利用Meanshift进行道路提取

介绍了基于Meanshift算法的小比例尺航空影像道路提取方法。通过估计给定的中心点附近概率密度提取道路中心点,并利用核函数的影子函数使得其搜索过程沿着概率密度分布的梯度方向前进,加快收敛速度。分别从基于矢量信息进行自动提取和给定少量初值自动跟踪两个层次讨论了该方法的实用性,并用实验证明了该方法的有效性。     

采用非正定OCSVM 的高光谱影像地物检测

高斯径向基核函数是基于光谱向量间欧氏距离的度量,对于因光照强度变化而引起的地物光谱变异敏感,当同类地物光谱发生变异时,基于高斯径向基核的高光谱影像地物检测算法的性能下降.为了解决该问题,基于光谱曲线形状相似性描述提出了光谱角度余弦核测度这一非正定核函数,并应用于一种非正定OCSVM 方法的高光谱影像地物检测.最后利用两幅高光谱影像进行了实验分析,实验结果证明了本文算法的有效性.     

高光谱遥感影像的广义判别分析特征提取

高光谱遥感影像具有丰富的光谱信息,在地物分类识别方面具有明显的优势。针对复杂高光谱影像分类问题,应用了一种广义判别分析特征提取技术。将输入样本通过非线性函数映射到特征空间,在特征空间中应用线性判别特征提取方法;算法求解过程中涉及到在特征空间的内积用核函数代替,简化计算的同时也使得算法与非线性函数的具体形式无关。通过影像分类试验表明,该方法较常用特征提取方法更有利于分类精度的提高。     

一类新的独立性度量及其在盲信号分离中的应用

提出了一类无穷多种称为准熵的新的独立性度量 ,它们用严格凸函数对原变量经分布函数变换再量化后得到的变量的联合概率的均匀性进行度量 ,并提出了基于准熵的盲分离算法 ,可分离任意连续分布的信号 ,包括峭度为零的信号。通过与前人算法的对比试验 ,证实了基于准熵的算法的优越性     

高光谱遥感影像的广义判别分析特征提取

高光谱遥感影像具有丰富的光谱信息,在地物分类识别方面具有明显的优势.针对复杂高光谱影像分类问题,应用了一种广义判别分析特征提取技术.将输入样本通过非线性函数映射到特征空间,在特征空间中应用线性判别特征提取方法;算法求解过程中涉及到在特征空间的内积用核函数代替,简化计算的同时也使得算法与非线性函数的具体形式无关.通过影像分类试验表明,该方法较常用特征提取方法更有利于分类精度的提高.     

基于核估计的北斗RDSS传输延时随机数生成法

北斗卫星导航系统（BDS）采用卫星无线电导航业务(RNSS)和卫星无线电测定业务(RDSS)双模结构体制,不但具有全球定位系统(GPS)等系统的导航、定位和授时功能,同时还提供双向短报文信息服务,在多个民用领域有着非常广阔的潜在应用前景. 传输成功率和传输延时是北斗短报文的两个重要性能参数,北斗短报文技术在各行业的推广应用以及相关系统的建设都急需这两个性能参数作为参考. 本文测试了北斗短报文的通信性能,提出基于核密度估计建立北斗短报文传输时延概率模型并生成时延伪随机数的方法. 通过MATLAB对北斗RDSS实测时延数据进行核密度估计并绘制概率密度估计值曲线;利用权重系数组合多个高斯函数对概率密度估计值曲线进行拟合,获得时延的概率密度分布函数,并采用舍选法实现北斗RDSS传输延时伪随机数的生成.      

基于结构相似性与直线提取的建筑物变化检测方法

利用数字线划图中建筑物的矢量信息,根据前后两个时期的高分辨率遥感影像,通过结合LSD直线段提取算法与结构相似性度量,采用比较特征差异的方式提出了一种能够以较高的正确率进行快速建筑物变化检测的算法。本文对检测算法的过程进行了介绍,通过实验数据对算法进行了检验,探讨了阈值变化对结果的影响。     

一种基于核回归的SAR图像自适应相干斑抑制方法

为了在抑制相干斑噪声的同时更好地保持SAR图像中的点目标和边缘目标,在经典核回归方法的基础上,本文提出了基于核回归的SAR图像自适应相干斑抑制方法。通过分析SAR图像的幅度分布特性,在构建模型时,以图像的幅度值为判别条件,使核函数在幅值较小的背景区域具有较大的光滑作用以抑制噪声,而在幅值较大的目标区域光滑作用较小以保护目标特征;同时考虑对边缘的保护作用,基于散布矩阵修正了自适应核回归方法,建立了基于核回归的SAR图像自适应相干斑抑制方法。试验结果表明,该算法通过将幅度值和散布矩阵引入核函数,更好地抑制了噪声,同时也保持了图像中的点目标和边缘。     

矢量GIS平面一般曲线等概率密度误差模型的几何特征

基于等概率密度误差模型建模原理和数值算法,运用函数极值理论和迭代方法来求解平面一般曲线上两相邻特征点间位置精度最高的点,以精确确定误差模型的最小带宽,从理论上给出等概率密度误差模型的几何特征,从而进一步完善矢量GIS的位置不确定性理论。通过实例计算与可视化分析,验证了理论推导的正确性。     

基于KICA的多时相遥感图像变化检测

针对PCA变化检测方法的精度较低和ICA方法的线性局限性问题,提出了基于核独立成分分析(KICA)的多时相遥感图像变化检测方法。首先,将每一时相的图像转化为列向量,并把这些列向量组成矩阵;然后,通过核函数将矩阵映射到高维特征空间中,再在该空间中利用ICA方法分离出相互独立的图像分量;接着通过FCM算法分割表征变化信息的图像分量,并采用区域生长算法获得完整的变化信息;最后,分别利用本文方法与差值法、PCA方法和ICA方法对多时相遥感图像进行变化检测,并对检测结果进行定性分析和定量比较。结果表明,该方法能更好地分离出多时相遥感图像的变化信息,具有更高检测精度。     

A Kernel-Based Similarity Measuring for Change Detection in Remote Sensing Images

This paper presents a kernel-based approach for the change detection of remote sensing images. It detects change by comparing the probability density (PD), expressed as kernel functions, of the feature vector extracted from bi-temporal images. PD is compared by defined kernel functions without immediate PD estimation. This algorithm is model-free and it can process multidimensional data, and is fit for the images with rich texture in particular. Experimental results show that overall accuracy of the algorithm is 98.9%, a little bit better than that of the change vector analysis and classification comparison method, which is 96.7 and 95.9% respectively.     

Kernel adaptive subspace detector for hyperspectral imagery

In this letter, we present a kernel-based nonlinear version of the adaptive subspace detector (ASD) that implicitly detects signals of interest in a high-dimensional (possibly infinite) feature space associated with a particular nonlinear mapping. In order to address the high dimensionality of the feature space, ASD is first implicitly formulated in the feature space, which is then converted into an expression in terms of kernel functions via the kernel trick property of the Mercer kernels. Experimental results based on simulated data and real hyperspectral imagery show that the proposed kernel-based ASD outperforms the conventional ASD and a nonlinear anomaly detector so called the kernel RX-algorithm.     

一种遥感影像核变化检测方法

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。     

Combining the FCM Classifier with Various Kernels to Handle Non-linearity of Class Boundaries

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.     

Improved Fukunaga–Koontz Transform with Compositional Kernel Combination for Hyperspectral Target Detection

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.     

Biophysical Parameter Estimation With a Semisupervised Support Vector Machine

This letter presents two kernel-based methods for semisupervised regression. The methods rely on building a graph or hypergraph Laplacian with both the available labeled and unlabeled data, which is further used to deform the training kernel matrix. The deformed kernel is then used for support vector regression (SVR). Given the high computational burden involved, we present two alternative formulations based on the Nystrom method and the incomplete Cholesky factorization to achieve operational processing times. The semisupervised SVR algorithms are successfully tested in multiplatform leaf area index estimation and oceanic chlorophyll concentration prediction. Experiments are carried out with both multispectral and hyperspectral data, demonstrating good generalization capabilities when a low number of labeled samples are available, which is usually the case in biophysical parameter retrieval.     

高光谱图像目标检测的核信号空间正交投影法

针对非线性混合下的亚像元目标检测问题, 提出一种基于核函数的信号空间正交投影方法(KSSP)。该方法作为信号空间正交投影方法(SSP)的非线性推广, 首先将原空间中像元矢量经非线性映射转换到高维特征空间,然后在特征空间中用线性信号空间正交投影进行目标检测。通过核技巧, 核信号空间正交投影不必知道具体的非线性映射形式。经模拟数据与真实高光谱图像数据实验证明, KSSP 方法在目标检测性能上优于SSP, 且对噪声的抑制也有很好的效果。     

A kernel functions analysis for support vector machines for land cover classification

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.     

A particle swarm optimized kernel-based clustering method for crop mapping from multi-temporal polarimetric L-band SAR observations

Polarimetric Synthetic Aperture Radar (PolSAR) data, thanks to their specific characteristics such as high resolution, weather and daylight independence, have become a valuable source of information for environment monitoring and management. The discrimination capability of observations acquired by these sensors can be used for land cover classification and mapping. The aim of this paper is to propose an optimized kernel-based C-means clustering algorithm for agriculture crop mapping from multi-temporal PolSAR data. Firstly, several polarimetric features are extracted from preprocessed data. These features are linear polarization intensities, and several statistical and physical based decompositions such as Cloude-Pottier, Freeman-Durden and Yamaguchi techniques. Then, the kernelized version of hard and fuzzy C-means clustering algorithms are applied to these polarimetric features in order to identify crop types. The kernel function, unlike the conventional partitioning clustering algorithms, simplifies the non-spherical and non-linearly patterns of data structure, to be clustered easily. In addition, in order to enhance the results, Particle Swarm Optimization (PSO) algorithm is used to tune the kernel parameters, cluster centers and to optimize features selection. The efficiency of this method was evaluated by using multi-temporal UAVSAR L-band images acquired over an agricultural area near Winnipeg, Manitoba, Canada, during June and July in 2012. The results demonstrate more accurate crop maps using the proposed method when compared to the classical approaches, (e.g. 12% improvement in general). In addition, when the optimization technique is used, greater improvement is observed in crop classification, e.g. 5% in overall. Furthermore, a strong relationship between Freeman-Durden volume scattering component, which is related to canopy structure, and phenological growth stages is observed.     

基于OLI影像多参数设置的SVM分类研究

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.