Deep Learning Based Patch-Wise Land Cover Land Use Classification: A New Small Benchmark Sentinel-2 Image Dataset

Gulsan Alp, Elif Sertel

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

2 Citations (Scopus)

Abstract

In this paper, patch-wise land cover and land use (LCLU) classification was performed using the state-of-art ResNet 50 and Inception-ResNet-V2 architecture trained with Stochastic Gradient Descent(SGD) and Nadam optimizers. A new dataset was generated for the classification task using Sentinel-2 images having different patch sizes. The image patches were labeled using CORINE Land Cover (CLC) 2018 map. The dataset has 1961 image patches and it was divided into 1397 training and 564 testing patches during the experiment. Our dataset contains samples labeled with 7 CLC Level-2 classes. While the best training accuracy of 98.0% was obtained by Inception-ResNet-V2 trained with Nadam. The best testing accuracy of 93.0% was achieved with Inception-ResNet-V2 by using SGD optimizer.

Original languageEnglish
Title of host publicationIGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages3179-3182
Number of pages4
ISBN (Electronic)9781665427920
DOIs
Publication statusPublished - 2022
Event2022 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2022 - Kuala Lumpur, Malaysia
Duration: 17 Jul 202222 Jul 2022

Publication series

NameInternational Geoscience and Remote Sensing Symposium (IGARSS)
Volume2022-July

Conference

Conference2022 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2022
Country/TerritoryMalaysia
CityKuala Lumpur
Period17/07/2222/07/22

Bibliographical note

Publisher Copyright:
© 2022 IEEE.

Keywords

  • CORINE (CLC)
  • Deep Learning Classification
  • Land Cover
  • Land Use
  • Remote Sensing
  • Satellite Image Dataset
  • Sentinel-2

Fingerprint

Dive into the research topics of 'Deep Learning Based Patch-Wise Land Cover Land Use Classification: A New Small Benchmark Sentinel-2 Image Dataset'. Together they form a unique fingerprint.

Cite this