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Enhanced hyperspectral image classification using multi-scale residual depthwise separable convolutional networks with advanced feature extraction and selection techniques

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International Journal of Information Technology (Singapore) 2025年 1-20页

作者： Medikonda, Venkata Reddy Reddy, Tatireddy Subba Yallamandaiah, S. Karthik, Sajja Anil, Chintalapudi Department of Computer Science and Engineering Marri Laxman Reddy Institute of Technology and Management Telangana Hyderabad 500043 India Department of Computer Science and Engineering B. V. Raju Institute of Technology Telangana Narsapur 502313 India Department of Electronics and Communication Engineering Vignan’s Nirula Institute of Technology & Science for Women Andhra Pradesh Guntur 522009 India Department of Computer Science and Engineering R.V.R & J.C College of Engineering Chowdavaram Andhra Pradesh Guntur 522019 India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram Andhra Pradesh 522302 India

Hyperspectral image classification faces significant challenges due to high-dimensional data and variability, often leading to inefficiencies in processing and reduced classification accuracy. Existing methods struggle with handling high-dimensional data, exhibit limited generalization capabilities, and lack efficiency in feature selection. To address these issues, this research proposes an advanced deep learning-based approach that integrates noise reduction, contrast enhancement, and min–max normalization for preprocessing. Feature extraction is performed using an improved ResNet50 model, while optimal feature selection is achieved through the improved artificial rabbits optimization (IARO) algorithm, which effectively reduces dimensionality while preserving essential features. Classification is carried out using a Multi-Scale Residual Depthwise Separable Convolutional Network (MRDSCNN), leveraging residual learning and depthwise separable convolutions to enhance performance. The proposed approach significantly improves data quality and model accuracy, providing a robust and efficient solution for hyperspectral image classification. These innovations collectively address the limitations of traditional methods, offering a more reliable and effective classification system. © Bharati Vidyapeeth's Institute of Computer Applications and Management 2025.

关键词： Contrast enhancement model Feature extraction Hyperspectral imaging classification improved artificial rabbits optimization algorithm Multi-scale residual depthwise separable convolutional neural network

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