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Neural network based on rough sets and its application to remote sensing image classification 总被引:2,自引:0,他引:2
WUZhaocong LIDeren 《地球空间信息科学学报》2002,5(2):17-21
This paper presents a new kind of back propagation neural network(BPNN) based on rough sets,called rough back propagation neural network (RBPNN).The architecture and training method of RBPNN are presented and the survey and analysis of RBPNN for the classification of remote sensing multi-spectral image is discussed.The successful application of RBPNN to a land cover classification illustrates the simple computation and high accuracy of the new neural network and the flexibility and practicality of this new approach. 相似文献
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基于GIS和神经网络的森林植被分类 总被引:2,自引:0,他引:2
本文综述了国际遥感分类研究,使用Landsat7 ETM+遥感数据和地理辅助数据,应用BP神经网络方法,将莽汉山林场作为研究区进行了遥感影像的分类研究。比较了BP神经网络分类与最大似然、简单和复杂非监督分类法之间的类型与数量精度。BP神经网络分类的总类型精度是70.5%,总数量精度为84.65%,KAPPA系数是0.6455。结果说明BP神经网络的分类质量优于其他方法,其总的类型精度与其他三种分类方法相比分别增加了10.5%、32%和33%,总的质量精度增加了5.3%。因此,辅以地理参考数据的BP神经网络分类可以作为一种有效的分类方法。 相似文献
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提出了一种基于自适应谐振理论建立起来的自组织模糊ARTMAP神经网络分类器。分析了ART神经网络的结构和工作原理,给出模糊ARTMAP神经网络分类的具体算法,并将其运用到TM遥感影像分类的实验中。结果表明模糊ARTMAP神经网络分类器的速度快,精度高,比常用的BP网络具有更好的性能。 相似文献
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基于PCA-BPNN的多光谱遥感影像分类 总被引:7,自引:0,他引:7
基于BP算法的神经网络方法目前已广泛运用于遥感影像分类,提出一种主成分分析(PCA)与BP神经网络相结合的遥感影像分类方法——PCA-BPNN,实验证明该方法是可行并且有效的,在减少计算量和加快收敛的同时,提高了分类的精度。 相似文献
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使用机器学习进行遥感影像标注的一个重要前提是有足够的训练样本,而样本的标注是非常耗时的。本文采用了域适应的方法来解决遥感影像场景分类中小样本量的无监督学习问题,提出了结合对抗网络与辅助任务的遥感影像域适应方法。首先建立了基于深度卷积神经网络的遥感影像分类框架;其次,为了学习到域不变特征,在标签分类器的基础上增加域分类器,并使域损失函数在其反射传播时的梯度与标签损失的梯度相反,从而保证域分类器不能区分样本来自于哪个域;最后引入了辅助分类任务,扩充了样本的同时使网络更具泛化能力。试验结果表明,本文方法优于主流的无监督域适应方法,在小样本遥感影像无监督分类中得到了较好的效果。 相似文献
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针对室内环境下的5G定位需求,提出了利用神经网络算法对粗略定位结果进行优化的方法,减小了多径、非视距传播造成的定位误差,改善了结果域的定位精度.优化算法利用测距定位中的到达时间(TOA)定位法和到达时间差(TDOA)定位法获得粗略定位结果,分别结合BP神经网络、Elman神经网络及通过遗传算法(GA)优化后的GA-BP神经网络、GA-Elman神经网络共利用4种神经网络进行训练,得到修正后的精确定位结果,并对4种神经网络算法进行了分析与评估. Elman算法相较于BP算法具有迭代收敛快、迭代次数少、误差改正好的特点,更适合5G定位结果域的优化;融入GA后结果精度均有所提高,其中GA-Elman算法能够训练得到最好的定位结果. 相似文献
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基于BP网络的大坝变形分析与预报 总被引:5,自引:0,他引:5
人工神经网络具有表达非线性映射的性质,将BP神经网络模型用于大坝变形的拟合分析和预报研究,并用实例证明了该方法可以取得很好的拟合和预报结果。 相似文献
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针对传统手工提取特征方法需要专业领域知识,提取高质量特征困难的问题,将深度迁移学习技术引入到高分影像树种分类中,提出一种结合面向对象和深度特征的高分影像树种分类方法。为了获取树种的精确边界,该方法首先利用多尺度分割技术分割整幅遥感影像,并选择训练样本作为深度卷积神经网络的输入。为了避免样本数量少导致过拟合问题,采用迁移学习方法,使用ImageNet上训练的VGG16模型参数初始化深度卷积神经网络,并利用全局平局池化压缩参数,在网络最后添加1024个节点的全连接层和7个节点的Softmax分类器,利用反向传播和Adam优化算法训练网络。最后分类整幅遥感影像,生成树种专题地图。以安徽省滁州市的皇甫山国家森林公园为研究区,QuickBird高分影像作为数据源,采用本文方法进行树种分类。试验结果表明,本文方法树种分类总体精度和Kappa系数分别为78.98%和0.685 0,在保证树种精度的同时实现了端到端的树种分类。 相似文献
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Dawei Li Fengbao Yang Xiaoxia Wang 《Journal of the Indian Society of Remote Sensing》2017,45(2):229-237
High resolution remote sensing image contains abundant information, but remote sensing classification only based on spectral information is affected in the complex spectrum area. Crop area and other land-cover objects contain different texture features. This paper extracts texture information based on gray-level co-occurrence matrix and Gabor filters group, sets up spectrum-texture joint feature set. To enhance classification efficiency, Ensemble learning strategy is introduced to improve classical support vector machine and back propagation neural network classifiers in training process. To prove the effectiveness of proposed methods, several experiment images are utilized to execute experiments. Results indicate that proposed methods improve classification accuracy compared with classical algorithms significantly, and promote running efficiency compared with the situation of large sample, support corn area statistical process and yield estimation. 相似文献
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Iftikhar U. Sikder 《International Journal of Digital Earth》2016,9(12):1206-1223
This paper presents a granular computing approach to spatial classification and prediction of land cover classes using rough set variable precision methods. In particular, it presents an approach to characterizing large spatially clustered data sets to discover knowledge in multi-source supervised classification. The evidential structure of spatial classification is founded on the notions of equivalence relations of rough set theory. It allows expressing spatial concepts in terms of approximation space wherein a decision class can be approximated through the partition of boundary regions. The paper also identifies how approximate reasoning can be introduced by using variable precision rough sets in the context of land cover characterization. The rough set theory is applied to demonstrate an empirical application and the predictive performance is compared with popular baseline machine learning algorithms. A comparison shows that the predictive performance of the rough set rule induction is slightly higher than the decision tree and significantly outperforms the baseline models such as neural network, naïve Bayesian and support vector machine methods. 相似文献
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探讨了将人工神经网络专家系统用于大坝变形预测的方法,给出了系统功能结构框图,并对各模块的功能进行了分析。 相似文献
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Object matching facilitates spatial data integration, updating, evaluation, and management. However, data to be matched often originate from different sources and present problems with regard to positional discrepancies and different levels of detail. To resolve these problems, this article designs an iterative matching framework that effectively combines the advantages of the contextual information and an artificial neural network. The proposed method can correctly aggregate one‐to‐many (1:N) and many‐to‐many (M:N) potential matching pairs using contextual information in the presence of positional discrepancies and a high spatial distribution density. This method iteratively detects new landmark pairs (matched pairs), based on the prior landmark pairs as references, until all landmark pairs are obtained. Our approach has been experimentally validated using two topographic datasets at 1:50 and 1:10k. It outperformed a method based on a back‐propagation neural network. The precision increased by 4.5% and the recall increased by 21.6%, respectively. 相似文献
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基于云理论、粗集和模糊神经网络的区域橡胶种植适宜度评估模型 总被引:1,自引:0,他引:1
针对橡胶种植适宜性评估,基于云理论、粗集理论和模糊神经网络理论,提出了一种适宜度评估模型。该模型将转化的样本数据进行粗集简约,通过模糊神经网络得出评价因子的隶属函数,计算评价等级。研究结果表明,此模型能够科学、快速、准确地分析出橡胶种植最适宜区、适宜区、次适宜区和不适宜区。 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