Ensemble of Deep Learning-Based Multimodal Remote Sensing Image Classification Model on Unmanned Aerial Vehicle Networks

Gyanendra Prasad Joshi, Fayadh Alenezi, Gopalakrishnan Thirumoorthy, Ashit Kumar Dutta and Jinsang You
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Gyanendra Prasad Joshi: Department of Computer Science and Engineering, Sejong University, Seoul 05006, Korea
Fayadh Alenezi: Department of Electrical Engineering, College of Engineering, Jouf University, Sakakah 72388, Saudi Arabia
Gopalakrishnan Thirumoorthy: School of Computer Science and Engineering, Vellore Institute of Technology, Vellore 632014, India
Ashit Kumar Dutta: Department of Computer Science and Information Systems, College of Applied Sciences, Al Maarefa University, Ad Diriyah, Riyadh 13713, Saudi Arabia
Jinsang You: Seculayer Company, Ltd., Seongsuil-ro 4-gil, 25, Kolon Digital Tower, Seongdong-gu, Seoul 04784, Korea

Mathematics, 2021, vol. 9, issue 22, 1-17

Abstract: Recently, unmanned aerial vehicles (UAVs) have been used in several applications of environmental modeling and land use inventories. At the same time, the computer vision-based remote sensing image classification models are needed to monitor the modifications over time such as vegetation, inland water, bare soil or human infrastructure regardless of spectral, spatial, temporal, and radiometric resolutions. In this aspect, this paper proposes an ensemble of DL-based multimodal land cover classification (EDL-MMLCC) models using remote sensing images. The EDL-MMLCC technique aims to classify remote sensing images into the different cloud, shades, and land cover classes. Primarily, median filtering-based preprocessing and data augmentation techniques take place. In addition, an ensemble of DL models, namely VGG-19, Capsule Network (CapsNet), and MobileNet, is used for feature extraction. In addition, the training process of the DL models can be enhanced by the use of hosted cuckoo optimization (HCO) algorithm. Finally, the salp swarm algorithm (SSA) with regularized extreme learning machine (RELM) classifier is applied for land cover classification. The design of the HCO algorithm for hyperparameter optimization and SSA for parameter tuning of the RELM model helps to increase the classification outcome to a maximum level considerably. The proposed EDL-MMLCC technique is tested using an Amazon dataset from the Kaggle repository. The experimental results pointed out the promising performance of the EDL-MMLCC technique over the recent state of art approaches.

Keywords: unmanned aerial vehicles; remote sensing; deep learning; parameter tuning; planetscope imagery; ensemble model (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (5)

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