Multi-resolution networks for ship detection in infrared remote sensing images

Ship detection is a hot point of remote sensing image processing and analysis. Most current methods merely utilize a single resolution image and cannot take full advantage of registered multi-resolution images. In this paper, we proposed a novel ship detection method for multi-resolution infrared remote sensing images based on convolutional neural networks. With a specially designed layer of various size kernels, our net takes in registered multi-resolution images simultaneously, extracting more robust features and gives a more accurate detection result. Besides, as ship targets only occupy a few pixels in an infrared remote sensing image, to handle the extreme background-foreground imbalance problem in infrared remote sensing images, we introduce the focal loss to train our net. Compared with classical methods, experiments on visible bands and infrared bands of Landsat-8 satellite images demonstrate the effectiveness of our method.