Clustering augmented self-supervised learning: an application to land cover mapping

Rahul Ghosh, Xiaowei Jia, Leikun Yin, Chenxi Lin, Zhenong Jin, Vipin Kumar

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Collecting large annotated datasets in Remote Sensing is often expensive and thus can become a significant obstacle for training advanced machine learning models. Standard techniques for addressing this issue, based on the underlying idea of pre-training the Deep Neural Networks (DNN) on freely available large datasets, cannot be used for Remote Sensing due to the unavailability of such large-scale labeled datasets and the heterogeneity of data sources caused by the varying spatial and spectral resolution of different sensors. Self-supervised learning is an alternative approach that learns feature representation from unlabeled images without human annotations. In this paper, we introduce a new method for land cover mapping by using a clustering-based pretext task for self-supervised learning. We demonstrate the method's effectiveness in two societally relevant applications from the aspect of segmentation performance, discriminative feature representation learning, and the underlying cluster structure. We also show the effectiveness of the active sampling using the clusters obtained from our method in improving the mapping accuracy given a limited budget for annotating. Finally, a real-world application of the developed framework in identifying intra-class categories of well-managed and poorly-managed plantations demonstrates its utility in a problem of considerable societal importance.

Original languageEnglish (US)
Title of host publication30th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, ACM SIGSPATIAL GIS 2022
EditorsMatthias Renz, Mohamed Sarwat, Mario A. Nascimento, Shashi Shekhar, Xing Xie
PublisherAssociation for Computing Machinery
ISBN (Electronic)9781450395298
DOIs
StatePublished - Nov 1 2022
Event30th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, SIGSPATIAL GIS 2022 - Seattle, United States
Duration: Nov 1 2022Nov 4 2022

Publication series

NameGIS: Proceedings of the ACM International Symposium on Advances in Geographic Information Systems

Conference

Conference30th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, SIGSPATIAL GIS 2022
Country/TerritoryUnited States
CitySeattle
Period11/1/2211/4/22

Bibliographical note

Funding Information:
8 ACKNOWLEDGEMENTS This work was funded by the NSF awards 1838159, 1739191, and 2147195, and National Aeronautics and Space Administration (NASA) Land Cover Land Use Change program, grant 80NSSC20K1485, and NASA AIST program, grant 80NSSC22K1164. Access to computing facilities was provided by the Minnesota Supercomputing Institute.

Publisher Copyright:
© 2022 ACM.

Keywords

  • land cover mapping
  • self-supervised learning
  • semantic segmentation

Fingerprint

Dive into the research topics of 'Clustering augmented self-supervised learning: an application to land cover mapping'. Together they form a unique fingerprint.

Cite this