Geospatial Object Detection In Remote Sensing Images Based On Multi-Scale Convolutional Neural Networks
Qunli Yao, Xian Hu, Hong Lei · 2019
Automatic object detection is a basic but challenging problem in remote sensing images (RSIs) interpretation. Recently, a context-based top-down detection architecture has been proposed, which generates high-quality fusion features at all scales for object detection and significantly improves the accuracy of traditional detection framework. However, in the top-down architecture, small objects are easily lost in deep layers and the context cues will be weakened simultaneously. In this paper, to tackle these problems mentioned above, a novel Multi-scale Detection Network (MSDN) is proposed. The proposed method maintains the resolution of deep features, which enhances the capability of multi-scale objects feature expression. Meanwhile, a dilated bottleneck structure is introduced, which effectively enlarges the receptive filed and improves the regression ability of multi-scale objects. The proposed method is evaluated on NWPU VHR-10 benchmarks and achieves impressive improvement over the comparable state-of-the-art detection framworks.