Title: Black widow optimisation with deep learning-based feature fusion model for remote sensing image analysis

Authors: Vaishnavee Vijay Rathod; Dipti P. Rana; Rupa G. Mehta

Addresses: Department of Computer Science Engineering, Sardar Vallabhbhai National Institute of Technology, Surat, Gujarat, India ' Department of Computer Science Engineering, Sardar Vallabhbhai National Institute of Technology, Surat, Gujarat, India ' Department of Computer Science Engineering, Sardar Vallabhbhai National Institute of Technology, Surat, Gujarat, India

Abstract: Recently, achieving accurate remote sensing images (RSI) classification has been a primary goal in deep learning, given its extensive applications, including urban planning and disaster management. The performance of existing convolutional neural networks (CNN)-based strategies is primarily influenced by their parameter settings, necessitating automated hyperparameter tuning through metaheuristic methods. The proposed BWODLF-RSI technique integrates black widow optimisation with a deep learning feature fusion model for enhanced RSI analysis. The preliminary processing step is to enhance RSI quality using noise reduction through a Gaussian filter (GF), enhancing contrast with the help of contrast limited adaptive histogram equalisation (CLAHE), and data augmentation to prevent overfitting. It is followed by employing Inception v3 and DenseNet201 to extract and fuse potent features. A critical aspect of this strategy is using black widow optimisation to fine-tune the kernel extreme learning machine (KELM) model, attaining a notable RSI classification accuracy of 94.05%. When tested on UCM and AID datasets, the BWODLF-RSI approach demonstrated superior feature selection and RSI analysis performance.

Keywords: remote sensing; image classification; deep learning; pre-processing; feature fusion.

DOI: 10.1504/IJCSE.2025.143464

International Journal of Computational Science and Engineering, 2025 Vol.28 No.1, pp.56 - 70

Received: 14 Jun 2023
Accepted: 22 Jan 2024
Published online: 21 Dec 2024 *

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