基于 DTW 距离的时序 NDVI 数据植被信息提取--以秦巴山区为例     

韩晓勇  韩玲  陈鲁皖 《测绘工程》2016,25(3):11-16

秦巴山区是我国重要的生态屏障,对该区的植被信息提取开展研究,可为区内生态服务功能及自然资源开发利用提供基础数据。通过加窗处理改进DTW距离相似性算法,结合临近度模糊分类方法对2005—2014年的MODIS NDVI时序数据进行植被信息提取。首先利用S-G滤波对MODIS NDVI时序数据进行重建;再利用2013年的采样数据构建各类植被的标准NDVI时序曲线,逐像元计算与标准NDVI时序曲线的加窗DTW距离,利用临近度模糊分类实现植被信息提取;最后验证提取精度。结果表明,算法具有较高的运行效率,可避免错误匹配,以较高的精度(总体精度83.8%,kappa系数0.77)实现长时间序列的植被信息提取。  相似文献   

影像与激光雷达数据的城市地物信息提取     

易俐娜  李鸾  赵学胜  李艳华 《测绘科学》2016,41(2):92-96

针对城市地物信息提取中地物边界难以确定、分类精度不高的问题,该文提出一套综合利用影像及激光雷达点云高程信息的面向对象分类方法。在分割中,各类地物的最佳分割尺度由监督法分割精度评价确定,最终分割结果利用粒度理论下的分割尺度综合方法进行合成,能兼顾不同地物最优分割尺度,获得准确地物边界;在分类中,采用ReliefF特征选择算法度量从影像及点云数据提取的对象特征重要度,选择最佳特征组合,并采用多分类器组合方法进行分类,以消除Hughes现象,提高分类精度。选择德国斯图加特市两块实验区进行分类实验,结果表明:该方法有利于提高大范围城市地物精细信息提取的精度和效率,具有较高的应用价值。  相似文献   

混合核特征加权SVM遥感湿地空间分类     

刘冰  吴超  林怡 《测绘工程》2016,25(7):13-17

针对湿地空间信息的复杂性和SVM的分类性能,设计一种基于混合核函数的特征加权SVM分类模型,综合利用多种特征信息,避免被弱相关特征所支配,从而提供更佳的映射性能和泛化能力。实验结果表明,该分类模型兼具良好的外推和内推能力,能够有效地融合不同信息源特征,得到更完整和准确的分类结果,在总体精度、Kappa系数等多项指标上都表现出更高的水平。  相似文献   

结合多尺度纹理特征的遥感影像面向对象分类     

林雪  彭道黎  黄国胜  王雪军 《测绘工程》2016,25(7):22-27

地物具有多尺度的特点,单一尺度难以准确描述遥感影像包含的地物纹理信息。利用我国自行研发的高分一号遥感影像数据,采用灰度共生矩阵对第一主成分进行纹理特征提取,利用Jeffries-Matusit距离选择多尺度组合,并通过单一纹理结合多光谱数据的分类精度,以及纹理特征间的相关性,最终选择多尺度纹理特征组合进行面向对象分类。研究结果表明:结合多尺度纹理特征组合的面向对象GF-1影像分类能有效提取地物信息,总体分类精度达到81.75%,Kappa系数0.78。  相似文献   

高维遥感图像的快速分类算法   总被引：1，自引：0，他引：1  

孙华生  李晓轩 《测绘科学》2016,41(8):19-23,37

为了实现对高维遥感图像的快速准确分类,提出了一种基于k均值二叉树支持向量机(SVM)的分类方法。该方法通过对选取的训练样本进行k均值聚类,生成支持向量机分类二叉树,作为确定最佳分类顺序的依据,以降低分类过程中的误差累积并提高整体分类精度,而且可缓解由样本数量不均衡导致的分类误差。该方法可在不进行降维处理的情况下,对高维遥感图像进行快速准确分类。测试结果表明,其分类速度和分类精度都优于传统的支持向量机分类结果。  相似文献   

The use of WorldView-2 satellite imagery to model urban drainage system with low impact development (LID) Techniques     

Moh Moh Lin Khin  Ahmed Shaker  Darko Joksimovic 《国际地球制图》2016,31(1):92-108

Within a wide range of best management practices for stormwater management in urban areas, there has been an increasing interest in source control measures. Source controls such as low-impact development (LID) techniques are potentially attractive as retrofit options for older developed areas that lack available land to implement conventional measures such as stormwater management ponds. Hence, distributed urban drainage models requiring detailed representation of developed drainage areas should be developed to accurately estimate the benefits that LIDs may provide. This study (1) presents a two-stage classification process on a high-resolution WorldView-2 image, and (2) demonstrates how to use the extracted land cover information in the subsequent hydrologic modelling and assessment of different LIDs’ performance. The proposed two-stage classification method achieved an overall accuracy of 80.6%, whereas a traditional pixel-based achieved 68.4% in classifying the same urban area into six land cover classes. From the classification results, the hydrologic properties of micro-subcatchments were imported in the United States Environmental Protection Agency Storm Water Management Model to assess the performance of LIDs. A reduction of run-off volume 18.2% and 37.1% was found with the implementation of porous pavement and bioretention, respectively, in a typical low-rise residential area located in the city of San Clemente, California, US. The study demonstrates the use of high-resolution remote sensing image to aid in evaluating LID retrofit options, and thus benefits in situations where detailed drainage area information is not available.  相似文献   

中国版大洋洲地表覆盖产品研制     

桑会勇  翟亮  张晓贺  安芳 《测绘科学》2016,41(11):151-155

针对全球变化研究对大洋洲地表覆盖产品的需求,该文以2000年和2010年的Landsat卫星影像为数据源,提出了对大洋洲影像按照月份分组并进行样本采集与规则训练的方法,采用GLC树分类器进行自动分类,经过分类后处理和数据集成,完成了2000年和2010年两期、30m分辨率的大洋洲地表覆盖产品研制工作。利用高分辨率影像、实地采集照片等进行室内精度评定,该大洋洲地表覆盖产品的精度达到90%以上。  相似文献   

A support vector machine classifier based on a new kernel function model for hyperspectral data     

Zhilei Lin  Luming Yan 《地理信息系统科学与遥感》2016,53(1):85-101

The kernel function is a key factor to determine the performance of a support vector machine (SVM) classifier. Choosing and constructing appropriate kernel function models has been a hot topic in SVM studies. But so far, its implementation can only rely on the experience and the specific sample characteristics without a unified pattern. Thus, this article explored the related theories and research findings of kernel functions, analyzed the classification characteristics of EO-1 Hyperion hyperspectral imagery, and combined a polynomial kernel function with a radial basis kernel function to form a new kernel function model (PRBF). Then, a hyperspectral remote sensing imagery classifier was constructed based on the PRBF model, and a genetic algorithm (GA) was used to optimize the SVM parameters. On the basis of theoretical analysis, this article completed object classification experiments on the Hyperion hyperspectral imagery of experimental areas and verified the high classification accuracy of the model. The experimental results show that the effect of hyperspectral image classification based on this PRBF model is apparently better than the model established by a single global or local kernel function and thus can greatly improve the accuracy of object identification and classification. The highest overall classification accuracy and kappa coefficient reached 93.246% and 0.907, respectively, in all experiments.  相似文献   

Grid pattern recognition in road networks using the C4.5 algorithm   总被引：1，自引：0，他引：1  

Jing Tian  Fei Gao  Feng Zhao 《制图学和地理信息科学》2016,43(3):266-282

Pattern recognition in road networks can be used for different applications, including spatiotemporal data mining, automated map generalization, data matching of different levels of detail, and other important research topics. Grid patterns are a common pattern type. This paper proposes and implements a method for grid pattern recognition based on the idea of mesh classification through a supervised learning process. To train the classifier, training datasets are selected from worldwide city samples with different cultural, historical, and geographical environments. Meshes are subsequently labeled as composing or noncomposing grids by participants in an experiment, and the mesh measures are defined while accounting for the mesh’s individual characteristics and spatial context. The classifier is generated using the C4.5 algorithm. The accuracy of the classifier is evaluated using Kappa statistics and the overall rate of correctness. The average Kappa value is approximately 0.74, which corresponds to a total accuracy of 87.5%. Additionally, the rationality of the classifier is evaluated in an interpretation step. Two other existing grid pattern recognition methods were also tested on the datasets, and comparison results indicate that our approach is effective in identifying grid patterns in road networks.  相似文献   

Curve matching approaches to waveform classification: a case study using ICESat data     

Yuhong Zhou  Feng Ni  Yifei Lou  Caiyun Zhang  Mohammed Alfarhan 《地理信息系统科学与遥感》2016,53(6):739-758

Light Detection and Ranging (LiDAR) waveforms are being increasingly used in many forest and urban applications, especially for ground feature classification. However, most studies relied on either discretizing waveforms to multiple returns or extracting shape metrics from waveforms. The direct use of the full waveform, which contains the most comprehensive and accurate information has been scarcely explored. We proposed to utilize the complete waveform to test its ability to differentiate between objects having distinct vertical structures using curve matching approaches. Two groups of curve matching approaches were developed by extending methods originally designed for pixel-based hyperspectral image classification and object-based high spatial image classification. The first group is based on measuring the curve similarity between an unknown waveform and a reference waveform, including curve root sum squared differential area (CRSSDA), curve angle mapper (CAM), and Kullback–Leibler (KL) divergence. The second group assesses the curve similarity between an unknown and reference cumulative distribution functions (CDFs) of their waveforms, including cumulative curve root sum squared differential area (CCRSSDA), cumulative curve angle mapper (CCAM), and Kolmogorov–Smirnov (KS) distance. When employed to classify open space, trees, and buildings using ICESat waveform data, KL provided the highest average classification accuracy (87%), closely followed by CCRSSDA and CCAM, and they all significantly outperformed KS, CRSSDA, and CAM based on 15 randomized sample sets.  相似文献