共查询到20条相似文献，搜索用时 406 毫秒
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投影寻踪学习网络的遥感影像分类 总被引：2，自引：0，他引：2
采用投影寻踪(projection pursuit,PP)学习网络方法建立了一种新的遥感影像分类模型。该方法结合了统计学中投影寻踪算法节点函数灵活的非参数估计特点和人工神经网络的自学习功能,具有简捷的网络结构和良好的鲁棒性能。利用苏州市TM影像进行了分类实验,将分类结果与BP神经网络和最大似然法的分类结果相比较,投影寻踪学习网络的分类精度较高,具有一定的实用性。 相似文献
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遥感图像分类时,如果类别不明,K-means算法随机选取不同初值会导致分类结果有较大的差异.针对此问题,提出了一种改进的K-means算法.首先对遥感数据进行对数变换;然后采用主成分变换,依据主成分贡献率(≥85%)选择参与分类的主成分数;根据第一主成分的概率密度函数确定初始分类数和初始分类中心,并确定初始分类标签作为... 相似文献
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针对近年来极化SAR影像深度学习分类研究取得了显著的进展,但仍缺少全面系统的对比分析问题,该文首先讨论了极化SAR处理与分类中常用的深度学习网络结构,然后使用极化SAR分类研究的典型数据集,对多个主要深度学习算法的分类结果进行了对比分析,并对常用极化目标分解在深度学习分类算法中的应用进行了对比.实验表明,各类算法有不同的适用场景,同一场景不同算法的精度有时表现很大的差异.深度网络的选择、网络参数的优化和极化信息的应用依旧是该领域未来重点研究方向. 相似文献
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为提高遥感影像分类精度,本文提出基于混沌遗传算法(Chaos Genetic Algorithm)的遥感影像分类方法.首先应用混沌遗传算法对样本进行自学习得到全局最优的聚类中心,然后通过得到的聚类中心对整幅影像进行分类.该方法利用混沌变量的遍历性,进行粗粒搜索,优化遗传算法的初始种群,从而提高收敛速度;对经过选择算子、... 相似文献
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卷积神经网络等深度学习模型已经在高光谱影像分类任务中取得了理想的结果.然而,由于传统神经元只能进行标量计算,现有的深度学习模型无法对高光谱影像特征的实例化参数进行建模,因此无法在邻域范围受限的条件下获得令人满意的分类效果.通过引入胶囊网络结构设计了一种新型网络模型,该模型利用胶囊神经元进行向量计算,并利用权重矩阵编码特... 相似文献
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针对现有城市地铁网络结构评价研究的不足,该文提出运用中心性评价方法分析地铁网络中站点的重要程度,实现对网络结构的评价:引入中心性方法的度中心性、介中心性和邻近中心性指标,提出一种基于客观权重赋权法的系统中心性评价指标,对地铁网络的中心性进行计算。以广州市为研究区域,分析了广州市地铁网络中心性空间分布和频率分布特征。结果表明,较现有线网,广州市地铁规划线网各项中心性指标分布更为均匀,市辖区间联系更为紧密。中心性评价指标能够对非采用单一票制的广州地铁系统的网络结构进行评价,为规划方案的制定提供参考。 相似文献
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图像相似度学习是指通过网络学习图像内容信息来预测两张图像是否匹配。迄今为止，基于卷积神经网络改进的变体网络有效提升了学习效率，但由于提取特征比较单一无法准确描述图像特征，导致相似度学习效率较低。为此，本文提出一种基于卷积神经网络结构的双分支网络。该网络为左右分支网络结构相同，但权值不共享，网络输入为双分支输入。首先由左右分支网络分别提取单通道图像特征；然后通过特征融合层进行特征融合；最后将融合特征直接输入全连接层进行相似度学习，既改善了提取的图像特征多样性，又加快了模型训练速度。在实验室工业相机拍摄的芯片卡槽图像数据集上进行对比试验，结果表明，相比其他模型，本文提出的模型具有较强的网络学习能力和模型泛化能力，准确率高达97.96%。 相似文献
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人工神经网络在多源遥感影像分类中的应用 总被引：20，自引：1，他引：19
在研究人工神经网络理论的基础上,应用动量法和学习率自适应调整的策略,改变ＢＰ神经网络的主要缺点,提出对同一地区空间配准的Ｌａｎｄｓａｔ ＴＭ３,ＴＭ４,ＴＭ５影像和航空ＳＡＲ影像,一方面采用该种网络对两类影像分别进行分类,将其分类结果按提出的融合规则进行分类融合得到最终分类结果,另一方面采用该网络对两类影像进行融合分类,得到相应分类结果,比较这两者的结果表明,基于改进的ＢＰ神经网络用于土地利用分类 相似文献
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Improving neural network performance on the classification of complex geographic datasets 总被引：4，自引：0，他引：4
Neural Networks are now established computational tools used for search minimisation and data classification. They offer
some highly desirable features for landuse classification problems since they are able to take in a variety of data types,
recorded on different statistical scales, and combine them. As such, neural networks should offer advantages of increased
accuracy. However, a barrier to their general acceptance and use by all but `experts' is the difficulty of configuring the
network initially.
This paper describes the architectural problems of applying neural networks to landcover classification exercises in geography
and details some of the latest developments from an ongoing research project aimed at overcoming these problems. A comprehensive
strategy for the configuration of neural networks is presented, whereby the network is automatically constructed by a process
involving initial analysis of the training data. By careful study of the functioning of each part of the network it is possible
to select the architecture and initial weights on the node connections so the constructed network is `right first time'. Further
adaptations are described to control network behaviour, to optimise functioning from the perspective of landcover classification.
The entire configuration process is encapsulated by a single application which may be treated by the user as a `black box',
allowing the network to the applied in much the same way as a maximum likelihood classifier, with no further effort being
required of the user. 相似文献
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人工神经网络具有较强的非线性映射能力。本文介绍了神经网络BP算法的一些改进措施。这些措施可以提高BP算法的学习收敛速度,同时也可以提高BP网络性能的稳定性。为避免软土路基沉降传统计算方法中各种人为因素的干扰,本方法利用实测资料直接建模。基于改进的BP神经网络模型,建立了可依据现场量测信息对软基路堤沉降量随时间而发展的过程进行动态预报的分析方法。本文所建立的BP算法模型比较独特,利用该模型预测软土路基沉降精度高,预测结果的稳定性好。 相似文献
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《International Journal of Digital Earth》2013,6(4):343-358
Abstract The Digital Earth concept has attracted much attention recently and this approach uses a variety of earth observation data from the global to the local scale. Imaging techniques have made much progress technically and the methods used for automatic extraction of geo-ralated information are of importance in Digital Earth science. One of these methods, artificial neural networks (ANN) techniques, have been effectively used in classification of remotely sensed images. Generally image classification with ANN has been producing higher or equal mapping accuracies than parametric methods. Comparative studies have, in fact, shown that there is no discernible difference in classification accuracies between neural and conventional statistical approaches. Only well designed and trained neural networks can present a better performance than the standard statistical approaches. There are, as yet, no widely recognised standard methods to implement an optimum network. From this point of view it might be beneficial to quantify ANN's reliability in classification problems. To measure the reliability of the neural network might be a way of developing to determine suitable network structures. To date, the problem of confidence estimation of ANN has not been studied in remote sensing studies. A statistical method for quantifying the reliability of a neural network that can be used in image classification is investigated in this paper. For this purpose the method is to be based on a binomial experimentation concept to establish confidence intervals. This novel method can also be used for the selection of an appropriate network structure for the classification of multispectral imagery. Although the main focus of the research is to estimate confidence in ANN, the approach might also be applicable and relevant to Digital Earth technologies. 相似文献
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当某一问题很难甚至无法用数学方法建立精确模型时,人工神经网络的方法则显示了优势。对于一个具体问题,采用何种网络结构是至关重要的。本文以美国内华达州Cuprite矿区成像光谱数据特征矿物识别为例,采用6种不同结构的多层前馈网络模型,从其训练难度、运算效率及识别效果等方面进行了综合对比分析。 相似文献
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针对高光谱图像分类中对光谱信息利用不足的问题,提出一种基于卷积神经网络在光谱域开展的分类算法。该算法通过构建五层网络结构,逐像素对光谱信息开展分析,将全光谱段集合作为输入,利用神经网络展开代价函数值的计算,实现对光谱特征的提取与分类。实验中采用三组高光谱遥感影像数据进行对比分析,以India Pines数据集为例,提出的基于卷积神经网络的分类方法的分类正确率达到90.16%,比RBF-SVM方法高出2.56%,相比三种传统的深度学习方法高出1%~3%,训练速度也较为理想。实验结果表明,本文所提出的算法充分利用了高光谱图像中逐像素点的光谱域信息,能够有效提高分类正确率。与传统学习算法相比,在较少训练样本的情况下,更能发挥其良好的分类性能。 相似文献
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Neural network-based GPS GDOP approximation and classification 总被引：2，自引：2，他引：2
In this paper, the neural network (NN)-based navigation satellite subset selection is presented. The approach is based on approximation or classification of the satellite geometry dilution of precision (GDOP) factors utilizing the NN approach. Without matrix inversion required, the NN-based approach is capable of evaluating all subsets of satellites and hence reduces the computational burden. This would enable the use of a high-integrity navigation solution without the delay required for many matrix inversions. For overcoming the problem of slow learning in the BPNN, three other NNs that feature very fast learning speed, including the optimal interpolative (OI) Net, probabilistic neural network (PNN) and general regression neural network (GRNN), are employed. The network performance and computational expense on NN-based GDOP approximation and classification are explored. All the networks are able to provide sufficiently good accuracy, given enough time (for BPNN) or enough training data (for the other three networks). 相似文献
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在前人工作的基础上,针对传统神经网络存在收敛速度慢、易限于局部极小等问题,提出了几点改进措施,并将其应用到某水库土石坝变形观测资料的分析中,分析成果表明,改进的神经网络可以克服传统神经网络的缺点,很好地剔除粗差,使预测的精度和学习效率都有明显提高;充分证明了这一理论具有很强的实用性。 相似文献