共查询到19条相似文献，搜索用时 454 毫秒
1.
基于SVM决策支持树的城市植被类型遥感分类研究 总被引：17，自引：0，他引：17
城市植被类型不同,生物量不同,其生态功能与绿化效应也不同。在目前难直接获取城市“绿量”实测数据的情况下,可以绿地面积和植被类型间接反映绿地的生物量和绿化效应。本文利用高分辨率卫星影像IKONOS,以实验区与验证区城市植被类型信息为对象,在对常用的参数和非参数分类方法进行对比实验的基础上,对SVM的核函数进行了分析,构建了基于SVM决策树的城市植被类型分类模型。分类实验结果表明：与其他传统方法分类结果比较,SVM的决策树分类方法对植被类型的分类精度达到83．5％,绿化面积总精度接近95％,取得了良好的效果。 相似文献
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基于多尺度特征融合和支持向量机的高分辨率遥感影像分类 总被引：10，自引：1，他引：10
相对传统的中低分辨率遥感数据而言,高空间分辨率遥感影像同一地物内部丰富的细节得到表征,空间信息更加丰富,地物的尺寸、形状以及相邻地物的关系得到更好的反映,但其光谱统计特性不如中低分辨率影像稳定,类内光谱差异较大,而传统分类方法仅依据像元的光谱值,因此在高分辨率影像分类中,传统方法往往不能获得好的结果。在此背景下,提出了一种多尺度空间特征融合的分类方法,旨在利用不同尺度的空间邻域特征弥补传统方法的不足。其基本过程是：首先针对不同尺度特点,用小波变换压缩空间邻域特征,并结合支持向量机得到不同尺度下的分类结果,然后根据尺度选择因子为每个像元选择最佳的类别。文中QuickBird和IKONOS影像实验证明该算法能有效提高高分辨率影像解译的精度。 相似文献
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本文利用2002年陕西省ETM卫星影像及相关植被地理信息,在其影像上选取覆盖全省包含不同地物类型的85个地面样地(Ground Truth),通过2003年陕西省MODIS卫星影像获取所选样地全年的NDVI时间序列曲线。采用决策树分类方法,结合NDVI时间序列曲线,实现基于时相和波谱信息的植被分类。最后通过混淆矩阵与Kappa系数等方法对分类结果进行正确率评价,结果表明,文中所给方法优于传统分类方法,所得结果与其他调查结果相一致。 相似文献
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大气校正对SPOT卫星遥测水质的影响 总被引：1，自引：0，他引：1
藉由卫星遥测进行河川水质监测，目前尚没有较明确可行之方法，如何利用较为简单且适当的SPOT卫星遥测大气校正方法，正确辨识水体水质，是本研究的主要目的。利用SPOT卫星作两阶段非监督式及监督式自动分类，确认卫星影像中水质测站对应之水体样本，并将所有样本依季节分群，俾让卫星监测水体水质样本较为均质。模拟方式采用多变量回归、类神经网络及判别分析3种模式，并比较4种不同之大气校正程序。结果发现。以水质及其指标整体预测来看，类神经网络预测结果较优于多变量回归及判别分析的结果，大气校正方法以直接采用灰度值并消除最暗像元灰度值之校正方法，即可达到不错之预测结果。综合而言，以SPOT或分辨率更高之卫星光谱遥测水质是简单可行，但仍需更多数据以验证其精确度。 相似文献
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近年来,在遥感图像计算机识别与分类方面已经作了相当大的努力。但是大部分的工作集中在逐点分类方法的研究。卫星遥感图像,由于大气传输及其各种环境因素的影响,许多地物在一定波谱段内具有相近的电磁辐射特性,致使不同类别物体在光谱空间相互交迭,因此,只利用单个像元光谱亮度值的分类方法,可能会造成较大的误分。 相似文献
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基于GF2号卫星影像的农业信息提取方法对比分析 总被引：1，自引：0，他引：1
以GF2卫星0.8 m全色/3.2 m多光谱分辨率遥感影像为基础数据源,对基于GF2号卫星影像的农业信息提取流程和方法进行了研究与对比分析。首先对GF2号卫星影像进行波谱分析;其次对GF2号影像进行融合,并对多种融合方法进行质量评价;最后选择阈值法、波谱间关系法、非监督分类法和面向对象法分别对GF2号影像数据进行农业信息提取试验,并对信息提取结果进行精度验证和结果分析。试验表明,面向农业信息提取的GF2号卫星影像融合方法中,Pansharp融合算法融合影像色彩正常,无虚影,清晰度高,地类对比度正常,纹理清晰,熵值及与原始多光谱影像的相关系数高。阈值法和谱间关系法适用于提取单要素农业信息,非监督分类法能够初步获取研究区土地利用情况,面向对象法提取研究区全要素信息精度高。总体来说,不同信息提取方法具有各自的优势,在具体实际应用中,可以根据目标地类的波谱特性,选择适宜的遥感影像处理和信息提取方法。 相似文献
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《测绘与空间地理信息》2020,(8)
小型高光谱成像仪搭载在无人机平台上,可以获取更高空间分辨率的影像数据,在无人机升限范围内可以获取多种分辨率的高光谱数据,相比于卫星影像,无人机获取的高光谱影像分辨率往往会高出很多。通过已有数据验证,并非分辨率越高就能取得越好的识别分类效果,高分辨率数据更容易造成错分、漏分等。无人机获取的不同高分辨率的数据在进行分类时分类精度存在差异,本文将通过对比两种不同分辨率的高光谱数据分类结果精度,计算出分类精度差异,为高分辨率的高光谱数据获取和处理提供参考。 相似文献
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面向对象的遥感影像模糊分类方法研究 总被引：3，自引：0，他引：3
传统的基于像素的遥感影像处理方法都是基于遥感影像光谱信息极其丰富，地物间光谱差异较为明显的基础上进行的。对于只含有较少波段的高分辨率遥感影像，传统的分类方法，就会造成分类精度降低，空间数据的大量冗余，并且其分类结果常常是椒盐图像，不利于进行空间分析。本文采用面向对象的影像分类方法，考虑了对象的不同特征值，例如光谱值，形状和纹理，结合上下文关系和语义的信息，这种分类技术不仅能够使用影像属性，而且能够利用不同影像对象之间的空间关系。在对诸多对象进行分类后，再进行精度分析。在此研究提出了一种面向对象的方法结合模糊理论把许多的对象块分成不同的类别。这一过程主要有两个步骤：第一个步骤是分割。图像分割将整个图像分割成若干个对象，在这个过程中，分割尺度的选择会影响到后续的分类结果和精度。第二个步骤是分类。在这个步骤中，特征值的选择和隶属度函数的选择都对分类结果有着至关重要的影响。 相似文献
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《国土资源遥感》2017,(3)
结合高光谱影像地物光谱特征与高空间分辨率影像分割获得的目标对象进行地物分类。首先,对Hyperion影像进行坏线和Smile效应去除,经过FLAASH大气校正后,得到研究所用的155个波段;其次,利用地物光谱曲线的特征点确定适合地物识别的光谱分辨率,进行Hyperion影像降维,生成降维后所需的21个宽波段;然后,对IKONOS影像采用小波融合,利用多分辨率分割技术生成高空间分辨率影像目标对象;最后,基于层次分析法对分割后生成的目标对象进行分类,采用模糊隶属函数利用植被红边效应、水体在近红外波段吸收特征进行第1层次分类,再取距离值最大的前10个Hyperion影像波段作为标准最邻近分类的特征波段,完成第2层次分类。分类结果表明,研究区共分出9种地物类型,分类效果明显优于最大似然法分类与光谱角填图法。 相似文献
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Sumith Pathirana 《国际地球制图》2013,28(4):70-81
Abstract The output from any spatial data processing method may contain some uncertainty. With the increasing use of satellite data products as a source of data for Geographical Information Systems (GIS), there have been some major concerns about the accuracy of the satellite‐based information. Due to the nature of spatial data and remotely sensed data acquisition technology, and conventional classification, any single classified image can contain a number of mis‐classified pixels. Conventional accuracy evaluation procedures can report only the number of pixels that are mis‐classified based on some sampling observation. This study investigates the spatial distribution and the amount of these pixels associated with each cover type in a product of satellite data. The study uses Thematic Mapper (TM) and SPOT multispectral data sets obtained for a study area selected in North East New South Wales, Australia. The Fuzzy c‐Means algorithm is used to identify the classified pixels that contained some uncertainty. The approach is based on evaluating the strength of class membership of pixels. This study is important as it can give an indication of the amount of error resulting from the mis‐classification of pixels of specific cover types as well as the spatial distribution of such pixels. The results show that the spatial distribution of erroneously classified pixels are not random and varies depending on the nature of cover types. The proportions of such pixels are higher in spectrally less clearly defined cover types such as grasslands. 相似文献
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利用卫星遥感数据制作复杂地形环境的植被图面临的最主要问题是精度，单纯对遥感数据（ＴＭ或ＳＰＯＩ）进行监督或非监督分类的精度低于５０％。本文选择美国亚利桑那州ＳａｎｔａＣａｔａｌｉｎａ山脉的ＰｕｓｃｈＲｉｄｇｅ作为研究区，分析地理信息系统模型在改善植被分类精度中的作用。结果表明，通过结合辅助数据和应用地理信息系统模型，其精度可以从３７．４１％提高到７１．６７％（ＳＰＯＴ数据，非监督分类），或从５０．０７％提高到６１．５０％（ＴＭ数据，监督分类）。同时表明用ＳＰＯＴ数据进行山区植被制图的效果好于ＴＭ数据。 相似文献
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Olivier R. van Lier Richard A. Fournier Robert L. Bradley Nelson Thiffault 《International Journal of Applied Earth Observation and Geoinformation》2009,11(5):334-343
Invasive ericaceous shrubs (e.g. Kalmia angustifolia, Rhododendron groenlandicum, Vaccinium spp.) may reduce the regeneration and early growth of black spruce (Picea mariana) seedlings, the most economically important boreal tree species in Quebec. Our study focused, therefore, on developing a method for mapping ericaceous shrubs from satellite images. The method integrates very high resolution satellite imagery (IKONOS) to guide classifiers applied to medium resolution satellite imagery (Landsat-TM). An object-oriented image classification approach was applied using Definiens eCognition software. An independent ground survey revealed 80% accuracy at the very high spatial resolution. We found that the partial use (70%) of classified polygons derived from the IKONOS images were an effective way to guide classification algorithms applied to the Landsat-TM imagery. The results of this latter classification (78.4% overall accuracy) were assessed by the remaining portion (30%) of unused very high resolution classified polygons. We further validated our method (65.5% overall accuracy) by assessing the correspondence of an ericaceous cover classification scheme done with a Landsat-TM image and results of our ground survey using an independent set of 275 sample plots. Discrimination of ericaceous shrub cover from other land cover types was achieved with precision at both spatial resolutions with producer accuracies of 87.7% and 79.4% from IKONOS and Landsat, respectively. The method is weaker for areas with sparse cover of ericaceous shrubs or dense tree cover. Our method is adapted, therefore, for mapping the spatial distribution of ericaceous shrubs and is compatible with existing forest stand maps. 相似文献
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高分辨率影像的植被分类方法对比研究 总被引：12，自引：0，他引：12
高分辨率影像的纹理信息可解决用光谱分类面临的“同物异谱”和“同谱异物”问题,更精确地分辨地物的细微变化,但将纹理作为主要信息进行植被分类的研究较少。本文以南京市钟山景区为例,利用IKONOS影像数据的纹理信息进行植被分类,并将结果与用光谱信息、植被指数信息的分类结果比较。共使用了4个灰度共生矩阵纹理量：CON（对比）、COR（相关）、HOM（同质）和MCON（改进的对比）分析各类植被的纹理表征设阈值分割;用3个植被指数：NDVI（归一化指数）、MSAVI（改进的土壤调节指数）和SAVI（土壤调节指数）（L取0.5和5）选择发现SAVI5最能区分。对纹理和指数信息均设各类型的阈值进行分割提取;基于光谱信息分别用最小距离监督分类和ISODATA非监督分类。研究中先进行数据恢复,再分别用三种信息将试验区植被分为6类：草地、竹林、常绿针叶林、常绿阔叶林、混交林和园地,最后将三种方法4个结果进行比较。精度评价的结论是：纹理信息分类的精度最高,植被指数次之,光谱信息中的非监督分类最低,纹理反映地物光谱及差异信息,可作为最佳方法用于植被分类。 相似文献
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Kristin Spröhnle Olaf Kranz Elisabeth Schoepfer Matthias Moeller Stefan Voigt 《国际地球制图》2016,31(5):575-595
This study describes the development of a semi-automatic object-based image analysis approach for the detection and quantification of deforestation in Zalingei, Darfur, in consequence of the increasing concentration of refugees or internally displaced persons (IDPs) in the region. The classification workflow is based on a multi-scale approach, ranging from the analysis of high resolution SPOT-4 to very high resolution IKONOS and QuickBird satellite imagery between 2003 and 2008. The overall accuracy rates for the classification of the SPOT 4 data ranged from 92% up to 95%, while those for the QuickBird and IKONOS classification have shown values of 88 and 87%, respectively. The resulting trends in woody vegetation cover were compared with the development of the local population and the variability of precipitation. The results show that the strong increase in human population in the Zalingei IDP camps can be associated with considerable decrease in woody vegetation in the camp vicinity. 相似文献
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This study assesses the usefulness of Nigeriasat-1 satellite data for urban land cover analysis by comparing it with Landsat and SPOT data. The data-sets for Abuja were classified with pixel- and object-based methods. While the pixel-based method was classified with the spectral properties of the images, the object-based approach included an extra layer of land use cadastre data. The classification accuracy results for OBIA show that Landsat 7 ETM, Nigeriasat-1 SLIM and SPOT 5 HRG had overall accuracies of 92, 89 and 96%, respectively, while the classification accuracy for pixel-based classification were 88% for Landsat 7 ETM, 63% for Nigeriasat-1 SLIM and 89% for SPOT 5 HRG. The results indicate that given the right classification tools, the analysis of Nigeriasat-1 data can be compared with Landsat and SPOT data which are widely used for urban land use and land cover analysis. 相似文献
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本论文尝试讨论两个主题：主题一为利用主成分分析PCA方法应用于像元阶层资料融合技术的研究。主题二为应用Dempster-Shafer evidence theory方法于特征阶层数据融合技术的研究。在第一个主题中，由于合成孔径雷达的数据具有全偏极特性，在此选取了对植被较为敏感的HV极化合成孔径雷达数据，与具有光谱特性的光学SPOT数据做数据融合处理以利接下来的地物分类。首先，本研究利用小波转换技术来滤除合成孔径雷达斑驳噪声，在接下来融合步骤中，主成分分析出来的第一部分（PCI）是用做完滤除噪声后的合成孔径雷达取代，在数据融合后，进行地物分类是采用最大似然法来分类融合影像。在第二个主题中，利用全偏极雷达数据的极化特性结合SPOT数据的光谱特性，其主要目的是为了增加分类的精确度。首先使用李式滤波器滤除全偏极雷达数据噪声，接下来同样是使用采用最大似然法来分类融合影像，（不同的在于全偏极雷达影像使用Wishart几率分布，在光学影像采用multivariate Gaussian几率分布）将每个类别中每个像元属于某个类别的几率值计算出来，再利用Dempster-Shafer evidence theory来结合这些类别的机率值。最后产生出一张新的分类影像。实验的结果显示分类的精确度比较于未融合的资料都有明显提升的效果，也证明了此两个数据融合方法对于不同数据特性的融合都是很成功的。 相似文献
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Plague is a zoonotic infectious disease present in great gerbil populations in Kazakhstan. Infectious disease dynamics are influenced by the spatial distribution of the carriers (hosts) of the disease. The great gerbil, the main host in our study area, lives in burrows, which can be recognized on high resolution satellite imagery. In this study, using earth observation data at various spatial scales, we map the spatial distribution of burrows in a semi-desert landscape.The study area consists of various landscape types. To evaluate whether identification of burrows by classification is possible in these landscape types, the study area was subdivided into eight landscape units, on the basis of Landsat 7 ETM+ derived Tasselled Cap Greenness and Brightness, and SRTM derived standard deviation in elevation.In the field, 904 burrows were mapped. Using two segmented 2.5 m resolution SPOT-5 XS satellite scenes, reference object sets were created. Random Forests were built for both SPOT scenes and used to classify the images. Additionally, a stratified classification was carried out, by building separate Random Forests per landscape unit.Burrows were successfully classified in all landscape units. In the ‘steppe on floodplain’ areas, classification worked best: producer's and user's accuracy in those areas reached 88% and 100%, respectively. In the ‘floodplain’ areas with a more heterogeneous vegetation cover, classification worked least well; there, accuracies were 86 and 58% respectively. Stratified classification improved the results in all landscape units where comparison was possible (four), increasing kappa coefficients by 13, 10, 9 and 1%, respectively.In this study, an innovative stratification method using high- and medium resolution imagery was applied in order to map host distribution on a large spatial scale. The burrow maps we developed will help to detect changes in the distribution of great gerbil populations and, moreover, serve as a unique empirical data set which can be used as input for epidemiological plague models. This is an important step in understanding the dynamics of plague. 相似文献