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Open AccessArticle

Superpixel-Based Feature for Aerial Image Scene Recognition

Institute of Unmanned Systems, Beihang University, Beijing 100191, China
Key Laboratory of Advanced Technology of Intelligent Unmanned Flight System of Ministry of Industry and Information Technology, Beijing 100191, China
School of Electronic and Information Engineering, Beihang University, Beijing 100191, China
School of Automation Science and Electrical Engineering, Beihang University, Beijing 100191, China
Author to whom correspondence should be addressed.
Sensors 2018, 18(1), 156;
Received: 24 October 2017 / Revised: 22 December 2017 / Accepted: 4 January 2018 / Published: 8 January 2018
(This article belongs to the Special Issue Sensor Signal and Information Processing)
Image scene recognition is a core technology for many aerial remote sensing applications. Different landforms are inputted as different scenes in aerial imaging, and all landform information is regarded as valuable for aerial image scene recognition. However, the conventional features of the Bag-of-Words model are designed using local points or other related information and thus are unable to fully describe landform areas. This limitation cannot be ignored when the aim is to ensure accurate aerial scene recognition. A novel superpixel-based feature is proposed in this study to characterize aerial image scenes. Then, based on the proposed feature, a scene recognition method of the Bag-of-Words model for aerial imaging is designed. The proposed superpixel-based feature that utilizes landform information establishes top-task superpixel extraction of landforms to bottom-task expression of feature vectors. This characterization technique comprises the following steps: simple linear iterative clustering based superpixel segmentation, adaptive filter bank construction, Lie group-based feature quantification, and visual saliency model-based feature weighting. Experiments of image scene recognition are carried out using real image data captured by an unmanned aerial vehicle (UAV). The recognition accuracy of the proposed superpixel-based feature is 95.1%, which is higher than those of scene recognition algorithms based on other local features. View Full-Text
Keywords: superpixel-based feature; image scene recognition; aerial remote sensing superpixel-based feature; image scene recognition; aerial remote sensing
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MDPI and ACS Style

Li, H.; Shi, Y.; Zhang, B.; Wang, Y. Superpixel-Based Feature for Aerial Image Scene Recognition. Sensors 2018, 18, 156.

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