TY - JOUR
T1 - Ensemble of deep learning‐based multimodal remote sensing image classification model on unmanned aerial vehicle networks
AU - Joshi, Gyanendra Prasad
AU - Alenezi, Fayadh
AU - Thirumoorthy, Gopalakrishnan
AU - Dutta, Ashit Kumar
AU - You, Jinsang
N1 - Funding Information:
Funding: This work was supported by Institute of Information & communications Technology Plan‐ ning & Evaluation (IITP) grant funded by the Korea government(MSIT) (No. 2020‐0‐00107, Devel‐ opment of the technology to automate the recommendations for big data analytic models that define data characteristics and problems).
Publisher Copyright:
© 2021 by the authors. Licensee MDPI, Basel, Switzerland.
PY - 2021/11/1
Y1 - 2021/11/1
N2 - 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.
AB - 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.
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U2 - 10.3390/math9222984
DO - 10.3390/math9222984
M3 - Article
AN - SCOPUS:85119904978
SN - 2227-7390
VL - 9
JO - Mathematics
JF - Mathematics
IS - 22
M1 - 2984
ER -