光学遥感图像目标检测方法

更快速区域卷积神经网络(faster region-based convolutional neural network,Faster RCNN)是两阶段的目标检测模型,通过区域生成网络将区域提议与识别完全融合到网络模型中,使主要的运算可以在图形处理器中完成,因此,其同时具有良好的检测速度与精度。但是当Faster RCNN直接应用于遥感图像目标检测,面对宽尺寸范围的多种目标时,性能受到了很大削弱。分析了池化操作和目标尺寸对区域提议的影响,提出联合多层次特征进行区域提议的方法,提升了目标区域的提议召回率。针对性地优化前景样本的生成策略,避免训练过程中的产生无效前景样本,使得整个检测模型的训练更加高效。实验结果表明,所提出的模型和训练方法能够提高多尺度遥感图像目标的召回率与检测精度,且具备较高的训练效率。

Target detection method for optical remote sensing imagery

Faster region-based convolutional neural network (Faster RCNN), a two-stage object detection model, proposes candidate regions through a region proposal network and coalesces the two stages of region proposal and classification to a single network, which makes most computation be conducted in the graphic processing unit. Therefore, it has both high detection speed and accuracy on many public datasets. However, when we directly use Faster RCNN to conduct target detection on the remote sensing imageries, its performance is not ideal enough. In this work, the influences of pooling and target scale on region proposal are analyzed, and an optimized region proposal network is proposed to address the problems in the region proposal process. In addition, an individualized generation strategy for training samples which can avoid the generation of invalid foreground samples is introduced to speed up the training process. We evaluate the proposed detection method on the remote sensing image dataset. The result shows that the proposed model improves the recall rate and detection accuracy of multi-scale targets. Moreover, the experiments also demonstrate that the training of the model is rapid and high-efficient.