Deep Learning for Change Detection in Remote Sensing Images: Comprehensive Review and Meta-Analysis

• URL: http://arxiv.org/abs/2006.05612v1
• Date: Wed, 10 Jun 2020 02:14:08 GMT
• Title: Deep Learning for Change Detection in Remote Sensing Images: Comprehensive Review and Meta-Analysis
• Authors: Lazhar Khelifi and Max Mignotte
• Abstract summary: We first introduce the fundamentals of deep learning methods which arefrequently adopted for change detection. Then, we focus on deep learning-based change detection methodologies for remote sensing images by giving a general overview of the existing methods. As a result of these investigations, promising new directions were identified for future research.
• Score: 12.462608802359936
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Deep learning (DL) algorithms are considered as a methodology of choice for remote-sensing image analysis over the past few years. Due to its effective applications, deep learning has also been introduced for automatic change detection and achieved great success. The present study attempts to provide a comprehensive review and a meta-analysis of the recent progress in this subfield. Specifically, we first introduce the fundamentals of deep learning methods which arefrequently adopted for change detection. Secondly, we present the details of the meta-analysis conducted to examine the status of change detection DL studies. Then, we focus on deep learning-based change detection methodologies for remote sensing images by giving a general overview of the existing methods. Specifically, these deep learning-based methods were classified into three groups; fully supervised learning-based methods, fully unsupervised learning-based methods and transfer learning-based techniques. As a result of these investigations, promising new directions were identified for future research. This study will contribute in several ways to our understanding of deep learning for change detection and will provide a basis for further research.

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