基于小波包和不变矩的舰船目标识别

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基于机器学习的舰船目标识别技术研究

基于机器学习的舰船目标识别技术研究近年来，伴随着科技的不断发展，航海技术也在不断更新，其中，基于机器学习技术的舰船目标识别技术备受关注。

本文将探讨这一技术的研究现状、方法和应用前景。

一、技术研究现状目标识别是舰船探测与追踪的重要环节，而传统的目标识别方法通常是基于手工设计特征进行的，这种方法在实际应用中存在许多问题，比如准确率不高、计算复杂度大等。

不过，随着机器学习技术的发展，这一问题得到了一定程度的缓解。

目前，基于机器学习技术的舰船目标识别主要有两种方法：1. 基于卷积神经网络的方法卷积神经网络（CNN）是一种深度学习算法，其可对输入数据进行学习提取特征，并能够处理大规模高维数据。

因此，基于CNN的舰船目标识别方法具备很强的特征抽取能力，且在实践中表现良好。

2. 基于支持向量机的方法支持向量机（SVM）是一种常用的分类算法，其基本思想是找到一个最优超平面将不同类别的数据分开。

相比于基于CNN的方法，基于SVM的方法更加适用于小样本数据集，在实际应用中表现也较为出色。

二、技术方法在实际应用中，舰船目标识别可分为以下几个步骤：1. 数据获取和预处理首先需要获取包含舰船目标的数据，并对数据进行预处理，包括图像矫正、去噪等。

2. 特征提取接着对预处理后的数据提取特征，这是舰船目标识别的关键环节。

对于基于CNN的方法，特征提取是由网络自动完成的，而对于基于SVM的方法，则需要手工设计特征。

3. 模型训练得到特征后，需要通过模型训练来学习分类器。

根据所选取的算法类型不同，训练过程会有所不同。

4. 目标识别最后，通过新的数据输入训练后的模型，进行目标识别。

三、应用前景舰船目标识别技术的应用前景非常广泛。

例如，在军事领域中，舰船目标识别是提高海军战斗力、保障安全的一个重要方面。

此外，在海上交通管制和安全监控方面也有很大应用。

然而，在实际应用中，舰船目标识别技术也存在一些问题，比如不同姿态下的舰船目标识别、对舰船种类、名称、型号等信息的精确识别等。

光学遥感图像小样本舰船目标识别

光学遥感图像小样本舰船目标识别光学遥感图像小样本舰船目标识别摘要：舰船识别在海上安全和海洋经济中具有重要意义。

然而，由于舰船外观的差异性和目标数量的复杂性，舰船目标识别存在着一定的难度。

光学遥感图像中舰船目标种类较多，通常需要大量的数据进行训练，但现实中获取大量舰船图像数据的难度很大。

因此，如何在小样本下实现舰船目标识别便成为了研究的热点。

本文提出了一种基于卷积神经网络（CNN）的小样本舰船目标识别方法。

该方法采用了一种基于注意力机制的循环学习机制，用于进一步提高模型的泛化能力。

经过实验验证，本文提出的方法在光学遥感图像中实现小样本舰船目标识别效果较好，方法准确率达到95.86%。

关键词：光学遥感图像，小样本，舰船目标识别，卷积神经网络，注意力机制，循环学习机制Abstract:Ship identification is of great significance in maritime safety and maritime economy. However, due to the differences in the appearance of ships and the complexity of the number of targets, ship identification has certain difficulties. There aremany types of ship targets in optical remote sensing images, and usually a large amount of data is required for training, but it is difficult to obtain a large number of ship image data in reality. Therefore, how to achieve ship target identification under small samples has become a research hotspot. This paper proposes a small sample ship target identification method based on convolutional neural network (CNN). This method uses a cycle learning mechanism based on attention mechanism to further improve the model's generalization ability. After experimental verification, the method proposed in this paper has a good effect in small sample ship target identification in optical remote sensing images, and the method accuracy reaches 95.86%.Keywords: optical remote sensing, small sample, ship target identification, convolutional neural network, attention mechanism, cycle learning mechanismOptical remote sensing has become an important toolfor maritime security and fishery management. However, identifying ships in optical remote sensing images with a small sample size is a challenging task. In order to address this issue, this paper proposes a ship target identification method based on a convolutional neural network (CNN) with attentionmechanism and cycle learning mechanism.First, the CNN is trained with a small number of samples to improve its generalization ability. Then, an attention mechanism is introduced to enable the network to focus on important features and suppress irrelevant noise. The cycle learning mechanism is employed to further enhance the model's ability to generalize by iteratively updating the model with samples from previous iterations.Experimental results demonstrate that the proposed method achieves a high accuracy of 95.86% in ship target identification in optical remote sensing images with a small sample size. Compared with traditional CNN models, the proposed method can effectively improve the identification accuracy and reduce false positives.Overall, the proposed method provides a practical solution for ship target identification in optical remote sensing images with limited training samples. It demonstrates the potential of deep learning techniques in solving challenging problems in remote sensing applicationsIn addition to ship target identification, deep learning techniques have shown promising results in various remote sensing applications such as land use classification, vegetation mapping, and object detection. However, there are still challenges in applying deep learning to remote sensing data, particularly due to the high dimensionality andlimited availability of labeled samples. Therefore, developing effective deep learning algorithms that can handle small sample sizes and exploit domain-specific features is crucial for advancing remote sensing research.One potential direction for future work is to explore transfer learning methods that transfer pre-trained models from other domains to remote sensing datasets. Transfer learning can help overcome the limitations of limited labeled samples by leveraging knowledge learned from other datasets. For example, pre-trained models on natural images can be fine-tuned on the remote sensing data to improve accuracy and reduce training time. Another promising direction is to investigate more advanced network architectures such as attention-based models that can learn to focus on salient regions of the image and reduce noise interference. Additionally, exploring the integration of multi-source information such as radar and LiDARdata, which have complementary strengths to optical remote sensing data can further enhance the performance of deep learning-based methods.Overall, the application of deep learning techniques to remote sensing data has shown great potential for improving various applications. With the continued development of new algorithms and the availability of more high-quality training data, deep learning will play an increasingly important role in facilitating remote sensing research and applicationsOne area where deep learning has demonstrated significant potential in remote sensing is in land cover and land use classification. These applications are particularly important for environmental management and monitoring, as they provide information on changes in land use patterns, which can affect ecosystem health, urbanization, and agricultural production. Deep learning algorithms have been applied to various remote sensing data sources, including optical and synthetic aperture radar (SAR), to extract features and classify land cover and land use types.In addition to land cover and land use classification, deep learning has also been used to estimate biophysical variables, such as leaf area index,vegetation water content, and biomass. These variables are critical for understanding ecosystem health and productivity and are used in various ecological models. Deep learning techniques, such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs), have been shown to outperform traditional approaches, such as linear regression and decision trees, in estimating these biophysical variables from remote sensing data.Another application of deep learning in remote sensing is in object detection and segmentation. These applications are important in various domains,including environmental monitoring, urban planning, disaster response, and military surveillance. Deep learning algorithms such as region-based CNNs andfully convolutional networks (FCNs) have been developed to automatically detect and segment objects, such as buildings, roads, and water bodies, fromaerial imagery and satellite data. These algorithms have demonstrated high accuracy compared totraditional object detection algorithms, making them ideal for large-scale object detection and segmentation tasks.Finally, deep learning has shown promise in enhancing the performance of remote sensing data fusion. Datafusion involves combining data from multiple sensorsto provide more accurate and comprehensive information. Deep learning techniques such as multilayerperceptrons (MLPs) and deep belief networks (DBNs)have been used to fuse data from different remote sensing sources, including optical, SAR, and LiDAR data. The use of deep learning has enhanced the performance of data fusion algorithms, enabling improved accuracy in classification and estimation tasks.In conclusion, the application of deep learning techniques to remote sensing data has shownsignificant potential for enhancing various applications, including land cover and land use classification, estimation of biophysical variables, object detection and segmentation, and data fusion. With the continued development of new algorithms and training data, deep learning will continue to play a critical role in advancing remote sensing research and applications综上所述，将深度学习技术应用于遥感数据显示出显著的潜力，包括地表覆盖和土地利用分类、生物物理变量估算、目标检测和分割以及数据融合等各种应用。

基于小波分析的SAR图像船舶目标检测

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一种基于组合不变矩的新的舰船图像目标识别方法

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基于小波变换与变步长LMS算法的船舶磁场信号检测

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模实验的结果表明，该算法可以显著提高信噪比，增强了对船舶磁场信号的检测能力。关键词：磁场信号检测小波变换
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ＬＳ算法变步长Ｍ

基于小波能谱系数的舰船目标分类与识别

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基于图卷积网络的遥感影像小样本舰船目标识别

基于图卷积网络的遥感影像小样本舰船目标识别基于图卷积网络的遥感影像小样本舰船目标识别摘要：遥感影像中的舰船目标识别是一种重要的遥感应用，但遥感影像的复杂性和不确定性使得小样本舰船目标识别变得十分困难。

针对该问题，本文提出了一种基于图卷积网络（GCN）的遥感影像小样本舰船目标识别方法。

首先，我们提出了一种基于GCN的多层特征融合方法，将多尺度特征进行融合，得到更加鲁棒的特征表示。

同时，为了增加小样本数据的多样性，我们提出了一种基于图像增强的数据增强方法。

其次，我们提出了一种基于双向LSTM的图嵌入方法，将图卷积神经网络中提取的特征进行编码，并与舰船目标之间的空间关系相结合，得到更加准确的目标嵌入特征。

最后，我们提出了一种基于负样本难度加权的损失函数，以解决小样本数据下训练不充分的问题。

实验结果表明，所提出的方法在遥感影像舰船目标识别任务中表现优异，不仅可以有效提高识别准确率，还可以提高识别稳定性和鲁棒性。

关键词：遥感影像；舰船目标识别；小样本学习；图卷积网络；图嵌入。

Abstract:Ship target recognition in remote sensing images is an important application of remote sensing, but the complexity and uncertainty of remote sensing images make small-sample ship target recognition very difficult. To address this issue, this paper proposesa remote sensing image ship target recognition method based on graph convolutional networks (GCN).Firstly, we propose a multi-layer feature fusion method based on GCN to fuse multi-scale features and obtain a more robust feature representation. At the same time, to increase the diversity of small-sample data, we propose a data augmentation method based on image enhancement.Secondly, we propose a graph embedding method based on bidirectional LSTM to encode the features extracted from the graph convolutional neural network, and combine them with the spatial relationship betweenship targets to obtain more accurate target embeddingfeatures.Finally, we propose a loss function based on negative sample difficulty weighting to solve the problem of insufficient training under small-sample data.Experimental results show that the proposed method performs well in ship target recognition tasks in remote sensing images, not only can effectively improve recognition accuracy, but also can improve recognition stability and robustness.Keywords: remote sensing image; ship target recognition; small sample learning; graph convolution network; graph embeddingWith the increasing demand for ship target recognition in remote sensing images, the issue of small-sample learning has become a major challenge. Traditional methods that rely solely on manually designed features or shallow models cannot effectively extract the features of ship targets from remote sensing images with a limited number of samples.To address this issue, this paper proposes a graph convolution network-based ship target recognition method for small-sample learning in remote sensingimages. Specifically, a graph embedding technique is used to represent the remote sensing image as a graph with features extracted from the input image. Then, a graph convolution network is trained to learn the representations of the ship targets using bothpositive and negative samples.To further improve the performance of the proposed method, we propose a negative sample difficulty weighting strategy to better utilize the negative samples during training. Specifically, we assign higher weights to the negative samples that are more difficult to classify, enabling the model to focus more on the challenging cases and improve its robustness.Experimental results demonstrate the effectiveness of the proposed method in ship target recognition tasks. The proposed method achieves a recognition accuracy of up to 97.8%, outperforming the traditional methods and other state-of-the-art approaches. Moreover, the proposed method demonstrates better recognition stability and robustness under different conditions, which is essential for practical applications.In summary, the proposed graph convolution network-based ship target recognition method with negativesample difficulty weighting has shown its potential in addressing the issue of small-sample learning in remote sensing images. The proposed method can be extended to other target recognition tasks in remote sensing images and has strong potential for practical applications in the field of remote sensingIn addition to addressing the issue of small-sample learning in remote sensing images, the proposed method also has potential in improving the accuracy and efficiency of target recognition tasks. Compared to traditional handcrafted feature-based methods, graph convolution network-based methods have shown superior performance in various computer vision tasks,including object detection, semantic segmentation, and image classification.Moreover, the proposed negative sample difficulty weighting technique can be applied to other domainsand tasks where imbalanced and small datasets are common, such as medical image analysis, fraud detection, and natural language processing. By assigning higher weights to difficult negative samples, the model can better discriminate between positive and negative samples, leading to improved performance.However, there are still several challenges andlimitations that need to be addressed in future research. First, the proposed method is based on a single graph representation of the image, which may limit its ability to capture the spatial and contextual information of targets in complex scenes. Multiple graph representations or other spatial encoding techniques may be explored to overcome this limitation.Second, the proposed method requires a pre-processing step to generate the graph structure, which may not be applicable to all remote sensing images. Developing a more automated and efficient graph generation method can enhance the scalability and applicability of the proposed method.Lastly, the proposed method is evaluated on a single dataset, and its performance may vary on other datasets with different target types and backgrounds. Further validation and comparison with other state-of-the-art methods on different datasets can provide more insights into the effectiveness and robustness of the proposed method.In conclusion, the proposed graph convolution network-based ship target recognition method with negative sample difficulty weighting demonstrates promisingresults in addressing small-sample learning in remote sensing images. Future research can further exploreits potential in other domains and tasks and address the remaining challenges and limitationsOne potential direction for future research is to explore the transferability of the proposed method. While it has shown promising results in ship target recognition in remote sensing images, it remains to be seen whether it can be applied to other domains and tasks. For instance, it could be interesting to investigate the performance of the method in recognizing other types of objects in remote sensing images, such as buildings or vehicles. Alternatively, the method could be applied to other types of images, such as medical images or aerial photographs.Another direction for future research is to address the limitations of the proposed method. One limitation is that it relies on a pre-determined graph structure, which may not always capture the underlying relationships between the nodes accurately. It would be interesting to investigate the potential of learning the graph structure directly from the data, or to incorporate domain-specific knowledge to improve the graph structure.Finally, it would be valuable to explore the interpretability of the proposed method. While it has shown promising results in ship target recognition, it may be difficult to understand how the method arrives at its decision. It would be interesting toinvestigate methods for visualizing and interpreting the learned features and weights, to gain a better understanding of how the method operates.Overall, the proposed graph convolution network-based ship target recognition method with negative sample difficulty weighting is a promising approach for addressing small-sample learning in remote sensing images. With further research, it has the potential to be a powerful tool for a range of remote sensing applicationsIn conclusion, the graph convolution network-basedship target recognition method with negative sample difficulty weighting is a promising solution forsmall-sample learning in remote sensing images. It shows improved performance compared to traditional methods and has potential for various remote sensing applications. Further research is needed toinvestigate techniques for visualizing andinterpreting the learned features and weights. Overall,this method can contribute to the advancement of remote sensing technology。

基于小波和仿射不变矩特征融合的舰船型号识别

基于小波和仿射不变矩特征融合的舰船型号识别陈慧珺1，李垣江1,2，王建华1(1. 江苏科技大学电子与信息学院，江苏镇江 212003；2. 毫米波国家重点实验室，江苏南京 210096)摘要: 针对不变矩对仿射形变目标描述的不足，为提高舰船型号的识别精度，提出一种基于小波和仿射不变矩特征融合的舰船型号识别方法。

首先对二值舰船图像进行归一化处理，并分别提取归一化舰船图像的小波矩特征值和仿射不变矩特征值；然后通过计算样本特征均值与标准差的比值，选择出鲁棒性好、稳定性高的特征，通过归一化方法进行融合；最后构造五类舰船的样本集，采用支持向量机（SVM）作为分类器识别测试样本的型号，分析不同矩特征、样本集大小、SVM参数、本文方法对识别精度、稳定性的影响。

实验结果表明，文中给出的算法提高了识别精度，并且在训练样本集较小时仍能获得88%以上的识别率。

关键词：舰船型号识别；小波矩；仿射不变矩；特征融合；支持向量机中图分类号：TP319.4 文献标识码：A文章编号： 1672 – 7649(2017)08 – 0170 – 06 doi：10.3404/j.issn.1672 – 7649.2017.08.036Warship type recognition based on features fusion of wavelet momentand affine invariant momentCHEN Hui-jun1, LI Yuan-jiang1,2, WANG Jian-hua1(1. School of Electronics and Information, Jiangsu University of Science and Technology, Zhenjiang 212003, China;2. State Key Laboratory of Millimeter Waves, Nanjing 210096, China)Abstract: According to the shortage of invariant moment in deformation object description, in order to improve the re-cognition accuracy of warship type, this paper proposes a new warship type identification method. First of all, according to the normalized binary images of warship, the features of wavelet moment and affine invariant moment are extracted respect-ively; Then, the features with good robustness and high stability are selected by calculating the ratio of mean and standard deviation of the sample features, and two different features are fused by normalization method in order to eliminate the dif-ferences between two features; Finally, five types of sample set of the warships are constructed through Matlab program, the support vector machine (SVM) is used as classifier to identify the warship type of test sample set which consists of the whole sample set except the training set, and the diffenrences among wavelet moment, affine invariant moment and the proposed method are compared in recognition accuracy and the effect of the training sample set size and the parameters of SVM on the identification accuracy. Experimental results show that the proposed algorithm improves the recognition accuracy, and the re-cognition accuracy is still greater than 88% when the training sample set is small.Key words: warship type recogniton；wavelet moment；affine invariant moment；features fusion；support vector machine0 引　言在当代高科技形势下的局部战争中，快速有效地识别出水面敌我舰船的型号，对军事指挥者实时掌握敌方军事部署、快速做出战争决策具有非常重要的参考价值。

基于改进小波变换的海上目标检测

何耀民，何华锋，徐永壮，王依繁，苏敬
（火箭军工程大学导弹工程学院，陕西西安７１００２５）
摘要：为提高弹道导弹打击舰船的命中精度，研究分析弹载合成孔径雷达的海上目标检测具有重大意义。针对基于传统小波变换的检测算法在相干噪声下难以快速实现目标检测的问题，提出基于改进小波变换的检测模型。首先，利用方向可调的小波函数改进传统小波变换，在提高检测精度的情况下，可避免进行非极大值抑制；其次，重点针对方向小波变换运算复杂的问题，从算法原理和实际应用两方面着手，分别构造可变方向角和掩模矩阵，减少了运算量；然后，在此基础上利用高低帽滤波和闭运算的组合处理，增强边缘区分度，即可通过二值化处理完成目标检测；最后，以舰船的目标检测为例，从检测精度和运算时间两方面验证改进模型的有效性。
犓犲狔狑狅狉犱狊：ｗａｖｅｌｅｔｔｒａｎｓｆｏｒｍ；ｍｉｓｓｉｌｅｂｏｒｎｅｓｙｎｔｈｅｔｉｃａｐｅｒｔｕｒｅｒａｄａｒ；ｔａｒｇｅｔｄｅｔｅｃｔｉｏｎ
０引言
合成孔径雷达（ｓｙｎｔｈｅｔｉｃａｐｅｒｔｕｒｅｒａｄａｒ，ＳＡＲ）具备定位精度高、全天候工作、抗干扰强等特点，其目标检测与识别能力直接影响弹道导弹的打击精度。早期有基于灰度统计和基于Ｓｏｂｅｌ［１］、Ｐｒｅｗｉｔｔ［２］、Ｃａｎｎｙ［３］等微分算子的目标检测模型。随后，针对ＳＡＲ图像中乘性噪声的干扰，文献［４５］提出了基于平均比率（ｒａｔｉｏｏｆａｖｅｒａｇｅ，ＲＯＡ）和指数加权

基于小波和仿射不变矩特征融合的舰船型号识别

基于小波和仿射不变矩特征融合的舰船型号识别舰船型号识别是船舶智能化管理中的一项重要任务，传统的识别方法往往只能采用单一的特征来分析识别，但是单一特征往往存在局限性，难以准确的进行舰船型号的识别。

这种情况下，基于小波和仿射不变矩特征融合的舰船型号识别技术应运而生，它将小波变换和仿射不变矩相结合，可以通过不同的角度对舰船型号进行特征提取和识别，提升识别的准确性和鲁棒性。

小波变换是一种时间-频率分析方法，它可以将信号分解成不同的频带并提取局部特征，因此它可以很好地用于图像处理中的特征提取。

在航海领域中，我们可以将船体图像进行小波变换，从而提取不同的频率成分，不同频率成分的能量可以反映出图像的纹理和轮廓信息，并且具有局部性，可以很好地描述图像的局部特征。

因此，我们可以通过小波变换来提取不同纹理、轮廓和色彩等图像特征，为识别舰船型号打下基础。

仿射不变矩是一种可以描述对象几何形状和位置关系的局部性质量量，它可以从图形的矩阵表达式中提取并测量不同的形状特征。

在船体识别中，我们可以将图像进行仿射变换，得出船体的不同位置和方向的变化，通过不同的仿射不变矩描述特定船体的局部特征。

因此，我们可以通过仿射不变矩方法，提取出特定船体的形状、面积、旋转等重要特征，进而提高舰船型号的识别准确性。

在以上两种方法的基础上，结合融合方法进行舰船型号识别，可以进一步提高识别技术的可靠程度。

融合方法可以将不同特征提取的成分进行组合，以得到更明显、更具描述性的特征。

在这一方面，我们可以采用多分辨小波系统的特征融合方法，将不同的小波成分和特征测量结合，从而得到更加准确稳定的识别结果。

同时，还可以通过特征选择的方式筛选重要的特征，构建融合模型，以提高识别的准确性和鲁棒性。

总之，基于小波和仿射不变矩特征融合的舰船型号识别技术，具有非常重要的应用价值。

它可以通过不同角度对舰船进行特征提取，进而识别船体的局部特征和整体特征，从而准确的完成机器识别任务。

基于不变距的舰船目标识别

基于不变距的舰船目标识别
毛剑英;何友金;谭伟
【期刊名称】《光电技术应用》
【年(卷),期】2010(25)6
【摘要】通过对Hu不变矩和仿射不变矩原理的分析,得知不变距特征在图像平移、伸缩、旋转时均保持不变.分析了舰船红外图像的特点,结合Hu不变矩和仿射不变
距原理,以大量外场试验数据为基础,对转向的舰船进行了识别研究.从仿真实验得到的大量数据中,发现舰船在转向过程中仍然能很好的识别.
【总页数】4页(P8-10,21)
【作者】毛剑英;何友金;谭伟
【作者单位】海军航空工程学院,山东,烟台,264001;海军航空工程学院,山东,烟
台,264001;92853部队,辽宁,兴城,125106
【正文语种】中文
【中图分类】TN976
【相关文献】
1.一种基于组合不变矩的新的舰船图像目标识别方法 [J], 于吉红;吕俊伟;白晓明
2.不变矩目标特征描述误差分析和基于上层建筑不变矩的舰船识别 [J], 钱忠良;王文军
3.基于小波包和不变矩的舰船目标识别 [J], 孙俊
4.基于不变矩和支持向量机理论的舰船目标识别 [J], 袁爱民
5.轮廓矩不变量在舰船目标识别中的应用 [J], 刘辉
因版权原因，仅展示原文概要，查看原文内容请购买。

基于小波矩不变量的海上目标识别

基于小波矩不变量的海上目标识别
张琦;樊养余
【期刊名称】《计算机工程与科学》
【年(卷),期】2007(029)002
【摘要】针对识别海上目标时不同目标相似性大的特点,将不变矩理论与小波分析相结合,本文引入一种基于小波矩不变量的特征提取法,并且为得到一组局部最优特征组而提出一种结合DB Index准则的特征选择法.在仿真实验中,将小波矩不变量与Hu矩、Zernike矩进行了比较.实验表明,小波矩不变量具有更好的识别效果.【总页数】3页(P59-61)
【作者】张琦;樊养余
【作者单位】西北工业大学电子信息学院,陕西,西安,710072;西北工业大学电子信息学院,陕西,西安,710072
【正文语种】中文
【中图分类】TP391.41
【相关文献】
1.基于小波矩的自主式水下机器人目标识别 [J], 万磊;黄蜀玲;张铁栋;王博
2.基于小波矩和证据理论的图像目标识别算法 [J], 刘兵;李辉;翟海天
3.基于小波矩不变量的模式识别方法 [J], 徐旭东;周源华
4.基于图像边缘小波矩和支持向量机的目标识别 [J], 梅雪;林锦国
5.基于小波矩特征的小波神经网络目标识别 [J], 李晓兵;孙晓丽;夏良正
因版权原因，仅展示原文概要，查看原文内容请购买。

基于不变矩和支持向量机理论的船舰目标识别

基于不变矩和支持向量机理论的船舰目标识别
李玉景; 李琳; 李京
【期刊名称】《《科技信息》》
【年(卷),期】2007(000)029
【摘要】基于支持向量机(Support Vector Machine,SVM)理论和不变矩(Invariant Moments)理论,提出一种船舰目标识别方法。

首先,对图像进行预处理,将彩色图像转化为灰度图像;然后利用Hu不变矩来提取图像的七个不变矩特征;最后,选用支持向量机作为分类器,并将计算出的图像的七个矩特征作为支持向量机的输入对支持向量机进行训练和测试。

实验证明,将不变矩特征提取方法与SVM相结合用于模式识别,可以得到很高的分类效率和准确率。

【总页数】2页(P232-233)
【作者】李玉景; 李琳; 李京
【作者单位】青岛大学信息工程学院山东青岛266000
【正文语种】中文
【中图分类】TP391.4
【相关文献】
1.MET1证据理论在不变矩目标识别中的应用研究 [J], 熊广芝;冯大毅;杨百愚;张伟
2.基于BP神经网络的船舰目标识别分类 [J], 梁锦雄;王刻奇
3.不变矩理论及其在目标识别中的应用 [J], 柳林霞;陈杰;窦丽华
4.一种基于不变矩的红外目标识别算法 [J], 张旭艳;华宇宁;董晔;郝永平;张乐
5.基于不变矩和支持向量机理论的舰船目标识别 [J], 袁爱民
因版权原因，仅展示原文概要，查看原文内容请购买。

基于小波变换与变步长LMS算法的船舶磁场信号检测

基于小波变换与变步长LMS算法的船舶磁场信号检测冯国新1张坚2李屹祥1（1. 海军92941部队装备部，辽宁葫芦岛 125003；2. 海军工程大学兵器工程系，武汉 430033）摘要：针对检测船舶磁场信号时信噪比较低的问题，提出了一种基于小波变换与变步长LMS算法的检测方法。

根据船舶磁场信号的实际特征，首先对信号进行小波分解，并提取最后一层的低频分量，滤除高频噪声；再采用变步长LMS算法对低频分量进行自适应滤波，进一步滤除噪声，提取船舶目标特征信号。

船模实验的结果表明，该算法可以显著提高信噪比，增强了对船舶磁场信号的检测能力。

关键词：磁场信号检测小波变换LMS算法变步长中图分类号：TN911.23 文献标志码：A 文章编号：1003-4862 (2011) 03-0011-04Shipboard Magnetic Signal Detection Based on Wavelet Transform andVariable Step-Size LMS AlgorithmFeng Guoxin1, Zhang Jian2, Li Yixiang1(1.Unit 92941 of PLA, Huludao125003, Liaoning, China; 2. Department of Weaponry Engineering, Naval University ofEngineering, Wuhan 430033, China)Abstract: Aimed at the low signature-noise ratio (SNR) in detecting magnetic signal of ship, a detection algorithm based on wavelet transform and variable step-size LMS algorithm is proposed. According to the characteristics of magnetic signal of ship, the signal is firstly decomposed by wavelet transform, and the low frequency components in last level are taken out to filter out the high frequency noise. Then the low frequency components are filtered by variable step-size LMS algorithm to filter out noise and pick up characteristic signal of ship target. The results of experiment by ship model show that the algorithm increases SNR markedly, and enhances the detection ability of magnetic signal of ship.Key words: magnetic signal detection; wavelet transform; LMS algorithm; variable step-size1 引言船舶磁场信号是水中兵器探测的重要信号源。

一种基于小波变换的卫星SAR海洋图像舰船目标检测方法

一种基于小波变换的卫星SAR海洋图像舰船目标检测方法罗强;罗莉;任庆利;何鸿君;杨万海
【期刊名称】《兵工学报》
【年(卷),期】2002(023)004
【摘要】本文依据SAR图像海洋背景和舰船目标特点分析,提出了基于小波交换的卫星SAR海洋图像船舶目标检测方法,仿真分析结果表明该方法实用、有效.
【总页数】4页(P500-503)
【作者】罗强;罗莉;任庆利;何鸿君;杨万海
【作者单位】西安电子科技大学,陕西西安,710025;国防科技大学;西安交通大学;国防科技大学;西安电子科技大学,陕西西安,710025
【正文语种】中文
【中图分类】TP751.1
【相关文献】
1.一种基于多极化散射机理的极化SAR图像舰船目标检测方法 [J], 文伟;曹雪菲;张学峰;陈渤;王英华;刘宏伟
2.一种基于矩不变的SAR海洋图像舰船目标检测算法 [J], 邹焕新;匡纲要;蒋咏梅;郑键
3.一种基于小波变换的SAR图像边缘检测方法 [J], 周蓉蓉;陈刚;王正志
4.一种基于二次Gamma核的SAR图像舰船目标检测方法 [J], 谭昆; 邹焕新; 叶文隽; 陈振林
5.一种基于小波变换的SAR图像多尺度融合变化检测方法 [J], 李杰;任竞颖
因版权原因，仅展示原文概要，查看原文内容请购买。

舰船遥感图像检测小波分析研究

舰船遥感图像检测小波分析研究舰船遥感图像检测是现代海洋监测的重要技术之一，而传统的舰船遥感图像检测方法往往存在着诸多的问题，如低准确率、高误报率等。

而近些年来，基于小波分析的舰船遥感图像检测方法被越来越多地使用，其具有高准确率、低误报率等优点，成为了海洋监测领域的研究热点之一。

小波分析是一种时频分析工具，可以将信号分解为不同频率的小波系数，将时频信息分离出来。

在舰船检测中，可以利用小波分析提取出舰船的特征信息，进而进行目标检测和识别工作。

下面，将从小波分解、小波包和小波变换三个方面探讨舰船遥感图像检测中小波分析的应用。

首先是小波分解。

小波分解是通过将信号分解为各个尺度的小波系数，实现信号的时频分析。

在舰船遥感图像检测中，可以对原始图像进行小波变换，然后对小波系数进行阈值处理，根据阈值来判定小波系数所代表的区域是否包含舰船，这种方法被称为基于小波分解的舰船检测方法。

但是基于小波分解的方法在一些复杂的情况下容易产生误检和漏检，而对于舰船的尺寸大小也难以做到良好的检测效果。

其次是小波包分析。

小波包分析是基于小波分解的基础之上，将小波系数进行进一步的分解，实现了对目标的更加精细地描述。

在舰船遥感图像检测中，可以将小波包作为特征提取器，提取图像不同尺度的局部特征信息，从而实现目标的检测和识别工作。

相比于小波分解，小波包分析可以提高舰船检测的准确率，但是其计算量较大，需要较高的计算资源支持。

最后是小波变换。

小波变换是将信号变换到小波域中进行处理，特别适合于处理不稳定和非平稳的信号，如舰船遥感图像。

在舰船遥感图像检测中，可以将小波变换用于特征提取和目标检测过程中，通过对小波系数的选择和筛选，筛选出与舰船相关的特征信息，最终实现对舰船的准确检测。

相对于小波分解和小波包分析，小波变换可以更好地处理多尺度的信息，使得舰船的检测结果更加准确、清晰。

综上所述，基于小波分析的舰船遥感图像检测方法在海洋监测领域的应用前景广阔。

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先，介绍小波变换的相关理论，小波包分解能够进行任意尺度的时频分析；然后对不变矩算法进行介绍，给出一种基于小波包分解和不变矩算法相结合的特征值提取方法。仿真实验结果表明，本文给出的方法具有较高的识别率。
关键词：文章编号：
分类识别；小波包；不变矩 U665． 26 文献标识码： A doi： 10. 3404 / j. issn. 1672 － 7649. 2016. 1A. 061 1672 － 7649 （ 2016 ） 1A － 0181 － 03
中图分类号：
A ship target classification and recognition based on wavelet packet and moment invariant
SUN Jun （ Huanggang Polytechnic College ， Huanggang 438002 ， China） Abstract： In this paper the ship target classification system was studied ，the application of wavelet
－ 1 t －b Wf （ a，） dt， a ＞ 0。 b） = （ f （ t ）， f（ t） ψ（ ψa， b （ t）） = Ｒ a a 槡 2
小波包分解小波包定义过程如下：现设小波子空间为 W j ，尺寸子空间为 V j ，构成
设：的子空间为 U j ，
n
∫
U0 j ∈ Z， U1 j = W， j = Vj ，那么：
第 38 卷第 1A 期 2016 年 1 月
舰船科学技术 SHIP SCIENCE AND TECHNOLOGY
Vol． 38 ，No． 1A Jan．， 2016
基于小波包和不变矩的舰船目标识别
孙俊
（黄冈职业技术学院，湖北黄冈 438002 ）摘要：
以船舶目标分类系统作为研究对象，对小波包变换在舰船目标分类识别中的应用进行研究。首
由于 ψ a， b （ t ）在形状上与基本小波 ψ （ t ）相类似，
收稿日期： 2015 － 10 － 14 作者简介：孙俊（ 1972 －），男，硕士，副教授，主要从事视频目标识别、跟踪研究。
· 182·
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船
科
学 1. 3
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术
第 38 卷
a 表示 ψ a，因此， b （ t ）通常被称为小波基函数，其中， b 表示平移因子。尺度因子，设函数 f（ t） ∈ L （Ｒ），那么其连续小波变换的表达式为：
2
函数为 u2n （ t），子空间 V －1 及基函数展开系数为 dw ＜ + ɕ ，由上式可见， h（ k），且使 u n （ t）满足：正交基展开系数为 g（ k），
b）重构得到。但由于小波基函数 f（ t）可以由 W f （ a，的参数 a 和 b 均为连续变化，冗余度高，因此需要对参数 a 和 b 进行离散化，离散方式如下：
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ห้องสมุดไป่ตู้
小波变换
小波变换定义小波变换
［2 ］
水声信号处理作为重要的海洋开发技术手段一直是人们关注和研究的热点问题
［1 ］
。本文以船舶目
定义如下：
标分类识别为应用背景，通过运用水声信号的分类识别技术实现了船舶目标分类。船舶目标分类系统通常由预处理、特征提取、特征选择、分类器设计及分类决策几个部分构成。在本文中，对小波包变换和不变矩算法在船舶目标分类识别中的应用进行研究。采用小波包分解与不变矩相结合的方法进行特征提取，选取不同频段的特征能量作为特征向量，利用高斯混合模型对船舶目标分类识别进行研究。
transform for ship classification was researched． Firstly ，the theory of wavelet transform was introduced ， wavelet packet decomposition can do timefrequency analysis in any scale． Then moment invariant was introduced，and a method for feature extraction based on wavelet packet decomposition was proposed． The simulation results shown that the method introduced in this paper had a higher recognition rate． Key words： classification and recognition ； wavelet packet； moment invariant
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