Contextual Region-Based Convolutional Neural Network with Multilayer Fusion for SAR Ship Detection

By Miao Kang, Kefeng Ji, Xiangguang Leng, Zhao Lin. Remote Sensing, 2017

332 citations recorded by the source corpus.

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.

Subfields: Aerospace Engineering, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/4f7441113a80dae8b392d52cc56adacde8092daa

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Aerospace Engineering

1. Richard Bamler
2. Xiaoxiang Zhu
3. Jun Shi
4. Xiaoling Zhang
5. Feng Xu
6. Shunjun Wei
7. Lichao Mou

In Computer Vision and Pattern Recognition

1. Lichao Mou
2. Xiaoxiang Zhu
3. Hao Su
4. Chao Wang
5. Jun Shi
6. Richard Bamler
7. Xiaoling Zhang
8. Mohsin Ali

Citations in the corpus

• A SAR Dataset of Ship Detection for Deep Learning under Complex Backgrounds (2019, background): “For example, [16,20] have at least 15% false positives, which seems too high to be practical in an operational system.”
• SAR Ship Detection Dataset (SSDD): Official Release and Comprehensive Data Analysis (2021, background): “[26] also further improved the network structure of Faster R-CNN, i.”
• Deep Learning Meets SAR: Concepts, models, pitfalls, and perspectives (2020, background): “[105] proposed a contextual region based network that fuses features from”
• Automatic Ship Detection Based on RetinaNet Using Multi-Resolution Gaofen-3 Imagery (2019, background): “[1] proposed a region-based convolutional neural network with contextual information and multilayer features for ship detection and combined the low-level high-resolution features and high-level semantic features to improve the detection accuracy and to remove some errors based on contextual informa”
• Dense Attention Pyramid Networks for Multi-Scale Ship Detection in SAR Images (2019, background): “The fusion of ship context information in the feature maps of SSD [41] can improve the accuracy of inshore ships detection.”