A Semi-Supervised Approach Towards Land Cover Mapping with Sentinel-2 Desnse Time-Series Imagery

Ting Hu, Xin Huang, Jiayi Li, Jon Atli Benediktsson, Jiansi Yang, Jianya Gong

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

Abstract

This paper presents a new semi-supervised method for land cover classification using Sentinel-2 time-series images, which can deal with the problem of unclear observations. First, the MCCR method, which is constituted by the matrix completion (MC) of unclear observations and feature-adaptive collaborative representation (CR) based classifier, is adopted to handle the data quality problem. Second, by fusing RF, AdaBoost, and MCCR, a tri-training process is proposed to iteratively select the semi-labeled samples, considering the difference of classification certainty in different classifiers and classes. Experiments on two sets of Sentinel-2 images are conducted to validate the effectiveness of the proposed semi-supervised method.

Original languageEnglish
Title of host publication2019 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2019 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages2423-2426
Number of pages4
ISBN (Electronic)9781538691540
DOIs
Publication statusPublished - Jul 2019
Event39th IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2019 - Yokohama, Japan
Duration: 28 Jul 20192 Aug 2019

Publication series

NameInternational Geoscience and Remote Sensing Symposium (IGARSS)

Conference

Conference39th IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2019
Country/TerritoryJapan
CityYokohama
Period28/07/192/08/19

Bibliographical note

Publisher Copyright:
© 2019 IEEE.

Other keywords

  • land cover classification
  • Sentinel-2
  • time series images
  • tri-training

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