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Proceedings Paper

Multi-class object detection in remote sensing image based on context information and regularized convolutional network
Author(s): Bei Cheng; Zhengzhou Li; Qingqing Wu
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Paper Abstract

Multi-class objects detection in remote sensing image is attracting increasing attention recent years. In particular, the method based on deep learning has made outstanding achievements in the object detection. However, the deep network is easy to overfitting for the insufficient of remote sensing image dataset. What’s more, the current deep learning- based methods of object detection in remote sensing image usually ignore the context information of the objects. To cope with these problems, a novel object detection method based on regularized convolutional network and context information are proposed in this paper. A form of structured dropout method is used in convolutional layers to dropping continuous regions. To address the problem of lack of context, spatial recurrent neural networks are used to integrate the contextual information outside the region of interest. Comprehensive experiments in a public ten-class object detection data set show that the proposed object detection method has an outstanding detection accuracy under different scenarios.

Paper Details

Date Published: 31 January 2020
PDF: 6 pages
Proc. SPIE 11427, Second Target Recognition and Artificial Intelligence Summit Forum, 114271E (31 January 2020);
Show Author Affiliations
Bei Cheng, Chongqing Univ. (China)
Zhengzhou Li, Chongqing Univ. (China)
Qingqing Wu, Chongqing Univ. (China)


Published in SPIE Proceedings Vol. 11427:
Second Target Recognition and Artificial Intelligence Summit Forum
Tianran Wang; Tianyou Chai; Huitao Fan; Qifeng Yu, Editor(s)

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