Deep Learning-Based Change Detection in Remote Sensing Images: A Review

Ayesha Shafique,Guo Cao,Muhammad Asad,Zia Khan,Muhammad Aslam

doi:10.3390/rs14040871

Abstract

Images gathered from different satellites are vastly available these days due to the fast development of remote sensing (RS) technology. These images significantly enhance the data sources of change detection (CD). CD is a technique of recognizing the dissimilarities in the images acquired at distinct intervals and are used for numerous applications, such as urban area development, disaster management, land cover object identification, etc. In recent years, deep learning (DL) techniques have been used tremendously in change detection processes, where it has achieved great success because of their practical applications. Some researchers have even claimed that DL approaches outperform traditional approaches and enhance change detection accuracy. Therefore, this review focuses on deep learning techniques, such as supervised, unsupervised, and semi-supervised for different change detection datasets, such as SAR, multispectral, hyperspectral, VHR, and heterogeneous images, and their advantages and disadvantages will be highlighted. In the end, some significant challenges are discussed to understand the context of improvements in change detection datasets and deep learning models. Overall, this review will be beneficial for the future development of CD methods.

Highlights

We present the data from different sensors used for Change detection (CD) in detail, mainly including synthetic aperture radar (SAR), multispectral, hyperspectral, Very High Spatial Resolution (VHR), and heterogeneous images
For assessing the performances of different evaluation matrices, we presented some previous results as an example that are based on change detection by using deep learning methods for different datasets
We reviewed some of the most well-known remote sensing (RS) datasets and the latest deep learning (DL) algorithms for change detection in the literature that achieved outstanding performances

Summary

Introduction

Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. With the advancement of Remote sensing(RS) technology, RS platforms have become increasingly capable of collecting a wide range of data. These available data have become key resources for environmental monitoring by detecting changes on the land surface. It has attracted widespread interest due to it being extensively used in several real-world applications, such as fire detection, environmental monitoring [4], disaster monitoring [5], urban change analysis [6], and land management [7], among others.CD has attracted increasing attention from researchers throughout the world

Results

Discussion

Conclusion