联合显著性特征与卷积神经网络的遥感影像舰船检测

余东行; 张保明; 郭海涛; 赵传; 徐俊峰

doi:10.11834/jig.180323

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联合显著性特征与卷积神经网络的遥感影像舰船检测
Joint salient feature and convolutional neural network for ship detection in remote sensing images
2018年23卷第12期页码：1947-1958
收稿：2018-05-18，

修回：2018-7-16，

纸质出版：2018-12-16
DOI： 10.11834/jig.180323
稿件说明：

移动端阅览

余东行, 张保明, 郭海涛, 赵传, 徐俊峰. 联合显著性特征与卷积神经网络的遥感影像舰船检测[J]. 中国图象图形学报, 2018,23(12):1947-1958. DOI： 10.11834/jig.180323.

Donghang Yu, Baoming Zhang, Haitao Guo, Chuan Zhao, Junfeng Xu. Joint salient feature and convolutional neural network for ship detection in remote sensing images[J]. Journal of Image and Graphics, 2018, 23(12): 1947-1958. DOI： 10.11834/jig.180323.

摘要

目的

针对高分辨率遥感影像舰船检测受云雾、海浪以及海岛等复杂因素干扰，存在虚警率高、漏检率高、目标检测和识别困难等问题，提出一种联合视觉显著性特征与卷积神经网络的海面舰船目标检测方法。

方法

基于频率域相位谱显著性检测能够有效抑制高分辨率遥感影像上云层、海面杂波干扰的特点，计算影像多尺度显著图并进行加权融合。采用对数变换对融合后的图像进行空间域灰度增强以提高目标与背景的区分度，利用灰度形态学闭运算填充舰船目标孔洞，采用大津分割法来提取疑似舰船目标作为兴趣区域。最后构建舰船样本库，利用迁移学习的思想训练卷积神经网络模型，对所有兴趣区域切片进行分类判断和识别，得到最终检测结果。

结果

利用多幅不同背景下的高分辨率遥感影像，分别从视觉显著性检测、舰船粗检测与船只类型识别3个方面进行实验验证，选取检测率、虚警率、识别率3个指标进行定量评价。结果表明，本文方法相比于其他方法能有效排除云雾、海岛等多种因素的干扰，检测率、虚警率、识别率分别为93.63%、3.01%、90.09%，明显优于其他算法，能够实现大范围影像上多种类型舰船的快速准确检测和识别。

结论

本文将图像视觉显著性检测快速获取图像显著目标的特点与卷积神经网络在图像分类的优势相结合，应用于遥感影像的海域舰船目标检测，能够实现对复杂背景下舰船目标的检测和船只类型的精细化识别。

Abstract

Objective

Ships and warships are important sea-based transportation carriers and military targets. Thus

detecting and recognizing these targets in high resolution remote sensing images are of substantial practical significance to. However

satellite imaging can be affected by weather

illumination

cloud

and atmosphere scattering. In addition

the targets in the image can be disturbed by sea clutters and other objects

which render the ship detection and recognition increasingly difficult. The majority of ship recognition algorithms typically adopt low-level features

such as shape

invariant moment

and histogram of gradient (HOG)

which are simple but not robust to disturbances

such as waves

clouds

and islands. In general

handcraft features can only be used to distinguish ships from other interferences on the sea surface and have weak ability to differentiate various types of ship. In view of the above mentioned problems

this study proposes a new method that combines salient features and a convolutional neural network to recognize ships in remote sensing images.

Method

The proposed method consists of three parts

namely

image pre-processing

ship pre-detection

and ship recognition. First

the image can be enhanced by a homomorphic filter to improve the texture clarity and contrast in the pre-processing phase

which is helpful for the detection and recognition of the subsequent phase. In the ship detection stage

the saliency map of images can be calculated by phase spectrum of Fourier transform (PFT)

which is a technique based on the analysis of the frequency domain. To take account of different resolutions

the multi-scale saliency maps are fused. The PFT method can effectively suppress the interference of cloud and sea wave

but the distinction between background and ship is barely notable. To solve this problem

logarithmic transformation is utilized to enhance the saliency map. Then

the gray morphological operation of close is adapted to eliminate the noise areas and fill holes

and the image segmentation algorithm of Otsu is used to extract all salient areas as areas of interest. In the stage of recognition

a deep convolutional neural network (CNN) can be well trained with a small number of ship samples based on the concept of transfer learning. All areas of interest can be finally classified and recognized by the CNN.

Result

To verify the effectiveness of the proposed algorithm

experiments were conducted on remote sensing images with varying backgrounds. The experiments were conducted in three aspects

namely

visual saliency detection

ship detection

and ship recognition. Three kinds of indicators

namely

detection

false alarm

and recognition rates were used to quantify experimental results. The qualitative results indicate that saliency detection based on PFT can effectively restrain the disturbance of sea surface and clutter

in which logarithmic transformation substantially improves the integrity of the ships' contour. Quantitative analysis shows that the three indexes of the proposed method are 93.63%

3.01%

and 90.09%

respectively

which are extensively better than the compared algorithms.

Conclusion

Visual saliency detection is one of the commonly used and effective methods for ship detection. This paper combined the advantages of visual salient features and convolutional neural network for ship recognition in remote sensing images. The method can realize the rapid detection of ship targets with high accuracy of classification in complex backgrounds.

关键词

Keywords

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