Contextual Region-Based Convolutional Neural Network with Multilayer Fusion for SAR Ship Detection
Contextual Region-Based Convolutional Neural Network with Multilayer Fusion for SAR Ship Detection
复制标题
用于 SAR 船舶检测的多层融合的基于上下文区域的卷积神经网络
DOI：
10.3390/rs9080860
复制
发表时间：
2017-08-01
期刊：
影响因子：
5
通讯作者：
Lin, Zhao
中科院分区：
文献类型：
--
作者：
Kang, Miao;Ji, Kefeng;Lin, Zhao
合成孔径雷达（SAR）舰船探测在海洋监测中发挥着越来越重要的作用。宽测绘带SAR图像中舰船信息的缺乏，给传统的舰船识别方法带来了困难。由于能够进行特征表示，深度神经网络最近在目标检测方面取得了巨大进展。然而，它们中的大多数遭受的小尺寸的目标的丢失检测，这意味着很少有他们能够直接用于SAR舰船检测任务。本文提出了一种用于SAR舰船检测的深度层次网络，即基于上下文区域的多层融合卷积神经网络，它由具有高分辨率的区域建议网络（RPN）和具有上下文特征的目标检测网络组成。所提出的方法不是使用来自单个层的低分辨率特征图来在RPN中生成建议，而是采用与缩小的浅层和上采样的深层相结合的中间层来产生区域建议。在目标检测网络中，区域建议被投影到具有感兴趣区域（ROI）池的多个层上，以提取对应的ROI特征和ROI周围的上下文特征。在归一化和重新缩放之后，它们随后被连接成一个集成的特征向量，用于最终输出。该框架融合了深层语义和浅层高分辨率特征，提高了小型船舶的检测性能。额外的上下文特征提供用于分类的补充信息，并帮助排除假警报。基于Sentinel-1数据集（包含27幅SAR图像和7986个标记舰船）的实验表明，该方法在SAR舰船检测中取得了良好的效果。
Synthetic aperture radar (SAR) ship detection has been playing an increasingly essential role in marine monitoring in recent years. The lack of detailed information about ships in wide swath SAR imagery poses difficulty for traditional methods in exploring effective features for ship discrimination. Being capable of feature representation, deep neural networks have achieved dramatic progress in object detection recently. However, most of them suffer from the missing detection of small-sized targets, which means that few of them are able to be employed directly in SAR ship detection tasks. This paper discloses an elaborately designed deep hierarchical network, namely a contextual region-based convolutional neural network with multilayer fusion, for SAR ship detection, which is composed of a region proposal network (RPN) with high network resolution and an object detection network with contextual features. Instead of using low-resolution feature maps from a single layer for proposal generation in a RPN, the proposed method employs an intermediate layer combined with a downscaled shallow layer and an up-sampled deep layer to produce region proposals. In the object detection network, the region proposals are projected onto multiple layers with region of interest (ROI) pooling to extract the corresponding ROI features and contextual features around the ROI. After normalization and rescaling, they are subsequently concatenated into an integrated feature vector for final outputs. The proposed framework fuses the deep semantic and shallow high-resolution features, improving the detection performance for small-sized ships. The additional contextual features provide complementary information for classification and help to rule out false alarms. Experiments based on the Sentinel-1 dataset, which contains twenty-seven SAR images with 7986 labeled ships, verify that the proposed method achieves an excellent performance in SAR ship detection.