Scene classification for aerial images based on CNN using sparse coding technique
Antennas; Classification (of information); Codes (symbols); Convolution; Image classification; Neural networks; Remote sensing; Satellite imagery; Semantics; Unmanned aerial vehicles (UAV); Convolutional Neural Networks (CNN); Future applications; High resolution remote sensing imagery; Mid-level fe...
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2023
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my.uniten.dspace-232272023-05-29T14:38:35Z 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. 57211138712 12800348400 56567441400 54386959400 57188825497 24475459400 56594684600 57193519737 Antennas; Classification (of information); Codes (symbols); Convolution; Image classification; Neural networks; Remote sensing; Satellite imagery; Semantics; Unmanned aerial vehicles (UAV); Convolutional Neural Networks (CNN); Future applications; High resolution remote sensing imagery; Mid-level features; Multi-scale features; Robust performance; Scale invariant feature transforms; Scene classification; Image coding; aircraft; artificial neural network; image classification; satellite imagery; unmanned vehicle 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. Final 2023-05-29T06:38:35Z 2023-05-29T06:38:35Z 2017 Article 10.1080/01431161.2017.1296206 2-s2.0-85014553885 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85014553885&doi=10.1080%2f01431161.2017.1296206&partnerID=40&md5=c41a65077a8f7c4fbc7f59ab0a7955ef https://irepository.uniten.edu.my/handle/123456789/23227 38 8-Oct 2662 2685 Taylor and Francis Ltd. Scopus |
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Antennas; Classification (of information); Codes (symbols); Convolution; Image classification; Neural networks; Remote sensing; Satellite imagery; Semantics; Unmanned aerial vehicles (UAV); Convolutional Neural Networks (CNN); Future applications; High resolution remote sensing imagery; Mid-level features; Multi-scale features; Robust performance; Scale invariant feature transforms; Scene classification; Image coding; aircraft; artificial neural network; image classification; satellite imagery; unmanned vehicle |
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57211138712 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 |
| author |
Qayyum A. Malik A.S. Saad N.M. Iqbal M. Faris Abdullah M. Rasheed W. Rashid Abdullah T.A. Bin Jafaar M.Y. |
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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_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 |
2023 |
| _version_ |
1806427611472068608 |
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13.214268 |