LULC image classification with convolutional neural network

Anas Tukur Balarabe, Ivan Jordanov

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

Abstract

The topic of land use and land cover classification (LULC) has attracted the interest of many researchers in recent times. A variety of techniques have been proposed for LULC and while some of them are semantic segmentation-based, others are classifying an entire image to determine its class. The semantic segmentation approaches label objects as members of a class by assigning a different colour to each class. In this work, we investigate class heterogeneity, which so far, to the best of our knowledge, has not been explored in LULC or scene classification. We carefully cluster the 21 classes of the UC Merced dataset into four superclasses based on their textural, spectral, or structural similarities and use the dataset to test the performance of our model. We also demonstrate the efficiency and accuracy of our deep learning approach, reporting a superior performance of our model in terms of Accuracy, Precision, Recall, and F1 score.

Original languageEnglish
Title of host publication2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS
PublisherInstitute of Electrical and Electronics Engineers
Pages5985-5988
Number of pages4
ISBN (Electronic)9781665403696, 9781665403689
ISBN (Print)9781665447621
DOIs
Publication statusPublished - 12 Oct 2021
Event2021 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2021 - Brussels, Belgium
Duration: 12 Jul 202116 Jul 2021

Publication series

NameIEEE International Geoscience and Remote Sensing Symposium proceedings
PublisherInstitute of Electrical and Electronics Engineers
ISSN (Print)2153-6996
ISSN (Electronic)2153-7003

Conference

Conference2021 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2021
Country/TerritoryBelgium
CityBrussels
Period12/07/2116/07/21

Keywords

  • convolutional neural networks (CNN)
  • deep learning
  • LULC
  • multilabel
  • scene classification

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