Contrastive Adaptive Segmentation Method for Spartina Alterniflora Based on Intermediate Domain Prototypes

  • Boyu Zhao
  • , Zhengmao Li
  • , Xiangyang Jiang
  • , Mengmeng Zhang
  • , Wei Li
  • , Yuxiang Zhang
  • , Xiukai Song*
  • *Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Citation (Scopus)

Abstract

Observing spartina alterniflora (S.alterniflora) with multi-temporal remote sensing data aids in comprehending its development and spread in wetland ecosystems, thereby facilitating the formulation of effective strategies for its containment and control. Unsupervised Domain Adaptation (UDA) techniques uncover its spatio-temporal patterns, but most methods miss critical domain and class differences, whereas Intermediate Domain Prototype Class-level Learning Network (IDCNet) addresses these gaps. IDCNet generates class prototypes based on intermediate domain features, incorporating inter-class information for more accurate distribution alignment. Experimental results on two cross-year multi-spectral datasets demonstrate that the proposed IDCNet outperforms several state-of-the-art UDA methods.

Original languageEnglish
Title of host publicationICIGP 2024 - Proceedings of the 2024 7th International Conference on Image and Graphics Processing
PublisherAssociation for Computing Machinery
Pages85-91
Number of pages7
ISBN (Electronic)9798400716720
DOIs
Publication statusPublished - 19 Jan 2024
Event7th International Conference on Image and Graphics Processing, ICIGP 2024 - Beijing, China
Duration: 19 Jan 202421 Jan 2024

Publication series

NameACM International Conference Proceeding Series

Conference

Conference7th International Conference on Image and Graphics Processing, ICIGP 2024
Country/TerritoryChina
CityBeijing
Period19/01/2421/01/24

Keywords

  • Intermediate Domain Prototypical Contrast Adaptation
  • Multitemporal Remote Sensing Image Segmentation
  • Spartina Alterniflora
  • Unsupervised Domain Adaptation

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