Deep neural network ensembles for remote sensing land cover and land use classification

Burak Ekim, Elif Sertel*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

21 Citations (Scopus)

Abstract

With the advancement of satellite technology, a considerable amount of very high-resolution imagery has become available to be used for the Land Cover and Land Use (LCLU) classification task aiming to categorize remotely sensed images based on their semantic content. Recently, Deep Neural Networks (DNNs) have been widely used for different applications in the field of remote sensing and they have profound impacts; however, improvement of the generalizability and robustness of the DNNs needs to be progressed further to achieve higher accuracy for a variety of sensing geometries and categories. We address this problem by deploying three different Deep Neural Network Ensemble (DNNE) methods and creating a comparative analysis for the LCLU classification task. DNNE enables improvement of the performance of DNNs by ensuring the diversity of the models that are combined. Thus, enhances the generalizability of the models and produces more robust and generalizable outcomes for LCLU classification tasks. The experimental results on NWPU-RESISC45 and AID datasets demonstrate that utilizing the aggregated information from multiple DNNs leads to an increase in classification performance, achieves state-of-the-art, and promotes researchers to make use of DNNE.

Original languageEnglish
Pages (from-to)1868-1881
Number of pages14
JournalInternational Journal of Digital Earth
Volume14
Issue number12
DOIs
Publication statusPublished - 2021

Bibliographical note

Publisher Copyright:
© 2021 Informa UK Limited, trading as Taylor & Francis Group. The International Journal of Digital Earth is an Official Journal of the International Society for Digital Earth.

Keywords

  • Classification
  • convolutional neural networks (CNN)
  • deep neural network ensembles (DNNE)
  • land cover and land use (LCLU)
  • remote sensing

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