Multi-Scale Ships Detection in High-Resolution Remote Sensing Image Via Saliency-Based Region Convolutional Neural Network
Zezhong Li, Yanan You, Fang Liu · 2019
Ship detection is of great significance in both military and civilian application domains. Deep Convolutional Neural Network (DCNN) method with region proposal, e.g. Faster R-CNN, achieves ship detection well. However, for multi-scale target detection in high-resolution remote sensing image, the limitation of accuracy is induced by the region proposal restricted by the training set. Therefore, the mechanism of multi-scale ship detection based on saliency estimation is proposed in our work. Firstly, a saliency estimation algorithm is used to distinguish which image contains large ships and the image pyramid for each input one is established. Then, using a target detection network in different scales of images. The results are merged at the end of network. Finally, accuracy and validity are verified by real data processing.