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

• Published in: European 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
• Language: English
• 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
• DOI: 10.1080/22797254.2021.2017799
• ISSN: 2279-7254

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.