A comparison of machine learning techniques to extract human settlements from high resolution imagery

Jeanette Weaver, Brian Moore, Andrew Reith, Jacob McKee, Dalton Lunga

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

11 Scopus citations

Abstract

Two machine learning techniques were developed to extract human settlements from very high resolution (VHR) satellite images of 3 provinces in Afghanistan: Logar, Panjsher, and Wardak. The results were then compared with analyst verified reference data information known as the LandScan Settlement Layer (LandScan SL).[1] This study attempts to compare settlement mapping results from a support vector machine (SVM) classifier specifically integrated in a current settlement mapping framework and a deep learner utilizing a convolutional neural network (CNN) approach. By comparing the results from the SVM and the CNN to the reference data information we demonstrate that the CNN yields more accurate results overall, in terms of overall pixel cells, and the SVM performs more accurately in omission, based on derived statistics against the reference data information.

Original languageEnglish
Title of host publication2018 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2018 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages6412-6415
Number of pages4
ISBN (Electronic)9781538671504
DOIs
StatePublished - Oct 31 2018
Event38th Annual IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2018 - Valencia, Spain
Duration: Jul 22 2018Jul 27 2018

Publication series

NameInternational Geoscience and Remote Sensing Symposium (IGARSS)
Volume2018-July

Conference

Conference38th Annual IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2018
Country/TerritorySpain
CityValencia
Period07/22/1807/27/18

Keywords

  • Convolutional neural networks
  • Settlement mapping
  • Support vector machine
  • VHR

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