共查询到20条相似文献，搜索用时 15 毫秒
1.
提出了一种基于核函数度量相似性的遥感影像变化检测算法。该算法通过比较两个时相特征向量的概率密度进行变化判别,将概率密度的比较转化成核函数的形式,利用核函数的相似度量功能进行变化判别,通过指定的核函数避开概率密度的估计,达到概率密度比较的目的。 相似文献
2.
一种遥感影像核变化检测方法 总被引：1，自引：0，他引：1
提出了一种新的遥感影像核变化检测方法。该方法是将原始空间不同时相的输入矢量通过核函数非线性映射到高维特征空间，然后在高维特征空间中通过传统变化检测方法处理得到新的输入矢量，最后通过半监督的单类支持向量机算法对新的输入矢量构造变化区域与非变化区域的最优分割超平面。试验证实，本文的核变化检测方法具有较高的检测精度和效率。 相似文献
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A kernel functions analysis for support vector machines for land cover classification 总被引：6，自引：0，他引：6
T. Kavzoglu I. Colkesen 《International Journal of Applied Earth Observation and Geoinformation》2009,11(5):352-359
Information about the Earth's surface is required in many wide-scale applications. Land cover/use classification using remotely sensed images is one of the most common applications in remote sensing, and many algorithms have been developed and applied for this purpose in the literature. Support vector machines (SVMs) are a group of supervised classification algorithms that have been recently used in the remote sensing field. The classification accuracy produced by SVMs may show variation depending on the choice of the kernel function and its parameters. In this study, SVMs were used for land cover classification of Gebze district of Turkey using Landsat ETM+ and Terra ASTER images. Polynomial and radial basis kernel functions with their estimated optimum parameters were applied for the classification of the data sets and the results were analyzed thoroughly. Results showed that SVMs, especially with the use of radial basis function kernel, outperform the maximum likelihood classifier in terms of overall and individual class accuracies. Some important findings were also obtained concerning the changes in land use/cover in the study area. This study verifies the effectiveness and robustness of SVMs in the classification of remotely sensed images. 相似文献
4.
Akshara Preethy Byju Anil Kumar Alfred Stein A. Senthil Kumar 《Journal of the Indian Society of Remote Sensing》2018,46(9):1519-1526
This article presents the use of kernel functions in fuzzy classifiers for an efficient land use/land cover mapping. It focuses on handling mixed pixels obtained from a remote sensing image by considering non-linearity between class boundaries. It uses kernel functions combined with the conventional fuzzy c-means (FCM) classifier. Kernel-based fuzzy c-mean classifiers were applied to classify AWiFS and LISS-III images from Resourcesat-1 and Resourcesat-2 satellites. Optimal kernels were obtained from eight single kernel functions. Fractional images generated from high resolution LISS-IV image were used as reference data. Classification accuracy of the FCM classifier increased with 12.93%. Improvement in overall accuracy shows that non-linearity in the dataset was handled adequately. The inverse multiquadratic kernel and the Gaussian kernel with the Euclidean norm were identified as optimal kernels. The study showed that overall classification accuracy of the FCM classifier improved if kernel functions were included. 相似文献
5.
Hamidullah Binol 《Journal of the Indian Society of Remote Sensing》2018,46(10):1605-1615
This article presents a novel supervised target detection approach on hyperspectral images based on Fukunaga–Koontz Transform (FKT) with compositional kernel combination. The Fukunaga–Koontz Transform is one of the most effective techniques for solving problems that involve two-pattern characteristics. To capture nonlinear properties of data, researchers have extended FKT to kernel FKT (KFKT) by means of kernel machines. However, the performance of KFKT depends on choosing convenient kernel functions and/or selection of the proper parameter(s). In this work, instead of selecting a single kernel for nonlinear version of FKT, we have applied a compositional kernel combination approach to capture the underlying local distributions of hyperspectral remote sensing data. Optimal parameter selection for each kernel function is achieved applying an evolutionary technique called differential evolution algorithm. The proposed new nonlinear target detection algorithm is tested for hyperspectral images. The experimental results verify that the proposed target detection algorithm has effective and promising performance compared to the conventional version for supervised target detection applications. 相似文献
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针对多时相遥感影像变化检测存在数据不确定性、检测精度不高等问题,提出了一种结合变化向量分析（CVA）和直觉模糊C均值聚类算法（IFCM）的多时相遥感影像变化检测方法. 首先通过CVA构建两个时相遥感影像的差异影像;然后采用直觉模糊C均值聚类算法对差异影像进行聚类得出变化区域和未变化区域;最后对变化检测结果进行二值化处理并进行精度评价. 选取两个时相的高分一号遥感影像和Szada数据集影像作为实验数据. 实验结果表明,采用提出的方法可有效解决传统方法存在的数据不确定性问题,变化检测精度达到了95.92%和92.70%,是一种可行的遥感影像变化检测方法. 研究结果可用于森林动态变化监测、土地复垦利用规划变化分析以及灾损评估. 相似文献
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在遥感影像自动分类中仅使用光谱特征很难产生正确的分类,OLI影像是波段数较多的多光谱影像,如果增加纹理、几何等多种特征以提高分类精度,就会使得特征的维度很高.支持向量机善于解决小样本、非线性和高维的影像分类问题,但是核函数和参数的设置只能依靠实验来获得.文中在OLI影像中提取了23个特征,逐个测试核函数和参数值对分类结果的影响.研究的主要结论如下：RBF核的支持向量机分类精度最高,Sigmoid核支持向量机分类精度最低;核函数的选择对分类精度的影响最大;核函数和参数值的变化不会影响重要特征的使用,3种核的支持向量机分类所使用的重要特征基本一致. 相似文献
9.
In many change detection applications, the focus is often on one specific change class. The one-class support vector machine (OCSVM)-based change detection method has been proved effective for dealing with such problems, which only requires samples from the change class of interest as the training data. However, this classical method only uses a single kernel which limits its separating capabilities in real-world applications. To further improve the efficacy of the OCSVM-based change detection method, this paper proposes an improved change detection method that uses a data-oriented composite-kernel-based one-class support vector machine. It utilizes the feature information entropy of the training data to determine the kernel weights in constructing a composite kernel. Experimental results on two data-sets demonstrate that the proposed method outperforms the existing classical OCSVM-based change detection method and the traditional composite-kernel-based method with relatively few false alarm errors, and shows good potential for further applications. 相似文献
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A semisupervised support vector machine is presented for the classification of remote sensing images. The method exploits the wealth of unlabeled samples for regularizing the training kernel representation locally by means of cluster kernels. The method learns a suitable kernel directly from the image and thus avoids assuming a priori signal relations by using a predefined kernel structure. Good results are obtained in image classification examples when few labeled samples are available. The method scales almost linearly with the number of unlabeled samples and provides out-of-sample predictions. 相似文献
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基于支持向量机的航空影像纹理分类研究 总被引：8，自引：0，他引：8
提出一种用SVM解决航空影像纹理分类的方法。在利用一些常用的纹理特征的基础上，将SVM用于航空影像纹理分类，有效地解决了特征选择难和高维数问题。试验表明，这种方法可以取得较好的结果。 相似文献
12.
Marconcini M. Camps-Valls G. Bruzzone L. 《Geoscience and Remote Sensing Letters, IEEE》2009,6(2):234-238
This letter presents a novel composite semisupervised support vector machine (SVM) for the spectral-spatial classification of hyperspectral images. In particular, the proposed technique exploits the following: 1) unlabeled data for increasing the reliability of the training phase when few training samples are available and 2) composite kernel functions for simultaneously taking into account spectral and spatial information included in the considered image. Experiments carried out on a hyperspectral image pointed out the effectiveness of the presented technique, which resulted in a significant increase of the classification accuracy with respect to both supervised SVMs and progressive semisupervised SVMs with single kernels, as well as supervised SVMs with composite kernels. 相似文献
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《International Journal of Digital Earth》2013,6(4):334-344
This paper presents a supervised polarimetric synthetic aperture radar (PolSAR) change detection method applied to specific land cover types. For each pixel of a PolSAR image, its target scattering vector can be modeled as having a complex multivariate normal distribution. Based on this assumption, the joint distribution of two corresponding vectors in a pair of PolSAR images is derived. Then, a generalized likelihood ratio test statistic for the equality of two likelihood functions of such joint distribution is considered and a maximum likelihood distance measure for specific land cover types is presented. Subsequently, the Kittler and Illingworth minimum error threshold segmentation method is applied to extract the specific changed areas. Experiments on two repeat-pass Radarsat-2 fully polarimetric images of Suzhou, China, demonstrate that the proposed change detection method gives a good performance in determining the specific changed areas in PolSAR images, especially the areas that have changed to water. 相似文献
14.
组合核支持向量回归提取高光谱影像不透水面 总被引：1，自引：0，他引：1
由于城市地表组成的复杂性,基于单核函数的支持向量回归模型很难满足精度。本文结合空间-光谱组合核函数和支持向量回归,提出了一种提取高光谱影像不透水面丰度的改进算法。首先从高光谱遥感图像上提取波谱特征和多通道灰度共生矩阵空间纹理特征,选取研究区10%像元特征数据作为训练数据,以线性加权求和核为多核组合方式,建立结合光谱信息和空间信息的组合核支持向量回归模型。然后,用生成的回归模型预测未知像元不透水面丰度值。最后,对实验结果进行评价。在模拟数据试验中,本文算法比单核回归均方根误差平均降低1.4%,决定系数比单核回归平均提高0.6%。在Hyperion数据两组试验中,该算法比单核回归均方根误差平均降低1.8%,决定系数比单核回归平均提高11.7%。模拟和真实两种高光谱数据实验中,本文算法均得到了空间形态上更准确的不透水面结果,单核回归结果存在失真现象。研究结果表明:本文算法能够有效提取城市不透水面丰度,与单核方法相比有较明显的精度提升。 相似文献
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Zhan-Li Sun De-Shuang Huang Yiu-Ming Cheung Jiming Liu Guang-Bin Huang 《Geoscience and Remote Sensing Letters, IEEE》2005,2(2):108-112
In this letter, a new nonlinear approach based on a combination of the fuzzy c-means clustering (FCMC), feature vector selection and principal component analysis (PCA) is proposed to extract features of multispectral images when a very large number of samples need to be processed. The main contribution of this letter is to provide a preprocessing method for classifying these images with higher accuracy compared to the single PCA and kernel PCA. Finally, some experimental results demonstrate that our proposed approach is effective and efficient in analyzing multispectral images. 相似文献
17.
The kernel function is a key factor to determine the performance of a support vector machine (SVM) classifier. Choosing and constructing appropriate kernel function models has been a hot topic in SVM studies. But so far, its implementation can only rely on the experience and the specific sample characteristics without a unified pattern. Thus, this article explored the related theories and research findings of kernel functions, analyzed the classification characteristics of EO-1 Hyperion hyperspectral imagery, and combined a polynomial kernel function with a radial basis kernel function to form a new kernel function model (PRBF). Then, a hyperspectral remote sensing imagery classifier was constructed based on the PRBF model, and a genetic algorithm (GA) was used to optimize the SVM parameters. On the basis of theoretical analysis, this article completed object classification experiments on the Hyperion hyperspectral imagery of experimental areas and verified the high classification accuracy of the model. The experimental results show that the effect of hyperspectral image classification based on this PRBF model is apparently better than the model established by a single global or local kernel function and thus can greatly improve the accuracy of object identification and classification. The highest overall classification accuracy and kappa coefficient reached 93.246% and 0.907, respectively, in all experiments. 相似文献
18.
多尺度分割的高分辨率遥感影像变化检测 总被引：4，自引：1，他引：3
针对高空间分辨率的遥感影像,提出了一种基于多尺度分割的变化检测算法。采用Mean-Shift分割算法对影像进行多尺度分割,构建了不同尺度上的地理对象,以不同尺度上的地理对象灰度均值构建了变化检测的多尺度特征向量,采用变化矢量分析法获得最后的变化检测结果。以城镇区和农田区的Quick Bird影像对本文算法进行了检验,从精度评价的效果来看,无论城镇区还是农田区,采用面向对象的变化检测方法精度都高于基于单像素的检测方法,且当尺度层数固定时,多尺度组合的变化检测结果优于单一尺度的变化检测结果,对城镇、农田区域的变化检测的精度分别达到87.57%和81.55%。本文算法既可以顾及大面积同质区域变化,又可以反映小的地物目标及边缘部分的变化,能够很好地满足城镇、农田等不同环境背景下的变化检测需求,在国土资源监测中具有一定的应用价值。 相似文献
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基于相关向量机的高光谱影像分类研究 总被引：2，自引：0，他引：2
虽然支持向量机在高光谱影像分类得到成功应用,但是它自身固有许多不足之处。相关向量机是在贝叶斯框架下提出的更加稀疏的学习机器,它没有规则化系数,其核函数不需要满足Mercer条件,不仅具备良好的泛化能力,而且还能够得到具有统计意义的预测结果。本文从分析支持向量机用于高光谱影像分类存在的不足出发,提出了一种基于相关向量机的高光谱影像分类方法,介绍了稀疏贝叶斯分类模型,将相关向量机学习转化为最大化边缘似然函数估计问题,并采用了快速序列稀疏贝叶斯学习算法。通过PHI和OMIS影像分类实验分析表明了基于相关向量机的高光谱影像分类方法的优越性。 相似文献
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针对大坝变形系统的非线性、复杂性以及不确定等特点,提出一种优化多核相关向量机的大坝变形预测模型方法。通过对实验数据进行归一化处理,核函数的加权组合以及遗传算法对模型参数的优化,建立遗传算法优化多核相关向量机的大坝变形预测模型。实验结果表明:数据归一化能归纳统一样本的统计分布性,加快梯度下降求解最优解速度和提高预测精度;优化的加权核函数能有效提高模型预测精度;各项精度指标值均优于BP神经网络方法、多项式核相关向量机方法预测精度,证实优化的多核相关向量机模型是一种精度较高的大坝变形预测方法。 相似文献