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A new framework for object detection using fastcnn- Naïve Bayes classifier for remote sensing image extraction

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Abstract

In today's research, remote sensing applications play an important role. It might foresee any crisis, provide weather forecasting updates, geographical research, military security, and so forth. Object detection and recognition of remote sensing satellite images, however, is a difficult task. To solve the problem of distinct in remote sensing images, in this work the system, initially, the dataset goes under the phase of testing and training process. After the data is pre-processed with a fuzzy filter, the features are recovered utilizing an enhanced method by constructing effective CNN based Features from the Accelerated Segment Test feature extraction technique (FASTCNN) Convolutional Neural Network. Object detection classification is done via an optimized Naive Bayes (NB) classifier, which detects objects significantly faster and keeps the system running at a higher accuracy rate. The proposed feature extraction strategy outperforms the previous approach in terms of performance.

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All the authors mentioned in the manuscript have agreed for authorship, read and approved the manuscript, and given consent for submission and subsequent publication of the manuscript.

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Authors and Affiliations

  1. Department of Bioinformatics, Saveetha School of Engineering, SIMATS, Thandalam, Tamilnadu, India

    K. Kala

  2. Department of Biomedical Engineering, Rajalakshmi Engineering College, Chennai, 602105, India

    N. Padmasini

  3. Department of Biomedical Engineering, Easwari Engineering College, Anna University, Chennai, Tamil Nadu, India

    B. Suresh Chander Kapali

  4. Department of ECE, Siddharth Institute of Engineering & Technology(SIETK), Puttur, Andra Pradesh, India

    P. G. Kuppusamy

Authors
  1. K. Kala
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  2. N. Padmasini
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  3. B. Suresh Chander Kapali
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  4. P. G. Kuppusamy
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Contributions

All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by 1Kala. K, 2Padmasini and 3Suresh Chander Kapali. The first draft of the manuscript was written by Kuppusamy. P.G. All authors read and approved the final manuscript.

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Correspondence to K. Kala.

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Communicated by H. Babaie

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Kala, K., Padmasini, N., Kapali, B.S.C. et al. A new framework for object detection using fastcnn- Naïve Bayes classifier for remote sensing image extraction. Earth Sci Inform 15, 1779–1787 (2022). https://doi.org/10.1007/s12145-022-00834-3

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  • DOI: https://doi.org/10.1007/s12145-022-00834-3

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