sis 18: e38

Research Article

Scene Classification of Remotely Sensed Images using Optimized RSISC-16 Net Deep Convolutional Neural Network Model

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  • @ARTICLE{10.4108/eai.1-2-2022.173292,
        author={P. Deepan and L. R. Sudha and K. Kalaivani and J. Ganesh},
        title={Scene Classification of Remotely Sensed Images using Optimized RSISC-16 Net Deep Convolutional Neural Network Model},
        journal={EAI Endorsed Transactions on Scalable Information Systems: Online First},
        volume={},
        number={},
        publisher={EAI},
        journal_a={SIS},
        year={2022},
        month={2},
        keywords={Optimized RSISC-16, Scene classification, remote sensing image, and convolutional neural network},
        doi={10.4108/eai.1-2-2022.173292}
    }
    
  • P. Deepan
    L. R. Sudha
    K. Kalaivani
    J. Ganesh
    Year: 2022
    Scene Classification of Remotely Sensed Images using Optimized RSISC-16 Net Deep Convolutional Neural Network Model
    SIS
    EAI
    DOI: 10.4108/eai.1-2-2022.173292
P. Deepan1,*, L. R. Sudha2, K. Kalaivani3, J. Ganesh4
  • 1: Assistant Professor, Department of CSE (AI&ML), St. Martin’s Engineering College, Telangana, India
  • 2: Associate Professor, Department of CSE, Annamalai University, Tamilnadu, India
  • 3: Associate Professor, Department of CSE, E.G.S Pillay, Nagapattinam, Tamilnadu, India
  • 4: Assistant Professor, Department of CSE, Anjalai Ammal Mahalingam Engineering College,Tamilnadu, India
*Contact email: deepanp87@gmail.com

Abstract

Remote Sensing Image (RSI) analysis has seen a massive increase in popularity over the last few decades, due to the advancement of deep learning models. A wide variety of deep learning models have emerged for the task of scene classification in remote sensing image analysis. The majority of these models have shown significant success. However, we found that there is significant variability, in order to improve the system efficiency in characterizing complex patterns in remote sensing imagery. We achieved this goal by expanding the architecture of VGG-16 Net and fine-tuning hyperparameters such as batch size, dropout probabilities, and activation functions to create the optimized Remote Sensing Image Scene Classification (RSISC-16 Net) deep learning model for scene classification. Using the Talos optimization tool, the results are carried out. This will increase efficiency and reduce the risk of over-fitting. Our proposed RSISC-16 Net model outperforms the VGG-16 Net model, according to experimental results.