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Proceedings Paper • Open Access

Implementing and validating of pan-sharpening algorithms in open-source software
Author(s): Paúl Pesántez-Cobos; Fulgencio Cánovas-García; Francisco Alonso-Sarría

Paper Abstract

Several approaches have been used in remote sensing to integrate images with different spectral and spatial resolutions in order to obtain fused enhanced images. The objective of this research is three-fold. To implement in R three image fusion techniques (High Pass Filter, Principal Component Analysis and Gram-Schmidt); to apply these techniques to merging multispectral and panchromatic images from five different images with different spatial resolutions; finally, to evaluate the results using the universal image quality index (Q index) and the ERGAS index. As regards qualitative analysis, Landsat-7 and Landsat-8 show greater colour distortion with the three pansharpening methods, although the results for the other images were better. Q index revealed that HPF fusion performs better for the QuickBird, IKONOS and Landsat-7 images, followed by GS fusion; whereas in the case of Landsat-8 and Natmur-08 images, the results were more even. Regarding the ERGAS spatial index, the ACP algorithm performed better for the QuickBird, IKONOS, Landsat-7 and Natmur-08 images, followed closely by the GS algorithm. Only for the Landsat-8 image did, the GS fusion present the best result. In the evaluation of spectral components, HPF results tended to be better and ACP results worse, the opposite was the case with the spatial components. Better quantitative results are obtained in Landsat-7 and Landsat-8 images with the three fusion methods than with the QuickBird, IKONOS and Natmur-08 images. This contrasts with the qualitative evaluation reflecting the importance of splitting the two evaluation approaches (qualitative and quantitative). Significant disagreement may arise when different methodologies are used to asses the quality of an image fusion. Moreover, it is not possible to designate, a priori, a given algorithm as the best, not only because of the different characteristics of the sensors, but also because of the different atmospherics conditions or peculiarities of the different study areas, among other reasons.

Paper Details

Date Published: 4 October 2017
PDF: 12 pages
Proc. SPIE 10427, Image and Signal Processing for Remote Sensing XXIII, 104271E (4 October 2017); doi: 10.1117/12.2277543
Show Author Affiliations
Paúl Pesántez-Cobos, Univ. de Cuenca (Ecuador)
Fulgencio Cánovas-García, Univ. Politécnica de Cartagena (Spain)
Univ. Técnica Particular de Loja (Ecuador)
Francisco Alonso-Sarría, Univ. de Murcia (Spain)

Published in SPIE Proceedings Vol. 10427:
Image and Signal Processing for Remote Sensing XXIII
Lorenzo Bruzzone, Editor(s)

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