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遥感影像广泛应用于大气校正、土地覆盖分类和目标识别,但云的存在妨碍影像解译工作和后续利用。本文提出基于支持向量机(SVM)的多特征融合方法进行云识别,该方法基于不同地物之间的光谱、纹理和其他特征之间的差异,以资源三号卫星影像为实验数据进行实验。结果显示:该方法整体准确率大于90%,误检率小于5%,检测精度高、稳定性好且有较高的扩展性。 相似文献
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针对三维流场数据可视化容易出现的显示混乱问题,提出一种三维流场可视化自适应方法。该方法引入模糊支持向量机对流场特征进行分类预处理,并提出一种改进的线积分卷积算法,该算法利用模糊支持向量机得到的隶属度自适应生成Sobol稀疏噪声,避免当噪声过于稠密时产生重叠现象,而当噪声过于稀疏时漏掉流场重要的细节信息。通过多组流场三维可视化仿真实验的比较与分析,验证了本文方法的有效性。 相似文献
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针对目前单时相遥感夜间陆地辐射雾检测不能有效分离雾和地表、低云的问题,利用日本第2代多功能卫星数据的高时间分辨率特性,提出了基于时序特征和支持向量机的夜间陆地辐射雾检测模型。该模型首先在单时相夜间陆地辐射雾检测基础上,使用第1和第4波段亮温差时序曲线构造的亮温差累积特征将夜间地表与陆地辐射雾和低云分离,然后利用第1波段亮温时序曲线构造的亮温变化累积特征、斜率匹配特征和频域奇异性特征,结合支持向量机进行夜间雾和低云的分类,从而实现基于时序数据的夜间陆地辐射雾检测。对两期遥感时序数据进行实验发现,与单时相夜间陆地辐射雾检测相比,利用时序数据的方法较好地提高了夜间陆地辐射雾的检测精度。 相似文献
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自适应距离和模糊拓扑优化的模糊聚类SAR影像变化检测 总被引:1,自引:1,他引:0
针对模糊聚类算法的不足,结合差分影像的特点,提出一种基于自适应距离(adaptive distance)和模糊拓扑(fuzzy topology)理论的SAR影像变化检测技术框架(FATCD)。FATCD首先基于自适应距离公式提出一种自适应的样本到聚类中心的距离计算方法,优化了聚类过程中像元隶属度的计算公式,提高了模糊隶属度函数的准确程度;而后利用模糊拓扑理论改进传统去模糊化方式最大隶属度原则,从而增强了去模糊化过程。借助这两点,FATCD提高了模糊聚类变化检测的性能。两组真实SAR影像数据的试验结果表明本文方法可行、有效。 相似文献
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地理空间中的地理要素往往具有模糊性,这种模糊源于人对现实世界的概念化过程,因此具有主观特性.基于模糊集理论,尽管有很多途径对模糊地理要素对应的隶属度函数进行探讨,但是基于认知实验的方法最直接的反映了人们对相应要素的概念化过程中的模糊性.以中关村地区为例,设计了基于地标的问卷调查,并计算每个地标属于"中关村地区"这一概念的隶属度,进而采用支持向量回归方法,得到该要素的隶属度函数.该方法具有实验实施简单,结果便于管理的特点.最后,我们分析了中关村的隶属度函数的一些空间分布特征及其原因. 相似文献
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Clouds contribute significantly to the formation of many of the natural hazards. Hence cloud mapping and its classification
becomes a major component of the various physical models which are used for forecasting natural hazards. The problem of cloud
data classification from NOAA AVHRR (advance very high resolution radiometer) satellite imagery using image transformation
techniques is considered in this paper. The singular value decomposition (SVD) scheme is used to extract the salient spectral
and textural features attributed to satellite snow and cloud data in visible and IR channels. The goals of this paper are
to discriminate between clear sky and clouds in an 8 × 8 pixel array of 1.1 km AVHRR data. If clouds are present then classify
them as low, medium or high range. This scheme can effectively segregate clouds and non-cloud features in the visible and
IR bands of the imagery. It can also classify clouds as low, medium or high range with a success rate of 70–90%. Computer-based
snow and cloud discrimination and automatic cloud classification system will help the forecaster in various climatological
applications, viz., energy balance estimation, precipitation forecasting, landslide forecasting, weather forecasting and avalanche
forecasting etc. 相似文献
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利用高光谱遥感影像的空间纹理特征,可以提高高光谱遥感影像的分类精度。提出了一种多层级二值模式的高光谱影像空-谱联合分类方法。该方法将高光谱影像转化为局部二值模式特征图像获取像元微观特征,基于特征图像生成多层级特征向量获取像元宏观特征。为验证该方法的有效性,选取PaviaU、Salinas和Chikusei高光谱影像数据,利用核极限学习机分类器,分别针对光谱、局部二值模式、多层级二值模式等特征开展实验。结果表明,多层级二值模式空-谱分类总体精度分别达到97.31%、98.96%和97.85%,明显优于传统光谱、3Gabor空-谱等分类方法。该方法可为高光谱影像分类提供更加有效的类别判定特征,有助于提高影像分类精度并获取更加平滑的分类结果图。 相似文献
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Macroalgae plays an important role in coastal ecosystems. The accurate delineation of macroalgae areas is important for environmental management. This study compared the pixel- and object-based methods using Gaofen satellite no. 2 image to explore an efficient classification approach. Expert system rules and nearest neighbour classifier were adopted for object-based classification, whereas maximum likelihood classifier was implemented in the pixel-based approach. Normalized difference vegetation index, normalized difference water index, mean value of the blue band and geometric characteristics were selected as features to distinguish macroalgae farms by considering the spectral and spatial characteristics. Results show that the object-based method achieved a higher overall accuracy and kappa coefficient than the pixel-based method. Moreover, the object-based approach displayed superiority in identifying Porphyra class. These findings suggest that the object-based method can delineate macroalgae farming areas efficiently and be applied in the future to monitor the macroalgae farms with high spatial resolution imagery. 相似文献
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时间序列遥感影像常用于地表覆盖监测及其变化监测。然而,利用时序遥感数据—尤其是中分辨率遥感数据监测地表覆盖变化,其方法基本是先对多期影像分别进行监督分类然后对比分类结果。由于这种方法需要对每期遥感影像单独选择分类训练样本,而对于历史影像,常常难以获得可靠的样本数据。本文基于遥感数据定量化处理,尝试利用光谱特征扩展方法对时间序列Landsat数据进行分类:首先,结合一种新的大气校正方法和相对辐射归一化方法,对时间序列Landsat数据进行定量化处理,以消除各期影像之间的辐射差异,获得地表反射率数据。然后,论文选择一期易于获得分类训练样本的反射率数据作为"参考影像",并结合样本数据提取不同地表覆盖类型的光谱特征。最后,将"参考影像"中提取的地物光谱特征,扩展到所有时间序列反射率数据进行分类。论文利用青藏高原玛多地区的5景Landsat数据对本文的方法进行了验证,结果显示:基于光谱特征扩展的分类方法,可有效对定量化处理后的Landsat数据进行分类,分类总体精度为88.35%—94.25%,分类结果和传统的单景监督分类结果具有较好的一致性。此外,研究也发现,"参考影像"和待分类图像获取时间的季相差异会影响其分类的精度。 相似文献
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针对高光谱影像非线性分类问题,根据高光谱影像光谱分辨率高且光谱具有非线性的特点,结合深度学习理论,提出了一种采用降噪自动编码器(DAE)的高光谱影像分类方法。该方法结合降噪自动编码器与SOFTMAX分类器,构造深层网络分类模型;然后,利用加噪后的光谱数据,采用Dropout方法对分类模型进行预训练和微调;最后,利用训练得到的网络模型学习高光谱影像光谱的隐含特征,实现高光谱影像的分类。采用该方法对AVIRIS和PHI的高光谱影像分别进行分类对比实验,结果表明该方法能有效提高高光谱影像分类精度。 相似文献
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针对云检测在高亮度地表以及雪覆盖区域存在过度检测的问题,设计了一种不依赖热红外波段的增强型多时相云检测EMTCD(Enhanced Multiple Temporal Cloud Detection)算法。首先,利用云的光谱特征建立单时相云检测规则,并基于云、雪的光谱差异构建了增强型云指数ECI(Enhanced Cloud Index),改进了云、雪的区分能力;其次,以同一区域无云影像为参考,基于ECI指数构建了多时相云检测算法,较好地克服了单时相云检测中高亮度地表、雪和云容易混淆的问题,提高了云检测的精度;最后,选择两个典型区域的Landsat-8 OLI影像,对比分析了不同算法的云检测结果。实验结果表明:ECI指数能够有效区分云、雪,EMTCD方法的平均检测精度达到93.2%,高于Fmask(Function of mask)(81.85%)、MTCD(Multi-Temporal Cloud Detection)(66.14%)和Landsat-8地表反射率产品LaSRC(Landsat-8 Surface Reflectance Code)的云检测结果(86.3%)。因此,本文提出的EMTCD云检测算法能够有效地减少高亮度地表和雪的干扰,实现不依赖热红外波段的高精度云检测。 相似文献
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利用风云三号A星MERSI数据,基于ENVI ZOOM软件平台采用面向对象的多尺度影像分割技术,并结合监督分类技术提取云层边缘线,继而对影像进行反演处理时剔除云层覆盖区域的干扰。研究表明,与同时段彩色卫星云图进行视觉对比,分类结果较理想,该分类方法易于操作,可有效提高解译精度。 相似文献
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In this study, we test the potential of two different classification algorithms, namely the spectral angle mapper (SAM) and object-based classifier for mapping the land use/cover characteristics using a Hyperion imagery. We chose a study region that represents a typical Mediterranean setting in terms of landscape structure, composition and heterogeneous land cover classes. Accuracy assessment of the land cover classes was performed based on the error matrix statistics. Validation points were derived from visual interpretation of multispectral high resolution QuickBird-2 satellite imagery. Results from both the classifiers yielded more than 70% classification accuracy. However, the object-based classification clearly outperformed the SAM by 7.91% overall accuracy (OA) and a relatively high kappa coefficient. Similar results were observed in the classification of the individual classes. Our results highlight the potential of hyperspectral remote sensing data as well as object-based classification approach for mapping heterogeneous land use/cover in a typical Mediterranean setting. 相似文献
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Timothy G. Whiteside Guy S. Boggs Stefan W. Maier 《International Journal of Applied Earth Observation and Geoinformation》2011
The development of robust object-based classification methods suitable for medium to high resolution satellite imagery provides a valid alternative to ‘traditional’ pixel-based methods. This paper compares the results of an object-based classification to a supervised per-pixel classification for mapping land cover in the tropical north of the Northern Territory of Australia. The object-based approach involved segmentation of image data into objects at multiple scale levels. Objects were assigned classes using training objects and the Nearest Neighbour supervised and fuzzy classification algorithm. The supervised pixel-based classification involved the selection of training areas and a classification using the maximum likelihood classifier algorithm. Site-specific accuracy assessment using confusion matrices of both classifications were undertaken based on 256 reference sites. A comparison of the results shows a statistically significant higher overall accuracy of the object-based classification over the pixel-based classification. The incorporation of a digital elevation model (DEM) layer and associated class rules into the object-based classification produced slightly higher accuracies overall and for certain classes; however this was not statistically significant over the object-based using spectral information solely. The results indicate object-based analysis has good potential for extracting land cover information from satellite imagery captured over spatially heterogeneous land covers of tropical Australia. 相似文献