Deep learning-based change detection in remote sensing images: a review

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

*Corresponding author for this work

Research output: Contribution to journalReview articlepeer-review

4 Citations (Scopus)
154 Downloads (Pure)

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.
Original languageEnglish
Article number871
Number of pages40
JournalRemote Sensing
Volume14
Issue number4
DOIs
Publication statusPublished - 11 Feb 2022

Keywords

  • change detection methods
  • remote sensing images
  • SAR image
  • multispectral images
  • hyperspectral images
  • VHR images
  • heterogeneous image
  • deep learning

Fingerprint

Dive into the research topics of 'Deep learning-based change detection in remote sensing images: a review'. Together they form a unique fingerprint.

Cite this