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An active region corrected method for weakly supervised aircraft detection in remote sensing images
Author(s): Jian Xu; Shouhong Wan; Peiquan Jin; Qijun Tian
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Paper Abstract

Aircraft detection is a challenging task in remote sensing images which attract increasing attention in recent years. Existing methods based on fully-supervised convolutional neural networks (CNN) require expensive labeling information such as bounding box, which is time consuming and difficult to obtain. Recently, weakly supervised methods only using image-level labels has drawn increasing attention in natural imagery. An approach called class activation map (CAM) based on weakly supervised performs well in natural scene images for object detection, but there is a problem when using it in remote sensing images: inaccurate localization. In this paper, we propose a method called Active Region Corrected (ARC) to locate aircraft accurately. We find that generating the localization map in the classified network by extracting the feature before the last pooling layer contains more accurate position information but a lot of noise, and then we use the CAM to generate a localization map which contains rough location information of aircraft. Combining these two localization maps we can get the exact position of the aircraft. Experiments conducted on data set verify that our proposal obtains a superior performance on aircraft detect and localization in remote sensing images.

Paper Details

Date Published: 14 August 2019
PDF: 5 pages
Proc. SPIE 11179, Eleventh International Conference on Digital Image Processing (ICDIP 2019), 111792H (14 August 2019); doi: 10.1117/12.2539663
Show Author Affiliations
Jian Xu, Univ. of Science and Technology of China (China)
Shouhong Wan, Univ. of Science and Technology of China (China)
Peiquan Jin, Univ. of Science and Technology of China (China)
Qijun Tian, Univ. of Science and Technology of China (China)


Published in SPIE Proceedings Vol. 11179:
Eleventh International Conference on Digital Image Processing (ICDIP 2019)
Jenq-Neng Hwang; Xudong Jiang, Editor(s)

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