共查询到20条相似文献，搜索用时 31 毫秒
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本论文尝试讨论两个主题：主题一为利用主成分分析PCA方法应用于像元阶层资料融合技术的研究。主题二为应用Dempster-Shafer evidence theory方法于特征阶层数据融合技术的研究。在第一个主题中，由于合成孔径雷达的数据具有全偏极特性，在此选取了对植被较为敏感的HV极化合成孔径雷达数据，与具有光谱特性的光学SPOT数据做数据融合处理以利接下来的地物分类。首先，本研究利用小波转换技术来滤除合成孔径雷达斑驳噪声，在接下来融合步骤中，主成分分析出来的第一部分（PCI）是用做完滤除噪声后的合成孔径雷达取代，在数据融合后，进行地物分类是采用最大似然法来分类融合影像。在第二个主题中，利用全偏极雷达数据的极化特性结合SPOT数据的光谱特性，其主要目的是为了增加分类的精确度。首先使用李式滤波器滤除全偏极雷达数据噪声，接下来同样是使用采用最大似然法来分类融合影像，（不同的在于全偏极雷达影像使用Wishart几率分布，在光学影像采用multivariate Gaussian几率分布）将每个类别中每个像元属于某个类别的几率值计算出来，再利用Dempster-Shafer evidence theory来结合这些类别的机率值。最后产生出一张新的分类影像。实验的结果显示分类的精确度比较于未融合的资料都有明显提升的效果，也证明了此两个数据融合方法对于不同数据特性的融合都是很成功的。 相似文献
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针对灰度直方图在影像分类时需要考虑阈值和K-最近邻分类算法分类效率低等问题,提出了一种基于灰度直方图与KNN相结合的影像分割算法:首先对待分割的影像进行灰度直方图统计;其次利用灰度直方图对影像进行硬阈值的划分,得到已知类别样本和未标记样本;然后选择一定数量已知类别的样本对KNN分类器进行训练;最后利用KNN分类器对未标记样本进行类别划分,得到最终分割图像。实验结果表明,该算法结合了灰度直方图高效性和KNN高精度的优势,避免了直方图分割最佳阈值的选取;与传统的KNN算法相比,本文算法大大提高了分类效率,且精度相当,满足实际生产应用的需求。 相似文献
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面向对象的遥感影像多层次迭代分类方法研究 总被引：3，自引：0，他引：3
在分析应用对象化分析方法改进高空间分辨率遥感影像分类技术的基础上,提出了应用多层次的迭代模型改进分类流程,在自适应的迭代过程中有效地结合主导类别选择、高级对象特征计算、基于互信息的特征选择等技术提高对象化方法中丰富的影像特征的利用效率,同时,有机结合像素级特征信息弥补对象化特征。通过对SPOT5影像与航空影像两种数据源... 相似文献
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不同于一般分类算法基于像素统计的分类,忽略了地物的散射特性,文中提出了一种保持地物散射特性的分类方法。这种方法将Singh提出的Singh四分量分解与基于复Wishart分布的最大似然分类器相结合,对高分三号全极化影像进行分类。利用Singh四分量分解获得表面散射、体散射、二次散射和螺旋体散射,然后将前3种基础散射分别划分为多个聚类,根据复Wishart距离进行类间合并,直到获得指定类别数,输入复Wishart分类器进行迭代分类,最后进行类别合并获得最终分类结果。试验表明本文算法具有较好的分类效果且验证了利用高分三号全极化卫星数据进行影像分类的可行性。 相似文献
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Reliability of the scattering model based polarimetric SAR (PolSAR) speckle filter depends upon the accurate decomposition and classification of the scattering mechanisms. This paper presents an improved scattering property based contextual speckle filter based upon an iterative classification of the scattering mechanisms. It applies a Cloude-Pottier eigenvalue-eigenvector decomposition and a fuzzy H/α classification to determine the scattering mechanisms on a pre-estimate of the coherency matrix. The H/α classification identifies pixels with homogeneous scattering properties. A coarse pixel selection rule groups pixels that are either single bounce, double bounce or volume scatterers. A fine pixel selection rule is applied to pixels within each canonical scattering mechanism. We filter the PolSAR data and depending on the type of image scene (urban or rural) use either the coarse or fine pixel selection rule. Iterative refinement of the Wishart H/α classification reduces the speckle in the PolSAR data. Effectiveness of this new filter is demonstrated by using both simulated and real PolSAR data. It is compared with the refined Lee filter, the scattering model based filter and the non-local means filter. The study concludes that the proposed filter compares favorably with other polarimetric speckle filters in preserving polarimetric information, point scatterers and subtle features in PolSAR data. 相似文献
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一种利用Cloude-Pottier分解和极化白化滤波的全极化SAR图像分类算法 总被引：6，自引：2，他引：4
提出了一种新的基于Cloude-Pottier分解和极化白化滤波(PWF)的全极化SAR数据分类算法。该算法利用PWF的结果来代替反熵A对复WishartH/α分类结果进行进一步细化,按PWF的值将复WishartH/α分类结果由8类分为16类,然后再次进行Wishart迭代分类。实验结果表明,该算法能有效地提高分类精度,分类结果明显优于常规的复WishartH/α分类结果和复WishartH/α/A分类结果。 相似文献
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Johan Beekhuizen Keith C. Clarke 《International Journal of Applied Earth Observation and Geoinformation》2010
The classification of satellite imagery into land use/cover maps is a major challenge in the field of remote sensing. This research aimed at improving the classification accuracy while also revealing uncertain areas by employing a geocomputational approach. We computed numerous land use maps by considering both image texture and band ratio information in the classification procedure. For each land use class, those classifications with the highest class-accuracy were selected and combined into class-probability maps. By selecting the land use class with highest probability for each pixel, we created a hard classification. We stored the corresponding class probabilities in a separate map, indicating the spatial uncertainty in the hard classification. By combining the uncertainty map and the hard classification we created a probability-based land use map, containing spatial estimates of the uncertainty. The technique was tested for both ASTER and Landsat 5 satellite imagery of Gorizia, Italy, and resulted in a 34% and 31% increase, respectively, in the kappa coefficient of classification accuracy. We believe that geocomputational classification methods can be used generally to improve land use and land cover classification from imagery, and to help incorporate classification uncertainty into the resultant map themes. 相似文献
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面向高光谱图像分类的半监督空谱判别分析 总被引：2，自引：2，他引：0
为充分利用高光谱图像蕴藏的空间信息提升分类精度,提出了面向高光谱图像分类的半监督空谱判别分析(S3 DA)算法。考虑高光谱图像数据集的空间一致性,首先利用少量标记样本定义类内散度矩阵,保存数据集同类像元的光谱近邻结构;再利用无标记样本定义空间近邻像元散度矩阵,揭示像元间的空间近邻结构和地物的空间分布结构信息。S3 DA既保持数据集在光谱域的可分性,又保存了无标记样本蕴藏的空间域近邻结构,增强了同类像元和空间近邻像元在投影子空间的聚集性,从而提升分类性能。在PaviaU和Indian Pines数据集的试验表明,总体分类精度分别达到81.50%和71.77%。与传统的光谱方法比较,该算法能有效提升高光谱图像数据集的地物分类精度。 相似文献
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RADARSAT-2全极化SAR数据地表覆盖分类 总被引：1，自引：0，他引：1
全极化合成孔径雷达(SAR)能够测量每一观测目标的全散射矩阵,但地物分布的复杂性往往造成不同地物具有相似的后向散射信号特征,因而增加了地物信息提取的难度。文中基于北京地区的RADARSAT-2全极化雷达数据,在图像处理的特征分解的基础上,利用PolSARPro软件提取包含地物散射机理信息的各种极化参数,按H-α、A-α、H-A对全极化SAR影像进行基于散射机理的分类,继而将分类结果作为Wishart H/A/α、Wishart H/α的初始类别划分。最后,采用决策树分类算法对基于Wishart分布的监督分类及以上两种分类算法进行融合处理,从而实现地物的分类,并将分类结果与经典的分类算法进行对比分析,验证了文中方法的有效性。 相似文献
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This paper presents a novel method for supervised water-body extraction and water-body types identification from Radarsat-2 fully polarimetric (FP) synthetic aperture radar (SAR) data in complex urban areas. First, supervised water-body extraction using the Wishart classifier is performed, and the false alarms that are formed in built-up areas are removed using morphological processing methods and spatial contextual information. Then, the support vector machine (SVM), the classification and regression tree (CART), TreeBagger (TB), and random forest (RF) classifiers are introduced for water-body types (rivers, lakes, ponds) identification. In SAR images, certain other objects that are misclassified as water are also considered in water-body types identification. Several shape and polarimetric features of each candidate water-body are used for identification. Radarsat-2 PolSAR data that were acquired over Suzhou city and Dongguan city in China are used to validate the effectiveness of the proposed method, and the experimental results are evaluated at both the object and pixel levels. We compared the water-body types classification results using only shape features and the combination of shape and polarimetric features, the experimental results show that the polarimetric features can eliminate the misclassifications from certain other objects like roads to water areas, and the increasement of classification accuracy embodies at both the object and pixel levels. The experimental results show that the proposed methods can achieve satisfactory accuracies at the object level [89.4% (Suzhou), 95.53% (Dongguan)] and the pixel level [96.22% (Suzhou), 97.95% (Dongguan)] for water-body types classification, respectively. 相似文献
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Li LuoGiorgos Mountrakis 《ISPRS Journal of Photogrammetry and Remote Sensing》2011,66(5):579-587
A classification model was demonstrated that explored spectral and spatial contextual information from previously classified neighbors to improve classification of remaining unclassified pixels. The classification was composed by two major steps, the a priori and the a posteriori classifications. The a priori algorithm classified the less difficult image portion. The a posteriori classifier operated on the more challenging image parts and strived to enhance accuracy by converting classified information from the a priori process into specific knowledge. The novelty of this work relies on the substitution of image-wide information with local spectral representations and spatial correlations, in essence classifying each pixel using exclusively neighboring behavior. Furthermore, the a posteriori classifier is a simple and intuitive algorithm, adjusted to perform in a localized setting for the task requirements. A 2001 and a 2006 Landsat scene from Central New York were used to assess the performance on an impervious classification task. The proposed method was compared with a back propagation neural network. Kappa statistic values in the corresponding applicable datasets increased from 18.67 to 24.05 for the 2006 scene, and from 22.92 to 35.76 for the 2001 scene classification, mostly correcting misclassifications between impervious and soil pixels. This finding suggests that simple classifiers have the ability to surpass complex classifiers through incorporation of partial results and an elegant multi-process framework. 相似文献
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In automated remote sensing based image analysis, it is important to consider the multiple features of a certain pixel, such as the spectral signature, morphological property, and shape feature, in both the spatial and spectral domains, to improve the classification accuracy. Therefore, it is essential to consider the complementary properties of the different features and combine them in order to obtain an accurate classification rate. In this paper, we introduce a modified stochastic neighbor embedding (MSNE) algorithm for multiple features dimension reduction (DR) under a probability preserving projection framework. For each feature, a probability distribution is constructed based on t-distributed stochastic neighbor embedding (t-SNE), and we then alternately solve t-SNE and learn the optimal combination coefficients for different features in the proposed multiple features DR optimization. Compared with conventional remote sensing image DR strategies, the suggested algorithm utilizes both the spatial and spectral features of a pixel to achieve a physically meaningful low-dimensional feature representation for the subsequent classification, by automatically learning a combination coefficient for each feature. The classification results using hyperspectral remote sensing images (HSI) show that MSNE can effectively improve RS image classification performance. 相似文献
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为了实现地物精准分类,需要有效地提取与分析高光谱遥感图像中丰富的空—谱信息。提出一种适用于高光谱遥感图像分类的变异系数与卷积神经网络相结合(CV-CNN)的方法。这种新方法引入变异系数的思想来衡量高光谱遥感图像不同波段之间的相似性和差异性,从而提出类间变异系数(CVIE)和类内变异系数(CVIA)的概念。通过计算(CVIE)~2/CVIA的值来剔除高光谱遥感图像中的低效波段,然后提取每个像素的空一谱信息,并对其进行2维矩阵化操作,转化为便于卷积神经网络(CNN)输入的灰度图像,最后采用自行构建的适合于高光谱遥感图像分类的CNN模型进行分类。Indian Pines和Pavia University两组数据的实验结果表明,该方法在两种数据集下的总体精度分别达到98.69%和99.66%,有效地改善了高光谱遥感图像的分类精度。 相似文献
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对现有马尔柯夫随机场 (MRF)参数的解求方法作了详细分析。针对现有方法存在的缺点 ,提出基于遗传算法的MRF参数解求方法 ;分析讨论了求解的稳定性 ;并利用解的稳定性作为纹理分类的判据 ,作了航空影像纹理的分类实验。实验结果表明 ,求解的方法是正确的。用解的稳定性作为判据的纹理分类效果 ,通过对比实验证明是有效的。 相似文献
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Chanussot J. Benediktsson J.A. Fauvel M. 《Geoscience and Remote Sensing Letters, IEEE》2006,3(1):40-44
The classification of very high-resolution remotely sensed images from urban areas is addressed. Previous studies have shown the interest of exploiting the local geometrical information of each pixel to improve the classification. This is performed using the derivative morphological profile (DMP) obtained with a granulometric approach, using opening and closing operators. For each pixel, this DMP constitutes the feature vector on which the classification is based. In this letter, we present an interpretation of the DMP in terms of a fuzzy measurement of the characteristic size and contrast of each structure. This fuzzy measure can be compared to predefined possibility distributions to derive a membership degree for a set of given classes. The decision is taken by selecting the class with the highest membership degree. This model is illustrated and validated in a classification problem using IKONOS images. 相似文献
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高分辨率遥感影像土地利用变化检测方法研究 总被引：3，自引：0，他引：3
提出一种利用高分辨率遥感影像进行土地利用变化检测的方法。以土地利用图为辅助数据,通过土地利用图和遥感影像的配准套合,获取影像像斑;同时,对遥感影像进行基于像素的监督分类,获取概略的类别图;再根据像斑内像素的类别编码完成子像斑的划分。以子像斑为影像分析的基本单位提取特征,以相关系数为相似性测度衡量不同时期子像斑的特征相似性,用ROC曲线(接受者操作特性曲线)代替经验选取的方法自动获取变化阈值,确定像斑是否发生变化。以武汉市区局部QuickBird 2002年和2005年多光谱影像、相同地区2002年1∶10 000土地利用图为实验数据进行了算法的实验,结果显示绝大部分的变化区域都可以被提取出来,实验方法可行。 相似文献
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针对经典极化分类算法在处理机载X波段SAR数据时将过多地物分为体散射类型,并且容易受噪声影响,分类结果存在大量误分现象的问题,通过对机载X波段SAR数据非监督分类方法的研究,提出将极化干涉信息用于机载X波段极化干涉SAR数据的分类。通过运用极化干涉数据进行目标分解得到参数A1和A2对数据进行初始分类,然后结合改进的Wishart最大似然分类算法来进行地物的自适应分类。实验结果表明,该方法能有效避免平地效应的影响,抗噪性好,能正确区分三种典型散射类型,分类效果明显优于极化分类效果。 相似文献
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为了充分利用不同极化特征信息,并将其有效地结合,提出一种结合粒度计算的全极化合成孔径雷达（synthetic aperture radar,SAR）影像分类方法。在不同极化目标分解特征组合的基础上引入影像纹理信息,利用光滑支持向量机（smooth support vector machine,SSVM）对不同特征组合进行类别划分获得粗粒度空间,采用商空间对粗粒度进行合并;根据全极化SAR影像分布特性,以相干矩阵作为新的特征矢量,利用Wishart测度代替传统欧氏距离对差异粒度进行推理,通过合并推理结果与合成论域,获得精细分类结果。采用L波段San Francisco地区和荷兰Flevoland地区的全极化SAR影像进行分类试验,结果表明:利用SSVM算法对全极化SAR影像进行粗粒度划分,并采用Wishart距离对差异粒度推理综合,总体分类效果优于结合纹理信息的Cloude及Yamaguchi4分类结果,且优于基于线性特征融合进行监督分类方法。 相似文献