Categorizing Sentinel-2 Images Based On Binary-Weighted VGG-16 Network

Authors

  • Vinod Mansiram Kapse Director, Department of Electronics & Communication Engineering, Noida Institute of Engineering and Technology, Greater Noida, Uttar Pradesh, India
  • Jayachandran A. Professor & HOD, Department of Computer Science and Engineering, Presidency University, Bangalore, India
  • Bharti Sharma Assistant Professor, Department of Computer Science & Engineering, Vivekananda Global University, Jaipur, India
  • Abhilash Kumar Saxena Assistant Professor, College of Computing Science and Information Technology, Teerthanker Mahaveer University, Moradabad, Uttar Pradesh, India

Keywords:

Satellite imagery, Sentinel-2 images, binary weighted VGG-16 (BW-VGG), fruit fly optimization (FFO)

Abstract

Intelligent In a wide range of applications, including the classification of land cover and environmental surveillance, satellite imagery is essential. High-resolution multispectral images from the Sentinel-2 satellite project offer helpful information for a variety of Earth observation needs. In this paper, a brand-new binary weighted VGG-16 (BW-VGG) method for classifying Sentinel-2 images is proposed. In this study, we propose a binary weighting strategy that increases the discriminative strength of the network by giving more weight to more relevant spectrum bands. The model's training set was the multidimensional Sentinel 2 photo with its 13 spectrum bands. The featured photographs were categorized into several classes to build the dataset for the study area. By using the fruit fly optimization (FFO) method, the proposed model's accuracy is increased even more. The suggested approach was found to have the highest accuracy. Results from experiments show that the proposed approach is more effective than existing approaches at classifying Sentinel-2 photos.

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Published

11.07.2023

How to Cite

Kapse, V. M. ., A., J. ., Sharma, B. ., & Saxena, A. K. . (2023). Categorizing Sentinel-2 Images Based On Binary-Weighted VGG-16 Network. International Journal of Intelligent Systems and Applications in Engineering, 11(8s), 434–439. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/3071

Issue

Vol. 11 No. 8s (2023): Advances on Machine Learning and Artificial Intelligence in Computer Technology

Section

Research Article

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