A Patch-to-Pixel Convolutional Neural Network for Small Ship Detection With PolSAR Images
Kan Jin, Yilun Chen, Bin Xu, Junjun Yin, Xuesong Wang, Jian Yang
Abstract
Compared with optical images, polarimetric synthetic aperture radar (PolSAR) images usually maintain lower resolution. Ship targets in PolSAR images have fewer pixels than those in optical images. Therefore, architectures such as Faster R-CNN and its variations that focus more on target pixels may fail for PolSAR images. Due to limited data and the simple geometric structures of small ship targets, the R-CNN framework with a large neural network as its backbone easily overfits the training set. In this article, we propose a novel lightweight patch-to-pixel convolutional neural network (P2P-CNN) for ship detection via PolSAR images. P2P-CNN focuses on both the target and its surroundings. A patch of proper size that contains both a target and its surroundings is used as the input of the neural network to determine whether the pixel in the center of the patch belongs to the target. To utilize contextual semantic information at all scales, all feature maps from the top down are combined to improve the final result. Instead of conventional convolution, dilated convolutions are used in the proposed neural network to exponentially expand receptive fields without adding any model parameters. The proposed approach and comparative methods are tested and compared on PolSAR images in various environments under varying image resolution, target size, sea condition, sensor type, etc. The experimental results demonstrate that the proposed approach outperforms all the compared methods.