OC-SVM驱动的SAR舰船检测虚假警报剔除策略

64 浏览量更新于2024-08-30 收藏 353KB PDF 举报

本文主要探讨了一种基于支持向量机（OC-SVM）的有效SAR（Synthetic Aperture Radar）舰船检测中的虚假警报消除方法。舰船在SAR遥感图像中的自动识别对于国家海洋安全、船舶交通服务以及海军作战等领域具有重要意义。然而，由于海杂波的高异质性和复杂场景，SAR图像往往会产生大量的虚假警报，这对目标检测的准确性和效率构成了挑战。 OC-SVM，全称为One-Class Support Vector Machine，是一种监督学习算法，特别适用于异常检测和分类问题。在SAR舰船检测场景中，OC-SVM被用来区分实际的舰船目标与背景中的噪声和非目标信号。相比于传统的二元分类方法，OC-SVM利用单一类别的数据进行训练，能够更专注于模式的边界，从而提高对异常（虚假警报）的识别能力。文章首先详细介绍了OC-SVM的工作原理和优势，包括其在小样本、非线性和高维数据处理方面的性能。然后，作者针对SAR图像的特点，对OC-SVM的参数进行了深入分析，如核函数的选择、惩罚参数C以及核宽度等，以优化模型的性能和减少虚警率。在实验部分，该研究对比了基于OC-SVM的虚假警报去除方法与其他常见方法（如阈值法、统计滤波等）在假警报清除效果和目标检测精度上的表现。通过一系列定量和定性的评估指标，如误报率（False Alarm Rate, FAR）、漏报率（Miss Rate）和F1分数，作者展示了OC-SVM在实际应用中的优越性。值得注意的是，尽管OC-SVM在处理复杂背景下的舰船检测问题上显示出显著的优势，但实际应用中仍面临一些挑战，如环境变化导致的模型适应性问题以及对大量标注数据的依赖。因此，后续的研究可能需要考虑结合深度学习或其他机器学习技术来进一步提升模型的鲁棒性和泛化能力。总结来说，本文提供了一个有效的虚假警报消除策略，利用OC-SVM在SAR舰船检测中的应用，有助于提高遥感图像处理的精确性和可靠性，对于推动相关领域的发展具有实际价值。同时，该研究也为其他领域的虚假警报抑制提供了新的思路和方法。

AN EFFECTIVE FALSE-ALARM REMOVAL METHOD

BASED ON OC-SVM FOR SAR SHIP DETECTION

Xiaoting Yang

, Fukun Bi

, Ying Yu

, Liang Chen

Beijing Key Laboratory of Embedded Real-time Information Processing Technology, Beijing

Institute of Technology,Beijing 100081,China

School of Electronic Information Engineering, North China University of Technology, Beijing

100144, China

School of Information Science and Engineering, Yunnan University, Kunming 650091, China

chenl@bit.edu.cn

Keywords: OC-SVM, ship detection, false alarms

removal, parameters analysis, performance comparison.

Abstract

Automatic ship detection from SAR remote sensing

imagery is very important, with a wide array of

applications in areas such as national marine safety,

vessel traffic services, and naval warfare. However, SAR

remote sensing images with the high inhomogeneity of

sea clutter and complex scene result in a large number of

false alarms. This paper focuses on the problem of false

alarm removal and coverts it as a classification problem

with respect to ship targets and false alarms. However, in

real application: 1) Compared to false alarms the number

of ships is much smaller, and 2) false alarms with diverse

categories and no uniform characteristics are difficult to

be distinguished from ships. To solve the problems above,

we present a new method based on one-class SVM (OC-

SVM) combined with grid optimization that only use the

data of ships to train classifier in the absence of false

alarms. First, 2P-CFAR is adopted to extract ship

candidates and 14 features mainly focusing on ship targets

are selected. Second, grid optimization is exploited to

obtain parameters for OC-SVM training, where it is a

compromise between the probability of false alarm and

detection rate. Experimental results of OC-SVM on a

large SAR image set demonstrate that in comparison with

other state-of-the-art methods like SVM, BP neural

network, our approach can achieve higher detection

accuracy.

1 Introduction

Synthetic aperture radar (SAR) with capacity to image

day and night under most meteorological conditions

became the state-of-the-art technique for ship detection [1]

and play a prominent role in the national marine safety,

ocean management, and monitoring illegal fishing [2][3].

Numerous studies have been performed. CFAR [4]

detection, which is famous for its constant false alarm

probability and adaptive threshold has been used widely,

such as Gamma, Weibull, K, etc.[3,5]. And Gui Gao [6]

proposed Parzen-Window-Kernel-Based CFAR algorithm

for ship detection. However, these methods all lead to a

high false alarm rate due to SAR remote sensing images

with the high inhomogeneity of sea clutter and cannot

remove false alarms effectively. The removal of false

alarm is still a challenge of today.

In fact, to distinguish ships from false alarms can regard

as a classification problem. And various classifiers have

been used in numerous fields, such as K-means cluster [7],

Support Vector Machine (SVM) [8] and One-Class SVM

[9].

K-means clustering is a simple method, no need to

estimate the distribution of samples, but it is unsuitable

here because the effects of noises and the diversity among

false alarms make it difficult to select the appropriate K

and the cluster centers, which are directly related to the

performance of the final classification. BP neural network

[10] is a feed-forward network, and is one of the most

widely used neural networks, with its self-learning, self-

organizing, and adaptive capabilities. However, there

may be over-fitting and lower generalization problems. In

the process of ship detection due to the small training sets,

which can not guarantee the training samples include all

types of ships and false alarms, so that the classifier can

not correctly predict unknown samples. Support Vector

Machine (SVM) [8] is originally for dichotomous

questions, the main idea is to map the data set to a high

dimensional feature space, and in this feature space to

find an optimal hyperplane to separate the two types of

samples. However, in the process of ship detection not

only false alarms with diverse categories, but they also

have various forms of internal features, resulting in a

simple hyperplane can not achieve the goal to classify

ships and false alarms effectively. Even though

constructing multiple classifiers, due to the uncertainty

and diversity of false alarms, we also hard to determine

the number of classifiers and extract optimal features.

What’s more, the number of ship is much smaller than

false alarms, which lead to the deviation of hyperplane.

Aiming at the problems of unbalanced number between

ship targets and false alarms, also the diverse categories

and no uniform characteristics for false alarms to be

distinguished from ships, We present a new method based

on one-class SVM (OC-SVM) to escape false alarms for

ship detection, mainly focusing on the ship targets to

extract features and train classifiers. On the one hand this

下载后可阅读完整内容，剩余3页未读，立即下载

weixin_38621897

粉丝: 6
资源: 956

OC-SVM驱动的SAR舰船检测虚假警报剔除策略

OC-SVM驱动的新情感词识别与分类方法

基于LS-SVM的实时入侵检测系统研究

基于粗集与RS-SVM的建筑施工安全预警模型对比研究

论文研究-基于OC-SVM的大型数据集分类方法.pdf

OC-SVM与SVM区别

算法一类支持向量机OC-SVM

Hypersphere-support-vector-machine.zip_OC-SVM matlab_hyperspher

算法一类支持向量机OC-SVM（2）

基于KPCA-SVM的煤与瓦斯突出预测方法

论文研究-基于PSO-SVM的短期交通流预测方法.pdf

最新资源