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Land cover classification from ICESat/GLAS waveform data

  • Beihang University

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

Abstract

Lidar waveform attributes extracted from the Ice, Cloud, and land Elevation Satellite/Geoscience Laser Altimeter System (ICESat/GLAS) were used to distinguish land cover types for GLAS footprints over Jakobshavn Glacier, West Greenland. The waveform derived attributes (reflectivity and kurtosis) and self-computed waveform attributes (waveform width and waveform beginning location) were extracted as features, while true labels were generated from the LANDSAT 7 image as ground truth through gap-filling, three-band combination and supervised maximum likelihood classification. A Gaussian Process (GP) classifier was used to train classification model for classifying the land cover types into three different categories: snow, bare bedrock and sea water. Over this test site, the algorithm achieved an overall accuracy (OA) of 92.22%.

Original languageEnglish
Title of host publicationIST 2017 - IEEE International Conference on Imaging Systems and Techniques, Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1-6
Number of pages6
ISBN (Electronic)9781538616208
DOIs
StatePublished - 1 Jul 2017
Event2017 IEEE International Conference on Imaging Systems and Techniques, IST 2017 - Beijing, China
Duration: 18 Oct 201720 Oct 2017

Publication series

NameIST 2017 - IEEE International Conference on Imaging Systems and Techniques, Proceedings
Volume2018-January

Conference

Conference2017 IEEE International Conference on Imaging Systems and Techniques, IST 2017
Country/TerritoryChina
CityBeijing
Period18/10/1720/10/17

Keywords

  • Gaussian process
  • ICESat/GLAS
  • Land cover classification
  • Supervised maximum likelihood classification

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