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Optimal trained ensemble of classification model for satellite image classification

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Abstract

Satellite image classification is the most significant remote sensing method for computerized analysis and pattern detection of satellite data. This method relies on the image's diversity structures and necessitates a thorough validation of the training data using the chosen classification algorithm. The classifying of remotely sensed images refers to the process of classifying pixels into a fixed set of distinct classes based on their data values. The pixel is categorized into that class if it complies with a specific set of requirements. A new SIC scheme is suggested in this work, where, Contrast limited adaptive histogram equalization (CLAHE) is deployed for pre-processing the image. Further, statistical features, Vegetation Indices (VI) and Improved Hybrid Vegetation Index (IHVI) are extracted from the preprocessed image. Then, we deploy 2 phase classification process for classifying the image. In the first phase, Bi-Directional Gated Recurrent Unit (BI-GRU), Convolutional Neural Network (CNN) and Deep Max Out (DMO) are used. In 2nd phase, optimized RNN is used for determining the final classification result. Also, the training of the model is done by the new Chaotic Opposition Behavior Improvised Coot Algorithm (COBI-CA) optimization via tuning the optimal weights. The COOT method has been enhanced and is now the suggested algorithm. Lastly, we conduct an analysis to compare the suggested Satellite Image Classification (SIC) model's performance to the traditional approaches in terms of statistical analysis, precision, accuracy, and error measure. In comparison to other conventional methods, the accuracy of the suggested model is 97%.

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Data availability

The data is available in https://github.com/ujjwalll/GACMIS.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, Chandigarh College of Engineering and Technology, Chandigarh, 160019, India

    Sarabjeet Singh

  2. Department of Computer Science and Engineering, Guru Nanak Institutions Technical Campus, Ranga Reddy District, Ibrahimpatnam, Telangana, 501506, India

    Harish Kundra, P. Venkata Pratima, M. V. Anjana Devi & Mariya Hassan

  3. Department of Artificial Intelligence and Data Science, Guru Nanak Institute of Technology, Ranga Reddy District, Ibrahimpatnam, Telangana, 501506, India

    Sheetal Kundra

  4. Department of Computer Science and Engineering, Central University of Haryana, Jant-Pali, Mahendergarh, 123031, India

    Sushil Kumar

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  1. Sarabjeet Singh
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Correspondence to Harish Kundra.

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Singh, S., Kundra, H., Kundra, S. et al. Optimal trained ensemble of classification model for satellite image classification. Multimed Tools Appl (2024). https://doi.org/10.1007/s11042-024-19071-5

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