Particle Swarm Optimization Based Deep Learning Model for Scene Classification of Remote Sensing Images

Authors

  • Jagroop Singh Department of Electronics and Communication Engineering, Amritsar College of Engineering and Technology, Punjab, India
  • Gurjeet Singh Department of Electronics and Communication Engineering, Amritsar College of Engineering and Technology, Punjab, India

Keywords:

Deep learning, Ensemble model, Hyper-parameters, Image classification

Abstract

Classification of remote sensing images is an open area of research. Recently deep learning models are extensively utilized to classify the images. To overcome the overfitting issue, an ensemble deep learning model is proposed. The hyper-parameter of the proposed model is tuned using particle swarm optimization. Initially, the features of remote sensing images are extracted. Theater, feature selection approach is used. The extracted features are then used to build the model by using the particle swarm optimization-based ensemble deep learning model. Extensive experiments are performed using the proposed model. The comparative analysis show that the proposed model outperforms the existing model.

Downloads

Download data is not yet available.

Downloads

Published

20-05-2021

Issue

Section

Articles

How to Cite

[1]
J. Singh and G. Singh, “Particle Swarm Optimization Based Deep Learning Model for Scene Classification of Remote Sensing Images”, IJRESM, vol. 4, no. 5, pp. 61–65, May 2021, Accessed: Dec. 21, 2024. [Online]. Available: https://journal.ijresm.com/index.php/ijresm/article/view/729