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

Mathematics

卷:9

Gyanendra Prasad Joshi¹  Ashit Kumar Dutta²  Fayadh Alenezi³  Gopalakrishnan Thirumoorthy⁴  Jinsang You⁵ 

[1] Department of Computer Science and Engineering, Sejong University, Seoul 05006, Korea;

[2] Department of Computer Science and Information Systems, College of Applied Sciences, Al Maarefa University, Ad Diriyah, Riyadh 13713, Saudi Arabia;

[3] Department of Electrical Engineering, College of Engineering, Jouf University, Sakakah 72388, Saudi Arabia;

[4] School of Computer Science and Engineering, Vellore Institute of Technology, Vellore 632014, India;

[5] Seculayer Company, Ltd., Seongsuil-ro 4-gil, 25, Kolon Digital Tower, Seongdong-gu, Seoul 04784, Korea;

关键词: unmanned aerial vehicles;    remote sensing;    deep learning;    parameter tuning;    planetscope imagery;    ensemble model;   

DOI  :  10.3390/math9222984

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown