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Scene classification for aerial images based on CNN using sparse coding technique

Aerial scene classification purposes to automatically label aerial images with specific semantic categories. However, cataloguing presents a fundamental problem for high-resolution remote-sensing imagery (HRRS). Recent developments include several approaches and numerous algorithms address the task....

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Main Authors: Qayyum, A., Malik, A.S., Saad, N.M., Iqbal, M., Faris Abdullah, M., Rasheed, W., Rashid Abdullah, T.A., Bin Jafaar, M.Y.
Format: Article
Published: Taylor and Francis Ltd. 2017
Online Access:https://www.scopus.com/inward/record.uri?eid=2-s2.0-85014553885&doi=10.1080%2f01431161.2017.1296206&partnerID=40&md5=c41a65077a8f7c4fbc7f59ab0a7955ef
http://eprints.utp.edu.my/19498/
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spelling my.utp.eprints.194982018-04-20T06:03:40Z Scene classification for aerial images based on CNN using sparse coding technique Qayyum, A. Malik, A.S. Saad, N.M. Iqbal, M. Faris Abdullah, M. Rasheed, W. Rashid Abdullah, T.A. Bin Jafaar, M.Y. Aerial scene classification purposes to automatically label aerial images with specific semantic categories. However, cataloguing presents a fundamental problem for high-resolution remote-sensing imagery (HRRS). Recent developments include several approaches and numerous algorithms address the task. This article proposes a convolutional neural network (CNN) approach that utilizes sparse coding for scene classification applicable for HRRS unmanned aerial vehicle (UAV) and satellite imagery. The article has two major sections: the first describes the extraction of dense multiscale features (multiple scales) from the last convolutional layer of a pre-trained CNN models; the second describes the encoding of extracted features into global image features via sparse coding to achieve scene classification. The authors compared experimental outcomes with existing techniques such as Scale-Invariant Feature Transform and demonstrated that features from pre-trained CNNs generalized well with HRRS datasets and were more expressive than low- and mid-level features, exhibiting an overall 90.3 accuracy rate for scene classification compared to 85.4 achieved by SIFT with sparse coding. Thus, the proposed CNN-based sparse coding approach obtained a robust performance that holds promising potential for future applications in satellite and UAV imaging. © 2017 Informa UK Limited, trading as Taylor & Francis Group. Taylor and Francis Ltd. 2017 Article PeerReviewed https://www.scopus.com/inward/record.uri?eid=2-s2.0-85014553885&doi=10.1080%2f01431161.2017.1296206&partnerID=40&md5=c41a65077a8f7c4fbc7f59ab0a7955ef Qayyum, A. and Malik, A.S. and Saad, N.M. and Iqbal, M. and Faris Abdullah, M. and Rasheed, W. and Rashid Abdullah, T.A. and Bin Jafaar, M.Y. (2017) Scene classification for aerial images based on CNN using sparse coding technique. International Journal of Remote Sensing, 38 (8-10). pp. 2662-2685. http://eprints.utp.edu.my/19498/
institution Universiti Teknologi Petronas
building UTP Resource Centre
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Petronas
content_source UTP Institutional Repository
url_provider http://eprints.utp.edu.my/
description Aerial scene classification purposes to automatically label aerial images with specific semantic categories. However, cataloguing presents a fundamental problem for high-resolution remote-sensing imagery (HRRS). Recent developments include several approaches and numerous algorithms address the task. This article proposes a convolutional neural network (CNN) approach that utilizes sparse coding for scene classification applicable for HRRS unmanned aerial vehicle (UAV) and satellite imagery. The article has two major sections: the first describes the extraction of dense multiscale features (multiple scales) from the last convolutional layer of a pre-trained CNN models; the second describes the encoding of extracted features into global image features via sparse coding to achieve scene classification. The authors compared experimental outcomes with existing techniques such as Scale-Invariant Feature Transform and demonstrated that features from pre-trained CNNs generalized well with HRRS datasets and were more expressive than low- and mid-level features, exhibiting an overall 90.3 accuracy rate for scene classification compared to 85.4 achieved by SIFT with sparse coding. Thus, the proposed CNN-based sparse coding approach obtained a robust performance that holds promising potential for future applications in satellite and UAV imaging. © 2017 Informa UK Limited, trading as Taylor & Francis Group.
format Article
author Qayyum, A.
Malik, A.S.
Saad, N.M.
Iqbal, M.
Faris Abdullah, M.
Rasheed, W.
Rashid Abdullah, T.A.
Bin Jafaar, M.Y.
spellingShingle Qayyum, A.
Malik, A.S.
Saad, N.M.
Iqbal, M.
Faris Abdullah, M.
Rasheed, W.
Rashid Abdullah, T.A.
Bin Jafaar, M.Y.
Scene classification for aerial images based on CNN using sparse coding technique
author_facet Qayyum, A.
Malik, A.S.
Saad, N.M.
Iqbal, M.
Faris Abdullah, M.
Rasheed, W.
Rashid Abdullah, T.A.
Bin Jafaar, M.Y.
author_sort Qayyum, A.
title Scene classification for aerial images based on CNN using sparse coding technique
title_short Scene classification for aerial images based on CNN using sparse coding technique
title_full Scene classification for aerial images based on CNN using sparse coding technique
title_fullStr Scene classification for aerial images based on CNN using sparse coding technique
title_full_unstemmed Scene classification for aerial images based on CNN using sparse coding technique
title_sort scene classification for aerial images based on cnn using sparse coding technique
publisher Taylor and Francis Ltd.
publishDate 2017
url https://www.scopus.com/inward/record.uri?eid=2-s2.0-85014553885&doi=10.1080%2f01431161.2017.1296206&partnerID=40&md5=c41a65077a8f7c4fbc7f59ab0a7955ef
http://eprints.utp.edu.my/19498/
_version_ 1738656078686584832
score 13.211869

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