Target Detection in Remote Sensing Image Based on Object-and-Scene Context Constrained CNN

Convolutional neural network (CNN) model has made a great breakthrough in target detection in remote sensing image due to the excellent feature extraction capability. However, diverse scenes and complex contextual information of remote sensing image make these CNN models face big challenges. For example, the distinctiveness between the target and the context would be reduced greatly. This letter proposes an object-and-scene context constrained CNN method to detect target in remote sensing image. This method has two channels, namely, object context constrained channel and scene context constrained channel. The object context constrained channel uses recurrent neural network (RNN) to explore the contextual relationship between the target and the object, including feature relationship and position relationship. The scene context constrained channel adopts priori scene information and Bayesian criterion to infer the relationship between the scene and the target, and it make full use of the scene information to enhance the target detection performance. The experimental results on two datasets demonstrate the robustness and effectiveness of the proposed method.