共查询到19条相似文献,搜索用时 46 毫秒
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作为横跨3个国家(尼泊尔、印度、中国)的国际跨界河流———柯西河流域,地形高差巨大,土地覆被结构组成复杂,进行土地覆被的自动分类研究具有典型意义。基于面向对象方法多源遥感数据、训练规则、丰富的细节信息为复杂土地覆被自动分类研究提供了可能。选择合适的影像分割特征和最优分割尺度,按照数据挖掘中的规则顺序逐步进行各个土地覆被的提取。总体精度说明分类结果与野外点相一致的概率能达到90.05%,说明国际跨界河流土地覆被分类方法是可行的,分类结果是准确、可信的。 相似文献
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高光谱遥感以其携带的数据量显著增加为代价换取了纳米级的光谱分辨率,使得基于特征光谱信息的目标地物识别成为可能。但如何从大量带有冗余的数据中提取有用信息,是高光谱研究的一个极富挑战性的课题和其实用化的基础之所在。本文以江苏宜兴地区OMIS I数据为例在全面计算影像的统计特征(相关系数、均方差、最佳波段指数、信噪比等)的基础上,结合目视效果对波段集合进行初步缩减和分组;结合地面实测地物光谱详细分析不同地物光谱特性,进行面向土地覆盖易混类别的波段选择;最后总结了OMIS I数据特征选择与提取流程,相关实验证明应用该流程进行特征选择与提取,其后续分类精度较高。 相似文献
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利用大丰市沿海滩涂湿地区域的高光谱影像和同时期的机载LIDAR数据,结合影像的光谱信息,采用随机森林算法(RF)对研究区进行湿地植被精细分类,并分析和评价分类模型参数设置对总体精度的影响,最后与SVM分类结果进行对比。结果表明:随机森林分类方法的总体精度为90.3%、卡帕(Kappa)系数为0.874;与传统的SVM分类方法相比,RF法均提高了4种湿地植被的生产者精度和使用者精度。通过分析RF分类模型参数设置对总体精度的影响,得出当生长树个数为30、生长树深度为30时,分类精度最高。 相似文献
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多源遥感数据融合技术研究 总被引:1,自引:0,他引:1
首先应用遥感图像处理软件对台湾的福卫全色图像进行增强处理,然后对SPOT5多光谱图像进行自然色彩变换,使其具有较好的色彩表现力,最后两种处理数据经过配准、融合. 相似文献
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本文以ArcGlobe为平台,表现江西省的多源多分辨率遥感数据对江西省现有遥感数据的整理分析,提取出所需区域的最新遥感影像资料,补充制作缺失的遥感数据,将各传感器源的影像模拟为近自然真彩色影像,解决影像间的几何差异和色彩差异问题,对影像数据进行整合,建设2.5m-15m分辨率的省级影像库和0.2m-1m分辨率的设区市影像库,同时对影像数据的查询方法和管理方法进行探讨和研究。 相似文献
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传统的离散型贝叶斯网络分类器是将所有变量视为离散变量,或对连续变量做离散化处理.可是离散化不可避免地存在信息损失,且在多源遥感数据的处理和分析中,连续变量的离散化会导致搜索空间的急剧增加和计算及存储量的极大开销.针对这些问题,开发了一种面向土地利用分类的多源遥感数据混合贝叶斯网络分类器,该分类器首先对问题领域的所有变量... 相似文献
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基于知识的分层综合分类法在土地利用/土地覆盖遥感信息提取中的应用 总被引:1,自引:0,他引:1
土地利用/土地覆盖数据的获取是研究LUCC的重要基础工作。随着遥感技术的飞速发展,通过遥感提取土地利用/土地覆盖专题信息已成为LUCC研究必不可少的一步。目前遥感专题信息提取水平相对滞后于遥感数据获取,为了提高遥感数据在土地利用/土地覆盖的应用,寻找一种较好的、具有相对适用性的方法是目前遥感应用的一个迫切要求。本文比较了目前比较常用的几种土地利用/土地覆盖遥感信息提取方法,分别以西部干旱区(柴达木盆地)和东部地区(鄱阳湖地区)为例,提出在GIS支持下基于知识的分层综合分类方法,并通过和其他几种常用方法进行比较分析,得到如下结果:在自然环境相差较大的柴达木盆地和鄱阳湖地区,采用了GIS支持下基于知识的分层综合分类方法的提取精度均要比单独采用最大似然法、纹理分析法、神经网络分类法等方法的总体精度高出25%,Kappa系数高出0.2。由此可以说明了该方法对于土地利用/土地覆盖专题信息的提取是可行的,同时它也具有一定的适用性。 相似文献
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随着遥感技术的发展,同一区域的多源遥感影像数据越来越丰富。以哈大齐为例,利用ETM+和SPOT-5数据探讨不同遥感信息融合在土地利用过程中的处理方法,比较不同融合算法在土地分类中的差异,并进行定性和定量比较。为有关部门进行土地规划、管理提供科学依据有着十分重要的意义。 相似文献
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Automatic land cover update was an effective means to obtain objective and timely land cover maps without human disturbance. This study investigated the efficacy of multi-temporal remote sensing data and advanced non-parametric classifier on improving the classification accuracy of the automatic land cover update approach integrating iterative training sample selection and Markov Random Fields model when the historical remote sensing data were unavailable. The results indicated that two-temporal remote sensing data acquired in one crop growth season could significantly improve the classification accuracy of the automatic land cover update approach by approximately 3–4%. However, the support vector machine (SVM) classifier was not suitable to be integrated in the automatic land cover update approach, because the huge initially selected training samples made the training of the SVM classifier unrealizable. 相似文献
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Land use and land cover classification is an important application of remote-sensing images. The performances of most classification models are largely limited by the incompleteness of the calibration set and the complexity of spectral features. It is difficult for models to realize continuous learning when the study area is transferred or enlarged. This paper proposed an adaptive unimodal subclass decomposition (AUSD) learning system, which comprises two-level iterative learning controls: The inner loop separates each class into several unimodal Gaussian subclasses; the outer loop utilizes transfer learning to extend the model to adapt to supplementary calibration set collected from enlarged study areas. The proposed model can be efficiently adjusted according to the variability of spectral signatures caused by the increasingly high-resolution imagery. The classification result can be obtained using the Gaussian mixture model by Bayesian decision theory. This AUSD learning system was validated using simulated data with the Gaussian distribution and multi-area SPOT-5 high-resolution images with 2.5-m resolution. The experimental results on numerical data demonstrated the ability of continuous learning. The proposed method achieved an overall accuracy of over 90% in all the experiments, validating the effectiveness as well as its superiority over several widely used classification methods. 相似文献
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D.G. StavrakoudisJ.B. Theocharis G.C. Zalidis 《ISPRS Journal of Photogrammetry and Remote Sensing》2011,66(4):529-544
A Boosted Genetic Fuzzy Classifier (BGFC) is proposed in this paper, for land cover classification from multispectral images. The model comprises a set of fuzzy classification rules, which resemble the reasoning employed by humans. Fuzzy rules are generated in an iterative fashion, incrementally covering subspaces of the feature space, as directed by a boosting algorithm. Each rule is able to select the required features, further improving the interpretability of the obtained model. After the rule generation stage, a genetic tuning stage is employed, aiming at improving the cooperation among the fuzzy rules, thus increasing the classification performance attained after the first stage. The BGFC is tested using an IKONOS multispectral VHR image, in a lake-wetland ecosystem of international importance. For effective classification, we consider advanced feature sets, containing spectral and textural feature types. Comparative results with well-known classifiers, commonly employed in remote sensing tasks, indicate that the proposed system is able to handle multi-dimensional feature spaces more efficiently, effectively exploiting information from different feature sources. 相似文献
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针对在多时相变化检测中,面向对象方法无法较好地检测影像中的细微变化,受分割效果以及面向像素方法的影响出现较高虚警率等问题,本文提出了一种结合基于像素的多特征变化向量分析法(CVA)与基于对象的多层次分割的联合判别方法。首先提取不同时相的光谱与纹理特征,利用最大相关最小冗余(mRMR)算法进行特征选择并通过CVA得到像素级变化检测结果;然后对两幅影像进行叠合分割,利用区域合并策略进行不同尺度检测并获取各尺度检测结果;最后结合多种检测结果进行融合,获得最终变化检测结果。检测结果表明本文所提方法能有效降低漏检率,同时提高了检测的准确性。 相似文献
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利用模式识别中特征提取和特征选择的相关理论对遥感影像的纹理特征进行遴选和变换处理,得到描述纹理的二次特征。实验证明这些新特征能够提高影像分类精度和分类效率。 相似文献
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Integrating multiple images with artificial neural networks (ANN) improves classification accuracy. ANN performance is sensitive to training datasets. Complexity and errors compound when merging multiple data, pointing to needs for new techniques. Kohonen's self-organizing mapping (KSOM) neural network was adapted as an automated data selector (ADS) to replace manual training data processes. The multilayer perceptron (MLP) network was then trained using automatically extracted datasets and used for classification. Two hypotheses were tested: ADS adapted from the KSOM network provides adequate and reliable training datasets, improving MLP classification performance; and fusion of Landsat thematic mapper (TM) and SPOT images using the modified ANN approach increases accuracy. ADS adapted from the KSOM network improved training data quality and increased classification accuracy and efficiency. Fusion of compatible multiple data can improve performance if appropriate training datasets are collected. This proved to be a viable classification scheme particularly where acquiring sufficient and reliable training datasets is difficult. 相似文献