An Optimal Use of SCE-UA Method Cooperated With Superpixel Segmentation for Pansharpening

• Published in: IEEE geoscience and remote sensing letters Vol. 18; no. 9; pp. 1620 - 1624
• Main Authors: Hallabia, Hind, Hamam, Habib, Hamida, Ahmed Ben
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.09.2021; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Clustering; Clustering algorithms; Coefficients; Data acquisition; Image segmentation; Injection; Local injection gains; Optimization; pansharpening; Remote sensing; Satellites; shuffled complex evolution developed at University of Arizona (SCE-UA) method; superpixel segmentation; Task analysis; WorldView-3/-4 images
• DOI: 10.1109/LGRS.2020.3004320
• ISSN: 1545-598X

Pansharpening is achieved by inferring spatial details derived from a PANchromatic (PAN) image into its corresponding expanded multispectral (MS) bands. In this letter, we propose to apply an adaptive superpixel-based injection scheme that modulates the PAN details through an optimization procedure. Optimal injection coefficients can be locally estimated by using the shuffled complex evolution developed in the University of Arizona (SCE-UA) algorithm over multiple local segments (i.e., superpixels) resulting from the simple linear iterative clustering (SLIC) method. The performance of the proposed approach is assessed using degraded and real data sets acquired from WorldView-3 and WorldView-4 satellites. Experimental results show the suitability of the proposed adaptive injection scheme compared with other state-of-the-art pansharpening methods in terms of spatial and spectral qualities.