Background and Objectives: Pan-sharpening algorithms integrate the spectral capabilities of the multispectral imagery with the spatial details of the panchromatic one to obtain a product with confident spectral and spatial resolutions. Due to the large diversities in the utilized pan-sharpening algorithms, occurring spatial and spectral deviations in their results should be recognized by performing the quantitative assessment analysis.
Methods: In this research, the pan-sharpened images from PCA, IHS, and Gram-Schmidt transformation based algorithms are evaluated for the multi-spectral and panchromatic images fusion of Landsat-8 OLI sensor (medium scale resolution satellite) and WorldView-2 (high-resolution satellite). Quantitative analysis is performed on the pan-sharpened products based on the Per-Pixel Deviation (PPD) measure for spectral deviation analysis and high-pass filter and edge extraction measures for analyzing the spatial correlations. Moreover, entropy and standard deviation quantitative evaluation measures are also utilized based on the pan-sharpened image content.
Results: Quantitative analysis represents that increasing the spatial resolution of the utilized remote sensing data has direct impacts on the spectral, spatial, and content-based characteristics of the generated Pan-sharpened products. Gram-Schmidt transformation based pan-sharpening method has the least spectral deviations in both WorldView-2 and Landsat-8 satellite images. But, the amount of spectral, spatial and content-based quantitative measures of PCA and IHS are changing with various spatial resolutions.
Conclusion: it can be said that Gram-Schmidt pan-sharpening method has the best performance in both medium-scale and high-resolution data sets based on the spectral, spatial, and content quantitative evaluation results. The IHS pan-sharpening method has better performance than the PCA method in Landsat-8 OLI data. But, by increasing the spatial resolution of the data, PCA generates pan-sharpened products with better spectral, spatial, and content based quantitative evaluation results.
This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit: http://creativecommons.org/licenses/by/4.0/
JECEI Publisher remains neutral with regard to jurisdictional claims in published maps and institutional afflictions.
Shahid Rajaee Teacher Training University