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

• Published in: International journal of digital earth Vol. 14; no. 12; pp. 1868 - 1881
• Main Authors: Ekim, Burak, Sertel, Elif
• Format: Journal Article
• Language: English
• Published: Abingdon Taylor & Francis 02.12.2021; Taylor & Francis Ltd; Taylor & Francis Group
• Subjects: Artificial neural networks; Classification; Comparative analysis; convolutional neural networks (CNN); deep neural network ensembles (DNNE); Geometric accuracy; Image classification; Image resolution; Imagery; Information processing; Land cover; land cover and land use (LCLU); Land use; Land use classification; Neural networks; Remote sensing; Satellite technology; Satellites
• ISSN: 1753-8947; 1753-8955
• DOI: 10.1080/17538947.2021.1980125

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.