Deep Transfer Learning based Fusion Model for Environmental Remote Sensing Image Classification Model

Remote-sensing images comprise massive amount of spatial and semantic data that can be employed for several applications. Presently, deep learning (DL) models for RS image processing become a familiar research area. Due to the advancements of recent satellite imaging sensors , the issue of huge amou...

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Bibliographic Details
Published inEuropean journal of remote sensing Vol. 55; no. sup1; pp. 12 - 23
Main Authors Hilal, Anwer Mustafa, Al-Wesabi, Fahd N., Alzahrani​, Khalid J, Al Duhayyim, Mesfer, Ahmed Hamza, Manar, Rizwanullah, Mohammed, García Díaz, Vicente
Format Journal Article
LanguageEnglish
Published Cagiari Taylor & Francis 21.10.2022
Taylor & Francis Ltd
Taylor & Francis Group
Subjects
Algorithms
Classification
Data processing
Datasets
Deep learning
Deep transfer learning
Entropy
Environmental monitoring
Feature extraction
Fusion model
Image classification
Image processing
Machine learning
Modelling
Optimization
Parameter tuning
Remote sensing
Satellite imagery
Satellites
Semantics
Spatial data
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Summary:Remote-sensing images comprise massive amount of spatial and semantic data that can be employed for several applications. Presently, deep learning (DL) models for RS image processing become a familiar research area. Due to the advancements of recent satellite imaging sensors , the issue of huge amount of data processing becomes a challenging problem. To accomplish this, deep transfer learning (DTL) models are developed to resolve the semantic gap among various datasets This study develops a new DTL-based fusion model for environmental remote-sensing image classification, called DTLF-ERSIC technique. The proposed technique focuses on the design of fusion model to combine multiple feature vectors and thereby attains maximum classification performance. The DTLF-ERSIC technique incorporates the entropy-based fusion of three feature extraction techniques, namely, Discrete Local Binary Pattern (DLBP), Residual Network (ResNet50), and EfficientNet models. Besides, a rain optimization algorithm (ROA) with fuzzy rule-based classifier (FRC) is applied to predict the class labels of the test RS images and shows the novelty of the work. A comprehensive experimental analysis of the DTLF-ERSIC technique takes place on benchmark dataset and examined the results in terms of different performance measures. The simulation results reported the supremacy of the DTLF-ERSIC technique over the recent state-of-art techniques.
ISSN:2279-7254
2279-7254
DOI:10.1080/22797254.2021.2017799