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应用支持向量机分类的多角度目标识别技术 总被引:4,自引:1,他引:3
综合应用图像的不变矩特征和支持向量机分类方法,提出了一种对于红外图像中多角度目标的识别方法。首先通过目标分割算法求得红外图像中目标的轮廓图像,然后从轮廓图像的Hu矩、Zernike矩和Fourier-Mellin矩中选取适当阶次的矩特征组成目标在特定视角范围内的不变性特征向量;对目标的视角范围进行适当划分以解决多角度引起的目标样本多样性,并在每个划分的视角范围内分别应用支持向量机的方法进行多目标分类。测试结果表明,本文提出的方法较好地实现了红外图像中多角度目标的识别问题,是一种有效的自动目标识别算法。 相似文献
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《现代电子技术》2016,(21)
针对高分辨率SAR图像的飞机目标识别问题,提出了一种基于飞机几何特征、PCA特征、Hu不变矩等多特征决策融合的自动目标识别方案。针对飞机样本特点,分别提取飞机的几何长宽特征、PCA特征和Hu不变矩特征,使用三个支持向量机分类器分别对样本的三类特征进行预分类,然后采用基于等级的决策融合方法将预分类结果进行决策融合,输出最终的目标类别。实验过程中,随机选取一定百分比的样本进行训练,获得分类器模型,对全部的样本进行测试识别。通过实验发现,将几何特征、PCA特征和Hu不变矩特征的分类结果进行决策融合后,克服了单一特征决策的不准确性,有效地提高了每一类样本的识别准确率。 相似文献
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用于一维图像识别的支撑矢量机方法 总被引:9,自引:1,他引:9
研究了支撑矢量机的分类机理,并利用支撑矢量机对雷达目标一维像进行了识别,识别的结果表明了该方法的优越性,并显示它可以对残缺不全的样本进行识别。 相似文献
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提出了一种监控视频中运动目标识别分类算法,通过提取视频中运动目标的Zernike矩的7个高阶矩、长宽比和占空比,然后利用支持向量机(SVM)进行训练识别分类.实验结果表明,该算法具有较高的运动目标分类正确率,且通过对比显示该算法优于运用运动目标的Hu矩的高阶矩、长宽比和占空比进行分类的方法. 相似文献
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提出了一种根据局部特征进行图像描述和自动学习的识别算法.该算法能对地表遥感图像进行外貌分析,利用地貌特征进行图像分割,识别出图像中的丘陵、森林、沙漠、冲积扇.等地形.通过用一个大小可变的、边界模糊的窗口对图像进行大量取样,再利用这些样本来训练支持向量机,并使用该支持向量机进行模式分类,进行基于某些类型局部模式的相似性的自组织聚集,从而获得对遥感图像的整体性描述或理解.最后给出该方法在一些真实的遥感图像中的运用和分类实验的结果.应也适合拓展到其它具有纹理特征的模式识别问题. 相似文献
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介绍了图像目标识别技术中的图像分割,不变性参数提取和目标分类,利用图像目标的均匀性和相应知识自适应地分割和提取图像目标,被提取的每个图像目标的不变性参数由归一化过程和Zernike矩提取,并利用MPNN模型将图像目标分类,实验结果该识别系统能识别光照不均匀或复杂背景下的图像目标。 相似文献
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Zhang Yanning Zheng Jiangbin Zhao Rongchun 《电子科学学刊(英文版)》2003,20(1):8-12
An efficient target recognition method for remote sensing image is proposed inthis paper, which is based on moment invariant and support vector machine. First, seven Hu'sinvariant moments are extracted as a feature vector. Then,a support vector machine is used torecognize targets of planes and ships on binary remote sensing images. The experimental results 相似文献
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针对遥感成像过程中普遍存在的退化现象,通过遥感成像模拟,揭示了不同退化程度对遥感数据质量的影响,反映了考虑成像退化因素对提高卫星遥感数据信息提取能力的作用.基于成像退化采用的支持向量机方法对遥感图像地物分类的实验研究表明,这种方法使遥感图像地物分类精度得到明显提高,特别是支持向量机方法与图像恢复技术的结合,效果更为明显. 相似文献
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In this paper, a new approach has been proposed for improved facial expression recognition. The new approach is inspired by the compressive sensing theory and multiresolution approach to facial expression problems. Initially, each image sample is decomposed into desired pyramid levels at different sizes and resolutions. Pyramid features at all levels are concatenated to form a pyramid feature vector. The vectors are further reinforced and reduced in dimension using a measurement matrix based on compressive sensing theory. For classification, a multilevel classification approach based on single-branch decision tree has been proposed. The proposed multilevel classification approach trains a number of binary support vector machines equal to the number of classes in the datasets. Class of test data is evaluated through the nodes of the tree from the root to its apex. The results obtained from the approach are impressive and outperform most of its counterparts in the literature under the same databases and settings. 相似文献
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Classification of hyperspectral remote sensing images with support vector machines 总被引:33,自引:0,他引:33
This paper addresses the problem of the classification of hyperspectral remote sensing images by support vector machines (SVMs). First, we propose a theoretical discussion and experimental analysis aimed at understanding and assessing the potentialities of SVM classifiers in hyperdimensional feature spaces. Then, we assess the effectiveness of SVMs with respect to conventional feature-reduction-based approaches and their performances in hypersubspaces of various dimensionalities. To sustain such an analysis, the performances of SVMs are compared with those of two other nonparametric classifiers (i.e., radial basis function neural networks and the K-nearest neighbor classifier). Finally, we study the potentially critical issue of applying binary SVMs to multiclass problems in hyperspectral data. In particular, four different multiclass strategies are analyzed and compared: the one-against-all, the one-against-one, and two hierarchical tree-based strategies. Different performance indicators have been used to support our experimental studies in a detailed and accurate way, i.e., the classification accuracy, the computational time, the stability to parameter setting, and the complexity of the multiclass architecture. The results obtained on a real Airborne Visible/Infrared Imaging Spectroradiometer hyperspectral dataset allow to conclude that, whatever the multiclass strategy adopted, SVMs are a valid and effective alternative to conventional pattern recognition approaches (feature-reduction procedures combined with a classification method) for the classification of hyperspectral remote sensing data. 相似文献
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针对变电站巡检机器人远程监控系统中红外图像识别存在的问题,提出一种基于改进区域生长法和BP神经网络的红外图像目标设备分割与识别的方法。利用最小二乘法拟合出红外图像中亮度与温度之间的线性关系,建立基于像素的图像温度场;根据设定温度范围确定区域生长法的种子点位置,利用Otsu法确定截屏窗口最优分割阈值,并结合灰度相似性阈值作为区域生长法的分割准则,实现该窗口目标设备精确分割;将分割出的设备二值图像的Hu不变矩作为设备形状特征向量,并对其进行不变性和类间区分度验证;采用引入附加动量法和自适应调整学习率的BP神经网络实现多种电气设备的识别,实验数据表明优化后的BP神经网络具有迭代收敛快,误差波动性小,分类准确度高等特点。 相似文献
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为了提高高光谱遥感图像的分类精度, 通过结合像元邻域谱与概率协同表示方法, 提出了一种基于空间信息与光谱信息的分类方法。首先采用插值方法生成像元的邻域谱, 然后用概率协同表示方法将待测样本进行分类。用所提出的方法在AVIRIS Indian Pines和Salinas scene高光谱遥感数据库上进行分类实验, 并和主成分分析、支持向量机、稀疏表示分类器和协同表示分类器方法进行了比较。结果表明, 所提出的方法在AVIRIS Indian Pines数据库上识别精度比主成分分析法高约17%, 其识别精度和kappa系数都优于另外4种方法。该方法是一种较好的高光谱遥感图像分类方法。 相似文献
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为了准确识别电网中的低零值绝缘子,提高劣化绝缘子诊断的准确率,提出了一种使用灰狼算法优化(grey wolf optimizer, GWO)与二进制支持向量机(support vector machine, SVM)分类器相结合的模型,对红外图像中的低零值绝缘子进行自动检测。首先对绝缘子红外图像进行增强,利用Ostu算法对红外图像进行分割,并对得到的二值图像进行倾斜角度矫正和切割,提取绝缘子串的有效区域,然后将图像特征用于向量机的分类识别。实验结果表明,灰狼算法优化支持向量机比常用的网格搜索算法(grid search, GS)、粒子群优化算法(particle swarm optimization, PSO)等得到的分类模型能更准确、有效地对低零值绝缘子进行识别,且准确率更高。 相似文献
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Most existing remote sensing image retrieval systems allow only simple queries based on sensor, location, and date of image capture. This approach does not permit the efficient retrieval of useful hidden information from large image databases. This paper presents an integrated approach to retrieving spectral and spatial patterns from remotely sensed imagery using state-of-the-art data mining and advanced database technologies. Land cover information corresponding to spectral characteristics is identified by supervised classification based on support vector machines with automatic model selection, while textural features characterizing spatial information are extracted using Gabor wavelet coefficients. Within identified land cover categories, textural features are clustered to acquire search-efficient space in an object-oriented database with associated images in an image database. Interesting patterns are then retrieved using a query-by-example approach. The evaluation of the study results using coverage and novelty measures validates the effectiveness of the proposed remote sensing image information mining framework, which is potentially useful for applications such as agricultural and environmental monitoring. 相似文献