共查询到19条相似文献，搜索用时 281 毫秒
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基于PCM改进算法的遥感混合像元模拟分析 总被引：7，自引：0，他引：7
混合像元的存在是影响遥感图像分类精度的主要原因,模糊分类是进行混合像元分解的重要方法,其效果的好坏取决于各像元分类后对各类别的隶属度值能否准确地反映像元的类别组成。当非监督分类中的聚类数目与实际类别数目不符,或者监督分类中训练样本存在未训练类别时,常用的模糊c-均值(FCM)方法的效果将大大降低,而可能性c-均值(PCM)方法则可以解决这个问题。该文提出了基于PCM算法的遥感图像混合像元分解方法,并用监督分类方法实例说明PCM方法的优越性。 相似文献
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在遥感影像计算机自动识别与分类中,选取最佳的波段子集对地物进行分类对提高分类精度至关重要。根据统计学原理,遥感影像中属于某类别地物的特征向量服从正态分布,训练样本的正态性检验是关键,基于此理论,本文利用TM影像数据,通过检验所选取的训练区的正态性与否,让计算机自动的选取最优的波段组合,并对分类的精度进行评估。研究表明,计算机自动选取最佳波段组合后对分类精度的预先评估,较常规分类后再进行数据检验精度评估方法方便,快捷,省时,省力。 相似文献
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高维遥感图像的快速分类算法 总被引：1，自引：0，他引：1
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
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以崇明东滩自然保护区盐沼植被为研究对象,利用Landsat TM遥感图像,结合现场调查和前人关于东滩时空动态变化的研究结果,确定崇明岛东滩主要分布的盐沼植被类型,提出了基于知识工程师的植被分类方法。与常规非监督和监督分类相比,该方法的精度较高,总体精度为92.35%,kappa系数为0.9072,而非监督分类和监督分类(最大似然法)的总体精度分别为86.92%和89.10%。实验结果表明,该方法能够有效地对研究区植被进行分类与识别,可为实现盐沼植被的自动提取提供理论依据和有效的方法途径。 相似文献
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结合后验概率对分类的影响和全极化SAR数据特点,提出了一种全极化SAR数据分类方法。首先将全极化SAR数据的协方差矩阵转换为9个服从正态分布的强度量;然后通过迭代分类计算类别出现的概率,对9个强度量进行基于最大后验概率的分类。以黑龙江省逊克县境内的一景ALOS PALSAR全极化数据为例,用该方法进行分类,总体精度和Kappa系数分别达到81.34%和0.84,优于传统的最大似然分类方法。 相似文献
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基于Landsat-8的遥感影像分类研究 总被引：1，自引：0，他引：1
遥感影像分类在专题信息提取、地表动态监测以及专题地图制作等应用中具有重要作用,传统的分类方法可以分为监督分类和非监督分类,因算法成熟、操作简单,这两类方法仍然是当前使用较广泛的分类方法,但从理论、过程以及使用范围条件上二者都不相同,各有其优缺点。鉴于这种现状,本文采用Landsat-8 OLI焦作地区遥感数据分别基于监督与非监督中的各种算法进行土地覆盖分类,并对分类结果进行比较分析和精度评价,以期为实际工作中根据不同需求选取适当分类器提供依据。研究结果表明:监督分类中最大似然法分类精度相对较高,漏分错分最少,总体分类精度达到87.152%;非监督分类中ISODATA算法从聚类效果、漏分错分以及计算时间上综合分析要优于K-均值分类;另外,不同分类算法对不同地物类型的解译效果不同。 相似文献
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Land cover maps obtained from classification of remotely sensed imagery provide valuable information in numerous environmental monitoring and modeling tasks. However, many uncertainties and errors can directly or indirectly affect the quality of derived maps. This work focuses on one key aspect of the supervised classification process of remotely sensed imagery: the quality of the reference dataset used to develop a classifier. More specifically, the representative power of the reference dataset is assessed by contrasting it with the full dataset (e.g. entire image) needing classification. Our method is applicable in several ways: training or testing datasets (extracted from the reference dataset) can be compared with the full dataset. The proposed method moves beyond spatial sampling schemes (e.g. grid, cluster) and operates in the multidimensional feature space (e.g. spectral bands) and uses spatial statistics to compare information density of data to be classified with data used in the reference process. The working hypothesis is that higher information density, not in general but with respect to the entire classified image, expresses higher confidence in obtained results. Presented experiments establish a close link between confidence metrics and classification accuracy for a variety of image classifiers namely maximum likelihood, decision tree, Backpropagation Neural Network and Support Vector Machine. A sensitivity analysis demonstrates that spatially-continuous reference datasets (e.g. a square window) have the potential to provide similar classification confidence as typically-used spatially-random datasets. This is an important finding considering the higher acquisition costs for randomly distributed datasets. Furthermore, the method produces confidence maps that allow spatially-explicit comparison of confidence metrics within a given image for identification of over- and under-represented image portions. The current method is presented for individual image classification but, with sufficient evaluation from the remote sensing community it has the potential to become a standard for reference dataset reporting and thus allowing users to assess representativeness of reference datasets in a consistent manner across different classification tasks. 相似文献
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《International Journal of Digital Earth》2013,6(4):330-346
Abstract In recent years, the rough set (RS) method has been in common use for remote-sensing classification, which provides one of the techniques of information extraction for Digital Earth. The discretization of remotely sensed data is an important data preprocessing approach in classical RS-based remote-sensing classification. Appropriate discretization methods can improve the adaptability of the classification rules and increase the accuracy of the remote-sensing classification. To assess the performance of discretization methods this article adopts three indicators, which are the compression capability indicator (CCI), consistency indicator (CI), and number of the cut points (NCP). An appropriate discretization method for the RS-based classification of a given remotely sensed image can be found by comparing the values of the three indicators and the classification accuracies of the discretized remotely sensed images obtained with the different discretization methods. To investigate the effectiveness of our method, this article applies three discretization methods of the Entropy/MDL, Naive, and SemiNaive to a TM image and three indicators for these discretization methods are then calculated. After comparing the three indicators and the classification accuracies of the discretized remotely sensed images, it has been found that the SemiNaive method significantly reduces large quantities of data and also keeps satisfactory classification accuracy. 相似文献
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GIS辅助下的基于知识的遥感影像分类方法研究——以土地覆盖/土地利用类型为例 总被引：23，自引：1，他引：23
提高计算机遥感影像的分类精度，是遥感应用中研究的主要问题之一。作者以规则的形式表示遥感影像解译知识，使用ＴＭ影像数据和ＤＥＭ、坡度、土地利用图等地理辅助数据，从遥感影像处理／地理数据／专家知识一体化的角度出发，使用基于知识的方法进行了研究，改善了分类精度。实例研究证明了方法的正确性。 相似文献
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面向遥感大范围应用的目标,自动化程度仍是遥感影像分类面临的重要问题,样本的人工选择难以适应当前土地覆盖信息自动化提取的实际应用需求。为了构建一套基于先验知识的遥感影像全自动分类流程,本文将空间信息挖掘技术引入到遥感信息提取过程中,提出了一种面向遥感影像对象级分类的样本自动选择方法。该方法通过变化检测将不变地物标示在新的目标影像上,并将过去解译的地物类别知识迁移至新的影像上,建立新的特征与地物关系,从而完成历史专题数据辅助下目标影像的自动化的对象级分类。实验结果表明,在已有历史专题层的图斑知识指导下,该方法能有效地自动选择适用于新影像分类的可靠样本,获得较好的信息提取效果,提高了对象级分类的效率。 相似文献
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从理论和试验方面对图像的噪声评估方法进行了分析。结合北京1号小卫星特性, 进行了该类方法应用效能的评价, 讨论了分块评估噪声方法的最佳参数设置。选取满足噪声评估环境的图像, 实现了综合不同地表覆盖条件的北京1号小卫星图像噪声的评估。噪声评估结果与在轨测试情况的对比表明, 北京1号小卫星经过近3年的运行, 仍保持了较好的性能。 相似文献
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遥感信息处理不确定性的可视化表达 总被引：2，自引：0，他引：2
如何全面、准确地度量和可视化表达遥感信息处理中不确定性的程度和空间分布方式,是遥感信息不确定性研究的关键问题之一.传统的度量方法(例如误差矩阵)是将以训练样本集为基础的度量作为总分类精度的度量,而我们需要估计模型对于"样本外数据"的性能.本文首先利用信息论和粗糙集理论等度量遥感分类影像属性信息的不确定性,提出基于像元、目标和影像的遥感信息不确定性度量指标;然后分别描述了基于不同度量指标的可视化表达方式,并对我国黄河三角洲地区的Landsat TM影像进行了分类信息不确定性度量和可视化表达实验. 相似文献
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提出了一种基于误差分析的组合分类器,通过结合两种监督分类方法,提出的算法分别估计了两种监督分类方法在计算过程中的误差,给出了规则输出的置信区间,再根据置信区间的大小对两种分类方法的输出结果进行加权平均,从而得到更精确的规则输出.利用该方法对遥感图像进行分类实验,在不同训练样本分布与不同训练样本数量的情况下,比较新的组合分类器与单一分类器的精度.结果表明新的组合分类器能够取得比单一的分类器更高的分类精度.结果还显示出,两个分类器的独立性越强,组合分类器的效果越好.另外一个实验比较了新的组合分类器与和式规则组合分类器的分类精度,结果仍显示出了新方法的优越性. 相似文献
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ABSTRACTTree species distribution mapping using remotely sensed data has long been an important research area. However, previous studies have rarely established a comprehensive and efficient classification procedure to obtain an accurate result. This study proposes a hierarchical classification procedure with optimized node variables and thresholds to classify tree species based on high spatial resolution satellite imagery. A classification tree structure consisting of parent and leaf nodes was designed based on user experience and visual interpretation. Spectral, textural, and topographic variables were extracted based on pre-segmented images. The random forest algorithm was used to select variables by ranking the impact of all variables. An iterating approach was used to optimize variables and thresholds in each loop by comprehensively considering the test accuracy and selected variables. The threshold range for each selected variable was determined by a statistical method considering the mean and standard deviation for two subnode types at each parent node. Classification of tree species was implemented using the optimized variables and thresholds. The results show that (1) the proposed procedure can accurately map the tree species distribution, with an overall accuracy of over 86% for both training and test stages; (2) critical variables for each class can be identified using this proposed procedure, and optimal variables of most tree plantation nodes are spectra related; (3) the overall forest classification accuracy using the proposed method is more accurate than that using the random forest (RF) and classification and regression tree (CART). The proposed approach provides results with 3.21% and 7.56% higher overall land cover classification accuracy and 4.68% and 10.28% higher overall forest classification accuracy than RF and CART, respectively. 相似文献