Coarse-To-Fine Unsupervised Change Detection for Remote Sensing Images Via Object-Based MRF and Inception UNET

Xuan Hou, Yunpeng Bai, Haonan Shi, Ying Li*

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference Proceeding (Non-Journal item)

2 Citations (SciVal)

Abstract

With the rapid development of various satellite sensor techniques, remote sensing imagery has been an important source of data in change detection applications. This paper aims to propose an unsupervised change detection method based on Object-based Markov Random Filed (OMRF) and Inception UNet (IUNet). Our method first utilizes a difference image (DI) obtained from two bi-temporal images as the initial feature, and proposes the OMRF algorithm based on homogeneous region to pre-classify the DI thus derive the coarse change map. The IUNet is then constructed to extract the points with high confidence from the coarse change map for training. Eventually, the trained model is fed to classify the original feature, then the final change map is obtained. Experimental results indicate that our method yields great detection results even without supervision.

Original languageEnglish
Title of host publication2022 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2022 - Proceedings
PublisherIEEE Press
Pages3288-3292
Number of pages5
ISBN (Electronic)9781665405409
ISBN (Print)9781665405409
DOIs
Publication statusPublished - 2022
Event47th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2022 - Virtual, Online, Singapore
Duration: 23 May 202227 May 2022

Publication series

NameICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
Volume2022-May
ISSN (Print)1520-6149

Conference

Conference47th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2022
Country/TerritorySingapore
CityVirtual, Online
Period23 May 202227 May 2022

Keywords

  • change detection
  • coarse-to-fine model
  • object-based markov random filed
  • unsupervised learning

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