No-Reference Quality Assessment of Pan-Sharpening Images with Multi-Level Deep Image Representations

Author:

Stępień IgorORCID,Oszust MariuszORCID

Abstract

The Pan-Sharpening (PS) techniques provide a better visualization of a multi-band image using the high-resolution single-band image. To support their development and evaluation, in this paper, a novel, accurate, and automatic No-Reference (NR) PS Image Quality Assessment (IQA) method is proposed. In the method, responses of two complementary network architectures in a form of extracted multi-level representations of PS images are employed as quality-aware information. Specifically, high-dimensional data are separately extracted from the layers of the networks and further processed with the Kernel Principal Component Analysis (KPCA) to obtain features used to create a PS quality model. Extensive experimental comparison of the method on the large database of PS images against the state-of-the-art techniques, including popular NR methods adapted in this study to the PS IQA, indicates its superiority in terms of typical criteria.

Publisher

MDPI AG

Subject

General Earth and Planetary Sciences

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