A Novel Approach for Satellite Image Classification using Optimized Deep Convolutional Neural Network

A Novel Approach for Satellite Image Classification using Optimized Deep Convolutional Neural Network
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© 2022 by IJETT Journal
Volume-70 Issue-6
Year of Publication : 2022
Authors : Bindhu J S, Pramod K V
DOI : 10.14445/22315381/IJETT-V70I6P236

How to Cite?

Bindhu J S, Pramod K V, "A Novel Approach for Satellite Image Classification using Optimized Deep Convolutional Neural Network," International Journal of Engineering Trends and Technology, vol. 70, no. 6, pp. 349-365, 2022. Crossref, https://doi.org/10.14445/22315381/IJETT-V70I6P236

Abstract
Recently, remote sensing images have been extensively used in different scene classification. Satellite image classification is used for different applications like land use classification, crop monitoring, forest cover mapping, and natural disaster detection. Accurate classification of scenes in satellite images is very challenging due to the complex, rich details in the images. This work presents a deep learning framework for accurately classifying scene types through improved learning. The proposed approach pre-processes the input image with an adaptive bilateral filtering approach. Then, the pre-processed input image is given as an input to the proposed Optimized Deep Convolutional Neural Network (ODCNN) for improved feature learning and classification. Here, the ODCNN framework is utilized to classify different scenes in the satellite images accurately.
Moreover, the modified beetle swarm optimization (MBSO) algorithm is utilized for weights optimization in the ODCNN classifier. This process improves the learning of the ODCNN classifier by accurately detecting the scene in remote sensing images. The results of the presented approach are compared with various existing schemes using different performance measures. It is proved that the examined presented approach outperforms the various existing schemes in accuracy (99.75%), precision (99.16%), recall (99.523%), and F-measure (99.34%), kappa measure (0.99), and processing time (10.67 seconds).

Keywords
Filtering, Feature learning, Optimization, Deep learning, Classification.

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