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Integration of object-based image analysis and convolutional neural network for the classification of high-resolution satellite image: a comparative assessment

During the past decade, deep learning-based classification methods (e.g., convolutional neural networks—CNN) have demonstrated great success in a variety of vision tasks, including satellite image classification. Deep learning methods, on the other hand, do not preserve the precise edges of the targ...

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Main Authors: Azeez, Omer Saud, M. Shafri, Helmi Z., Alias, Aidi Hizami, Haron, Nuzul A.
Format: Article
Published: Multidisciplinary Digital Publishing Institute 2022
Online Access:http://psasir.upm.edu.my/id/eprint/101946/
https://www.mdpi.com/2076-3417/12/21/10890
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spelling my.upm.eprints.1019462023-06-16T20:16:25Z http://psasir.upm.edu.my/id/eprint/101946/ Integration of object-based image analysis and convolutional neural network for the classification of high-resolution satellite image: a comparative assessment Azeez, Omer Saud M. Shafri, Helmi Z. Alias, Aidi Hizami Haron, Nuzul A. During the past decade, deep learning-based classification methods (e.g., convolutional neural networks—CNN) have demonstrated great success in a variety of vision tasks, including satellite image classification. Deep learning methods, on the other hand, do not preserve the precise edges of the targets of interest and do not extract geometric features such as shape and area. Previous research has attempted to address such issues by combining deep learning with methods such as object-based image analysis (OBIA). Nonetheless, the question of how to integrate those methods into a single framework in such a way that the benefits of each method complement each other remains. To that end, this study compared four integration frameworks in terms of accuracy, namely OBIA artificial neural network (OBIA ANN), feature fusion, decision fusion, and patch filtering, according to the results. Patch filtering achieved 0.917 OA, whereas decision fusion and feature fusion achieved 0.862 OA and 0.860 OA, respectively. The integration of CNN and OBIA can improve classification accuracy; however, the integration framework plays a significant role in this. Future research should focus on optimizing the existing CNN and OBIA frameworks in terms of architecture, as well as investigate how CNN models should use OBIA outputs for feature extraction and classification of remotely sensed images. Multidisciplinary Digital Publishing Institute 2022-10-27 Article PeerReviewed Azeez, Omer Saud and M. Shafri, Helmi Z. and Alias, Aidi Hizami and Haron, Nuzul A. (2022) Integration of object-based image analysis and convolutional neural network for the classification of high-resolution satellite image: a comparative assessment. Applied Sciences, 12 (21). art. no. 10890. pp. 1-21. ISSN 2076-3417 https://www.mdpi.com/2076-3417/12/21/10890 10.3390/app122110890
institution Universiti Putra Malaysia
building UPM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Putra Malaysia
content_source UPM Institutional Repository
url_provider http://psasir.upm.edu.my/
description During the past decade, deep learning-based classification methods (e.g., convolutional neural networks—CNN) have demonstrated great success in a variety of vision tasks, including satellite image classification. Deep learning methods, on the other hand, do not preserve the precise edges of the targets of interest and do not extract geometric features such as shape and area. Previous research has attempted to address such issues by combining deep learning with methods such as object-based image analysis (OBIA). Nonetheless, the question of how to integrate those methods into a single framework in such a way that the benefits of each method complement each other remains. To that end, this study compared four integration frameworks in terms of accuracy, namely OBIA artificial neural network (OBIA ANN), feature fusion, decision fusion, and patch filtering, according to the results. Patch filtering achieved 0.917 OA, whereas decision fusion and feature fusion achieved 0.862 OA and 0.860 OA, respectively. The integration of CNN and OBIA can improve classification accuracy; however, the integration framework plays a significant role in this. Future research should focus on optimizing the existing CNN and OBIA frameworks in terms of architecture, as well as investigate how CNN models should use OBIA outputs for feature extraction and classification of remotely sensed images.
format Article
author Azeez, Omer Saud
M. Shafri, Helmi Z.
Alias, Aidi Hizami
Haron, Nuzul A.
spellingShingle Azeez, Omer Saud
M. Shafri, Helmi Z.
Alias, Aidi Hizami
Haron, Nuzul A.
Integration of object-based image analysis and convolutional neural network for the classification of high-resolution satellite image: a comparative assessment
author_facet Azeez, Omer Saud
M. Shafri, Helmi Z.
Alias, Aidi Hizami
Haron, Nuzul A.
author_sort Azeez, Omer Saud
title Integration of object-based image analysis and convolutional neural network for the classification of high-resolution satellite image: a comparative assessment
title_short Integration of object-based image analysis and convolutional neural network for the classification of high-resolution satellite image: a comparative assessment
title_full Integration of object-based image analysis and convolutional neural network for the classification of high-resolution satellite image: a comparative assessment
title_fullStr Integration of object-based image analysis and convolutional neural network for the classification of high-resolution satellite image: a comparative assessment
title_full_unstemmed Integration of object-based image analysis and convolutional neural network for the classification of high-resolution satellite image: a comparative assessment
title_sort integration of object-based image analysis and convolutional neural network for the classification of high-resolution satellite image: a comparative assessment
publisher Multidisciplinary Digital Publishing Institute
publishDate 2022
url http://psasir.upm.edu.my/id/eprint/101946/
https://www.mdpi.com/2076-3417/12/21/10890
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score 13.211869

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