University of Oulu

U. Muhammad, W. Wang and A. Hadid, "Feature Fusion with Deep Supervision for Remote-Sensing Image Scene Classification," 2018 IEEE 30th International Conference on Tools with Artificial Intelligence (ICTAI), Volos, 2018, pp. 249-253, https://doi.org/10.1109/ICTAI.2018.00046

Feature fusion with deep supervision for remote-sensing image scene classification

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Author: Muhammad, Usman1; Wang, Weiqiang1; Hadid, Abdenour2
Organizations: 1School of Computer and Control Engineering, University of Chinese Academy of Sciences Beijing, China
2Center for Machine Vision and Signal Analysis (CMVS), University of Oulu, Finland
Format: article
Version: accepted version
Access: open
Online Access: PDF Full Text (PDF, 0.7 MB)
Persistent link: http://urn.fi/urn:nbn:fi-fe2020042822713
Language: English
Published: Institute of Electrical and Electronics Engineers, 2018
Publish Date: 2020-04-28
Description:

Abstract

The convolutional neural networks (CNNs) have shown an intrinsic ability to automatically extract high level representations for image classification, but there is a major hurdle to their deployment in the remote-sensing domain because of a relative lack of training data. Moreover, traditional fusion methods use either low-level features or score-based fusion to fuse the features. In order to address the aforementioned issues, we employed a deep supervision (DS) strategy to enhance the generalization performance in the intermediate layers of the AlexNet model for remote-sensing image scene classification. The proposed DS strategy not only prevents from overfitting, but also extracts the features more transparently. Secondly, the canonical correlation analysis (CCA) is adopted as a feature fusion strategy to further refine the features with more discriminative power. The fused AlexNet features achieved by the proposed framework have much higher discrimination than the pure features. Extensive experiments on two challenging datasets: 1) UC MERCED data set and 2) WHU-RS dataset demonstrate that the two proposed approaches both enhance the performance of the original AlexNet architecture, and also outperform several state-of-the-art methods currently in use.

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Series: IEEE International Conference on Tools with Artificial Intelligence
ISSN: 1082-3409
ISSN-E: 2375-0197
ISSN-L: 1082-3409
ISBN: 978-1-5386-7449-9
ISBN Print: 978-1-5386-7450-5
Article number: 8576044
DOI: 10.1109/ICTAI.2018.00046
OADOI: https://oadoi.org/10.1109/ICTAI.2018.00046
Host publication: 30th International Conference on Tools with Artificial Intelligence, ICTAI 2018
Conference: International Conference on Tools with Artificial Intelligence
Type of Publication: A4 Article in conference proceedings
Field of Science: 113 Computer and information sciences
213 Electronic, automation and communications engineering, electronics
Subjects:
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