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Knowledge discovery from multispectral satellite images 总被引：1，自引：0，他引：1
A new approach to extract knowledge from multispectral images is suggested. We describe a method to extract and optimize classification rules using fuzzy neural networks (FNNs). The FNNs consist of two stages. The first stage represents a fuzzifier block, and the second stage represents the inference engine. After training, classification rules are extracted by backtracking along the weighted paths through the FNN. The extracted rules are then optimized by use of a fuzzy associate memory bank. We use the algorithm to extract classification rules from a multispectral image obtained with a Landsat Thematic Mapper sensor. The scene represents the Mississippi River bottomland area. In order to verify the rule extraction method, measures such as the overall accuracy, producer's accuracy, user's accuracy, kappa coefficient, and fidelity are used. 相似文献
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广义马尔可夫随机场及其在多光谱纹理影像分类中的应用 总被引：1，自引：0，他引：1
在二维马尔可夫随机场模型的基础上，提出顾及波段间的空间相关性，发展了一种适用于多光谱纹理影像分类的广义马尔可夫随机场模型。鉴于广义马尔可夫随机场模型的复杂性，利用最大伪似然法建立了求解模型参数的简化方程式，实现了纹理特征的快速提取。结合提取的纹理特征影像和光谱特征影像，采用概率松弛算法实现影像的分类。实验证明，提出的基于广义马尔可夫随机场的多光谱纹理影像分类算法克服了传统的基于光谱特征的分类算法的局限性，提高了纹理影像的分类精度。 相似文献
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JIAO Limin LIU Yaolin 《地球空间信息科学学报》2007,10(2):151-156
A novel model of land suitability evaluation is built based on computational intelligence (CI). A fuzzy neural network (FNN) is constructed by the integration of fuzzy logic and artificial neural network (ANN). The structure and process of this network is clear. Fuzzy rules (knowledge) are expressed in the model explicitly, and can be self-adjusted by learning from samples. Genetic algorithm (GA) is employed as the learning algorithm to train the network, and makes the training of the model efficient. This model is a self-learning and self-adaptive system with a rule set revised by training. 相似文献
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多光谱遥感图像土地利用分类区域多中心方法 总被引：1，自引：0，他引：1
针对遥感图像土地利用一种类别由多种地物组成,存在难以求取类别光谱特征多元分布模型的问题,分析了多光谱遥感图像土地利用的光谱特征和区域多中心特征,提出了一种光谱信息和区域信息基于规则的区域多中心分类方法,以类别的类内中心集合表征类别模式,以区域为分类单元,以区域单元含类别类内中心数和区域单元中属于某种类别的像元占单元总像元的百分比为分类准则;采用类内中心表征类别模式和基于规则的分类方法,较好地解决了土地利用类别由多种地物组成、类别模式不满足多元正态分布的问题,由于类别区域单元多中心特性差异大,分类规则的建立及训练样本的选择易于实现。实验表明:该方法能提高分类精度4%—6%。 相似文献
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A fuzzy topology-based maximum likelihood classification 总被引：2，自引：0，他引：2
Classification is one of the most widely used remote sensing analysis techniques, with the maximum likelihood classification (MLC) method being a major tool for classifying pixels from an image. Fuzzy topology, in which the set concept is generalized from two values, {0, 1}, to the values of a continuous interval, [0, 1], is a generalization of ordinary topology and is used to solve many GIS problems, such as spatial information management and analysis. Fuzzy topology is induced by traditional thresholding and as such gives a decomposition of MLC classes.Presented in this paper is an image classification modification, by which induced threshold fuzzy topology is integrated into the MLC method (FTMLC). Hence, by using the induced threshold fuzzy topology, each image class in spectral space can be decomposed into three parts: an interior, a boundary and an exterior. The connection theory in induced fuzzy topology enables the boundary to be combined with the interior. That is, a new classification method is derived by integrating the induced fuzzy topology and the MLC method. As a result, fuzzy boundary pixels, which contain many misclassified and over-classified pixels, are able to be re-classified, providing improved classification accuracy. This classification is a significantly improved pixel classification method, and hence provides improved classification accuracy. 相似文献
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机载多光谱LiDAR的随机森林地物分类 总被引：1，自引：0，他引：1
机载多光谱LiDAR技术利用激光进行探测和测距，不仅可以快速获取地面物体的三维坐标，还可以获得多个波段的地物光谱信息，可广泛用于地形测绘、土地覆盖分类、环境建模、森林资源调查等。本文提出了多光谱LiDAR的随机森林地物分类方法。该方法通过对LiDAR强度数据和高程数据提取分类特征，完成多光谱LiDAR的随机森林地物分类；并分析随机森林的特征贡献度特性，采用后向特征选择方法实现分类特征选择。通过对加拿大Optech Titan多光谱LiDAR数据的试验表明：随机森林方法可以获得较好的地物分类精度，而且可以适当地去除部分冗余和相关的特征，从而有效提高分类精度。 相似文献
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模糊特征的选择影响着模糊分类的结果。从大量模糊特征中选择出有效特征进行分类,存在着一定的难度。粒子群优化算法(PSO)是基于群体智能的新型进化计算技术,具有自适应、自组织等智能特性,具有强大的寻找最优解的能力。将离散二进制PSO用于模糊特征选择,实现了基于PSO的模糊特征自适应选择方法,并通过航空和卫星遥感影像的模糊分类实验,验证了此方法的有效性。 相似文献
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高分辨率多光谱影像城区建筑物提取研究 总被引：4，自引：2，他引：2
城区高空间分辨率遥感数据由于存在大量同物异谱和异物同谱现象,应用传统的基于像元光谱分类的方法进行建筑物分类提取难以取得满意的效果。本文发展了一种从高分辨率Ikonos卫星影像上基于知识规则的面向对象分类提取城区建筑物方法,包括如下步骤：(1)融合1m全色和4m多光谱波段影像,生成1m分辨率的多光谱融合影像;（2）分割融合影像;（3）执行基于对象光谱的最近邻监督分类;（4）应用模糊逻辑分类器结合光谱、空间、纹理和上下文特征等知识规则进行建筑物分类。精度统计结果表明,本文提出的分类方法提取城区建筑物取得了93%的精度。 相似文献
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天宫一号高光谱数据尚未得到普遍应用,其数据的质量和应用潜力仍在进一步实践求证和挖掘.See5.0数据挖掘工具是一种能够找出训练样本中模式类隐含特征,并可以自动建立决策规则的分类算法,可避免人为建立分类规则的主观性.本文首先通过光谱曲线分析,选择地物光谱分离性最好的波段组合,然后利用See5.0工具生成规则集,再利用规则集对同一幅天宫一号高光谱数据在不同分类级别上进行分类,并利用相同的验证样本进行精度验证.经过光谱分析发现分类不同森林类型的最佳谱段中心波长分别为:655 nm、673 nm、802 nm、866 nm、984 nm,See5.0分类结果表明在同一树种不同生长期及不同亚种的分类级别上,分类精度在45%以下,表现出了一定局限性,但在树种分类级别上,天宫一号数据表现出了高光谱的优越性,分类精度皆在80%以上,植被类型分类级别,分类精度可达到90%以上. 相似文献
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基于计算智能的土地适宜性评价模型 总被引：22，自引：2，他引：22
将计算智能理论引入土地评价领域，构建了一个全新的土地适宜性评价模型。首先基于模糊逻辑和人工神经网络构造了一个模糊神经网络模型，然后采用改进的遗传算法进行训练，能够快速收敛到最优解，对初始的规则库进行修正，形成了一个自学习、自适应的评价系统。 相似文献
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Synthetic Aperture Radar (SAR) data are of high interest for different applications in remote sensing specially land cover classification. SAR imaging is independent of solar illumination and weather conditions. It can even penetrate some of the Earth’s surface materials to return information about subsurface features. However, the response of radar is more a function of geometry and structure than a surface reflection occurs in optical images. In addition, the backscatter of objects in the microwave range depends on the frequency of the band used, and the grey values in SAR images are different from the usual assumption of the spectral reflectance of the Earth’s surface. Consequently, SAR imaging is often used as a complementary technique to traditional optical remote sensing. This study presents different ensemble systems for multisensor fusion of SAR, multispectral and LiDAR data. First, in decision ensemble system, after extraction and selection of proper features from each data, crisp SVM (Support Vector Machine) and Fuzzy KNN (K Nearest Neighbor) are utilized on each feature space. Finally Bayesian Theory is applied to fuse SVMs when Decision Template (DT) and Dempster Shafer (DS) are applied as fuzzy decision fusion methods on KNNs. Second, in feature ensemble system, features from all data are applied on a cube. Then classifications were performed by SVM and FKNN as crisp and fuzzy decision making system respectively. A co-registered TerrraSAR-X, WorldView-2 and LiDAR data set form San Francisco of USA was available to examine the effectiveness of the proposed method. The results show that combinations of SAR data with different sensor improves classification results for most of the classes. 相似文献
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Classification is always the key point in the field of remote sensing. Fuzzy c-Means is a traditional clustering algorithm that has been widely used in fuzzy clustering. However, this algorithm usually has some weaknesses, such as the problems of falling into a local minimum, and it needs much time to accomplish the classification for a large number of data. In order to overcome these shortcomings and increase the classification accuracy, Gustafson-Kessel （GK） and Gath-Geva （GG） algorithms are proposed to improve the traditional FCM algorithm which adopts Euclidean distance norm in this paper. The experimental result shows that these two methods are able to detect clusters of varying shapes, sizes and densities which FCM cannot do. Moreover, they can improve the classification accuracy of remote sensing images. 相似文献
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本文在研究BP神经网络和模糊理论的基础上,提出了传统BP算法的一种改进方法和基于模糊系统的神经网络遥感影像分类方法。通过试验表明:基于模糊技术的神经网络分类方法要优于BP神经网络方法,取得了令人满意的效果。 相似文献
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Lukas Zebedin Andreas Klaus Barbara Gruber-Geymayer Konrad Karner 《ISPRS Journal of Photogrammetry and Remote Sensing》2006,60(6):413
This paper describes the fusion of information extracted from multispectral digital aerial images for highly automatic 3D map generation. The proposed approach integrates spectral classification and 3D reconstruction techniques. The multispectral digital aerial images consist of a high resolution panchromatic channel as well as lower resolution RGB and near infrared (NIR) channels and form the basis for information extraction.Our land use classification is a 2-step approach that uses RGB and NIR images for an initial classification and the panchromatic images as well as a digital surface model (DSM) for a refined classification. The DSM is generated from the high resolution panchromatic images of a specific photo mission. Based on the aerial triangulation using area and feature-based points of interest the algorithms are able to generate a dense DSM by a dense image matching procedure. Afterwards a true ortho image for classification, panchromatic or color input images can be computed.In a last step specific layers for buildings and vegetation are generated and the classification is updated. 相似文献
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一种顾及上下文的遥感影像模糊聚类 总被引：7，自引：1，他引：7
模糊聚类是非监督分类中的一类重要方法。传统的模糊聚类方法应用于遥感影像的非监督分类时，均未考虑到邻域像元间的统计依赖关系即上下文信息。针对这一缺陷，在Markov随机场模型框架下，引入了空间隶属度概念，提出了一种顾及上下文信息的模糊聚类算法，有效地提高了聚类精度和抗噪声能力。针对需要预先指定聚类个数的问题，采用了一种兼顾类别内部紧密程度和类别之间分离程度的评价指标，用以检验聚类结果的有效性。从而找出最优的聚类个数，在一定程度上提高了聚类结果的客观性。最后通过实验验证了本文算法的有效性。 相似文献
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针对纹理影像先估计出其马尔可夫随机场参数，后运用多元统计分析中模糊聚类分析的数学方法进行定量分类，从而为解决划分上的不确定性现象找出描述方法，获得客观的分类结果 相似文献
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基于改进的像素级和对象级的遥感影像合成分类 总被引：1，自引：0，他引：1
像素级和对象级的分类研究分别作为两个独立的方向开展,二者的结合与优势互补还没有引起关注。本文对像素级和对象级分类方法的结合做出新的探索,提出了基于改进的像素级和对象级的遥感影像合成分类方法。首先,以一种改进的RBF神经网络分类器进行像素级分类、以一种基于改进模糊支持向量机和决策树的层次分类模型进行对象级分类,获得多层次分类结果。然后,提出了一个具体的像素级分类与对象级分类的合成算法,对多层次分类结果进行合成。实验表明,合成分类方法能有效地提高分类结果的精度,提供比单一像素级方法或对象级方法更准确的分类结果。 相似文献