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 deepnetwork is easy to overfitting for the insufficient of remote sensing image dataset. What’s more, the current deeplearning-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 contextinformation are proposed in this paper. A form of structured dropout method is used in convolutional layers to droppingcontinuous regions. To address the problem of lack of context, spatial recurrent neural networks are used to integrate thecontextual information outside the region of interest. Comprehensive experiments in a public ten-class object detectiondata set show that the proposed object detection method has an outstanding detection accuracy under different scenarios.
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