University of Oulu

U. Muhammad, W. Wang, A. Hadid and S. Pervez, "Bag of words KAZE (BoWK) with two-step classification for high-resolution remote sensing images," in IET Computer Vision, vol. 13, no. 4, pp. 395-403, 6 2019, doi: 10.1049/iet-cvi.2018.5069

Bag of words KAZE (BoWK) with two-step classification for high-resolution remote sensing images

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Author: Muhammad, Usman1,2; 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
3Yanbu University College, Saudi Arabia
Format: article
Version: accepted version
Access: open
Online Access: PDF Full Text (PDF, 4.3 MB)
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Language: English
Published: Institution of Electrical Engineers, 2019
Publish Date: 2020-06-08


The bag-of-words (BoW) model has been widely used for scene classification in recent state-of-the-art methods. However, inter-class similarity among scene categories and very high spatial resolution imagery makes its performance limited in the remote-sensing domain. Therefore, this research presents a new KAZE-based image descriptor that makes use of the BoW approach to substantially increase classification performance. Specifically, a novel multi-neighbourhood KAZE is proposed for small image patches. Secondly, the spatial pyramid matching and BoW representation can be adopted to use the extracted features and make an innovative BoW KAZE (BoWK) descriptor. Third, two bags of multi-neighbourhood KAZE features are selected in which each bag is regarded as separated feature descriptors. Next, canonical correlation analysis is introduced as a feature fusion strategy to further refine the BOWK features, which allows a more effective and robust fusion approach than the traditional feature fusion strategies. Experiments on three challenging remote-sensing data sets show that the proposed BoWK descriptor not only surpasses the conventional KAZE descriptor but also yields significantly higher classification performance than the state-of-the-art methods used now. Moreover, the proposed BoWK approach produces rich informative features to describe the scene images with low-computational cost and a much lower dimension.

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Series: IET computer vision
ISSN: 1751-9632
ISSN-E: 1751-9640
ISSN-L: 1751-9632
Volume: 13
Issue: 4
Pages: 395 - 403
DOI: 10.1049/iet-cvi.2018.5069
Type of Publication: A1 Journal article – refereed
Field of Science: 113 Computer and information sciences
213 Electronic, automation and communications engineering, electronics
Funding: The authors would like to acknowledge the world academy of sciences (TWAS) for giving an opportunity to conduct this research at University of Chinese Academy of Sciences.
Copyright information: © The Institution of Engineering and Technology 2019. The Definitive Version of Record is available online at: