Intelligent Ship Detection in Remote Sensing Images Based on Multi-Layer Convolutional Feature Fusion

doi:10.3390/rs12203316

CORC > 长春光学精密机械与物理研究所 > 中国科学院长春光学精密机械与物理研究所

	Intelligent Ship Detection in Remote Sensing Images Based on Multi-Layer Convolutional Feature Fusion
	Y. L. Zhang,L. H. Guo,Z. F. Wang,Y. Yu,X. W. Liu and F. Xu
刊名	Remote Sensing
	2020
卷号	12 期号:20 页码:30
DOI	10.3390/rs12203316
英文摘要	Intelligent detection and recognition of ships from high-resolution remote sensing images is an extraordinarily useful task in civil and military reconnaissance. It is difficult to detect ships with high precision because various disturbances are present in the sea such as clouds, mist, islands, coastlines, ripples, and so on. To solve this problem, we propose a novel ship detection network based on multi-layer convolutional feature fusion (CFF-SDN). Our ship detection network consists of three parts. Firstly, the convolutional feature extraction network is used to extract ship features of different levels. Residual connection is introduced so that the model can be designed very deeply, and it is easy to train and converge. Secondly, the proposed network fuses fine-grained features from shallow layers with semantic features from deep layers, which is beneficial for detecting ship targets with different sizes. At the same time, it is helpful to improve the localization accuracy and detection accuracy of small objects. Finally, multiple fused feature maps are used for classification and regression, which can adapt to ships of multiple scales. Since the CFF-SDN model uses a pruning strategy, the detection speed is greatly improved. In the experiment, we create a dataset for ship detection in remote sensing images (DSDR), including actual satellite images from Google Earth and aerial images from electro-optical pod. The DSDR dataset contains not only visible light images, but also infrared images. To improve the robustness to various sea scenes, images under different scales, perspectives and illumination are obtained through data augmentation or affine transformation methods. To reduce the influence of atmospheric absorption and scattering, a dark channel prior is adopted to solve atmospheric correction on the sea scenes. Moreover, soft non-maximum suppression (NMS) is introduced to increase the recall rate for densely arranged ships. In addition, better detection performance is observed in comparison with the existing models in terms of precision rate and recall rate. The experimental results show that the proposed detection model can achieve the superior performance of ship detection in optical remote sensing image.
URL标识	查看原文
语种	英语
内容类型	期刊论文
源URL	[http://ir.ciomp.ac.cn/handle/181722/64624]
专题	中国科学院长春光学精密机械与物理研究所
推荐引用方式 GB/T 7714	Y. L. Zhang,L. H. Guo,Z. F. Wang,Y. Yu,X. W. Liu and F. Xu. Intelligent Ship Detection in Remote Sensing Images Based on Multi-Layer Convolutional Feature Fusion[J]. Remote Sensing,2020,12(20):30.
APA	Y. L. Zhang,L. H. Guo,Z. F. Wang,Y. Yu,X. W. Liu and F. Xu.(2020).Intelligent Ship Detection in Remote Sensing Images Based on Multi-Layer Convolutional Feature Fusion.Remote Sensing,12(20),30.
MLA	Y. L. Zhang,L. H. Guo,Z. F. Wang,Y. Yu,X. W. Liu and F. Xu."Intelligent Ship Detection in Remote Sensing Images Based on Multi-Layer Convolutional Feature Fusion".Remote Sensing 12.20(2020):30.