Analysis on Saliency Estimation Methods in High-Resolution Optical Remote Sensing Imagery for Multi-Scale Ship Detection

Ship detection is of considerable significance in both military and civilian application domains. Deep Convolutional Neural Network (DCNN) with region proposal mechanism, e.g., Faster R-CNN, performs outstandingly in ship detection with high-resolution images. However, the accuracy limitation is induced by the region proposal restricted by the training set for multi-scale target detection. Therefore, the method of multi-scale ship object detection is proposed based on saliency estimation in our work. Saliency Estimation Algorithms (SEAs) are often used to extract saliency features in images. In ship detection of high-resolution remote sensing images, these algorithms can extract information such as the scale and position of the targets, and then help the DCNN-based ship detection method to obtain better performance. To verify the effectiveness of the saliency estimation algorithm in multi-scale ship detection of high-resolution remote sensing images, and analyze the advantages of different SEAs. This paper introduces 13 classic saliency estimation algorithms and 2 DCNN-based ones to compare them by using evaluation indicators. At last, in order to demonstrate the performance of different SEAs, the extracted saliency feature maps are used to assist the DCNN-based target detection under multi-scale ships condition. In general, this framework can improve the detection accuracy of large-scale ships under scenarios of training only with small scale ships or without enough datasets.