight factors to the image pixels proportional to their level of spatial information content is proposed. All experiments are performed using five high-resolution image datasets using fusion products produced by three common pan-sharpening algorithms. The datasets are acquired from WorldView-2, QuickBird, and IKONOS. Experimental results show that the spatial distortion of fused images for the class vegetation cover exceeds that of man-made structures, reaching more than 4% in some cases. Our procedure can preclude illogical fidelity estimations occurring when pan-sharpened images contain different land covers. Since particular image structures are of high importance in remote sensing applications, our procedure provides a purpose-oriented estimation of the spatial quality for pan-sharpened images in comparison with conventional procedures.

Original languageEnglish
Pages (from-to)44-70
Number of pages27
JournalInternational journal of image and data fusion
Volume13
Issue number1
Early online date3 May 2021
DOIs
Publication statusPublished - 2 Jan 2022

Keywords

  • Spatial quality
  • impact of landscape
  • object-level evaluation
  • pan-sharpened satellite imagery
  • weighted average
  • UT-Hybrid-D

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