Multi-class object detection in remote sensing image based on context information and regularized convolutional network

Bei Cheng; Zhengzhou Li; Qingqing Wu

doi:10.1117/12.2551461

31 January 2020 Multi-class object detection in remote sensing image based on context information and regularized convolutional network

Bei Cheng, Zhengzhou Li, Qingqing Wu

Author Affiliations +

Proceedings Volume 11427, Second Target Recognition and Artificial Intelligence Summit Forum; 114271E (2020) https://doi.org/10.1117/12.2551461
Event: Second Target Recognition and Artificial Intelligence Summit Forum, 2019, Changchun, China

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.

Citation Download Citation

Bei Cheng, Zhengzhou Li, and Qingqing Wu "Multi-class object detection in remote sensing image based on context information and regularized convolutional network", Proc. SPIE 11427, Second Target Recognition and Artificial Intelligence Summit Forum, 114271E (31 January 2020); https://doi.org/10.1117/12.2551461

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