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Title: Optimised context encoder-based fusion approach with deep learning and nonlinear least square method for pan-sharpening

Authors: Preeti Singh; Sarvpal Singh; Marcin Paprzycki

Addresses: Department of Computer Science and Engineering, Madan Mohan Malaviya University of Technology, Gorakhpur, UP, India ' Department of Information Technology and Computer Application (ITCA), Madan Mohan Malaviya University of Technology, Gorakhpur, Gorakhpur, UP, India ' Department of Computer Science, Polish Academy of Science, Poland

Abstract: In this study, a hybrid optimisation strategy is used to build a deep learning system for pan sharpening. The final output image is examined using a weighted nonlinear regression model after the spatial resolution of the low resolution-hyperspectral image (LR-HIS) and high resolution multi-spectral image (HR-MSI) is increased. The deep maxout network (DMN), which used residual learning to acquire its priors, is given the HR-MSI. Moreover, DMN is trained by fractional competitive multi-verse feedback tree algorithm (FrCMVFTA). Finally, the output produced from DMN and a weighted nonlinear regression model is combined together for obtaining pan sharpened image. The PSNR value obtained by the FrCMVFTA-based DMN for the dataset Indian pines by varying the number of bands is 5.41% greater than the existing approaches. The DD value obtained by the FrCMVFTA-based DMN for the dataset Pavia by varying the number of bands is 31.47% greater than existing approaches.

Keywords: pan sharpening; deep maxout network; feedback artificial tree algorithm; degree of distortion; competitive multi-verse optimiser.

DOI: 10.1504/IJBIC.2024.136228

International Journal of Bio-Inspired Computation, 2024 Vol.23 No.1, pp.53 - 67

Accepted: 31 Aug 2023
Published online: 22 Jan 2024 *

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