Feature selection for airborne LiDAR data filtering: a mutual information method with Parzon window optimization     

Zhan Cai  Liang Zhang 《地理信息系统科学与遥感》2020,57(3):323-337

ABSTRACT

Filtering is one of the key steps for Digital Elevation Model (DEM) generation from airborne Light Detection and Ranging (LiDAR) data. Machine-learning-based filters have emerged as a class of filtering algorithms in recent years. Most existing studies mainly focus on feature generation due to limited available features a point cloud possesses. More than 30 features have been described in the existing literature. But most generated features are based on geometric information of points. Several redundant and irrelevant features may not necessarily improve the filtering accuracy. Hence, this paper proposes a feature-selection method using minimal-Redundancy-Maximal-Relevance (mRMR) combined with Parzen window optimization to deal with both discrete and continuous features. An optimal/suboptimal feature subset is constructed for machine-learning filters in various landscapes. Experimental results based on AdaBoost show that height-related features, particularly height itself, are of the greatest significance in both urban and rural scenes. Moreover, different subsets can be selected from the datasets of the two landscapes by our feature-selection strategy, which increases the data relevance for describing each geographical landscape. This study provides guidelines for the selection of optimal/suboptimal features for point cloud filtering based on machine-learning algorithms.  相似文献   

基于mRMR特征优选算法的多光谱遥感影像分类效率精度分析     

程希萌  沈占锋  邢廷炎  夏列钢  吴田军 《地球信息科学学报》2016,18(6):815-823

在遥感图像分类过程中,进行合理的特征优选操作,将有助于提高分类器的分类效率及精度。本文以淮南地区资源三号卫星多光谱遥感影像数据为例,采用二值离散化、直方图法及F统计法3种计算方法实现mRMR（minimal-Redundancy-Maximal-Relevance）算法特征优选过程。根据3种方法所得到的特征优选结果及全部特征信息,分别采用C5.0决策树和K近邻2种分类器进行图像分类实验,并利用目视解译方法对不同方法组合的影像分类结果进行精度验证。实验结果表明,利用3种计算方法实现mRMR特征优选算法对不同分类器的影响程度不同：在分类效率方面,C5.0决策树分类器可提高36.84%,而K近邻分类器可提高72.05%;在分类精度方面,C5.0决策树分类器能保证分类精度大致不变,总体分类精度可提高0.60%,Kappa系数可提高0.80%,而K近邻分类器总体分类精度可提高4.34%,Kappa系数可提高7.90%。  相似文献   

结合mRMR选择和IFCM聚类的遥感影像分类算法     

黄磊  向泽君  楚恒 《测绘通报》2019,(4):32-37

为解决高分影像特征间相关性大冗余度高、FCM聚类稳健性差带来的分类精度不佳问题，提出一种基于mRMR选择和改进FCM聚类的影像分类算法。首先基于对象置信度指标（OC）进行影像分割，然后利用mRMR算法实现特征选择，解决特征冗余问题，最后将提取的特征输入分类器通过IFCM聚类，得到最终分类结果。试验结果表明，本文算法能减少特征间相关性，降低冗余，并有效提高影像分类精度。  相似文献   

基于改进mRMR特征选择的云型识别研究     

王俊  谢明元  杨玲  汤志亚  杨智鹏 《气象科技》2013,41(5):803-808

传统的云型识别主要是提取云的颜色、纹理和形状等特征,但这些特征中存在不相关和冗余特征,导致云型识别率降低.在最大相关最小冗余（max relevance and min-redundancy,mRMR）特征选择方法的基础上,运用互信息标准化形式（Symmetrical Uncertainty,SU）克服互信息偏向于取值较多属性的固有缺点,提出了改进的mRMR特征选择方法,对云的综合特征集进行特征筛选,筛选出最优特征子集,运用支持向量机进行云型识别.试验结果表明该方法优于mRMR方法,使层云、积云、高积云、卷云和晴空5种天空类型的总正确率提高,特征选择前、后的总识别率分别为86.96％、89.04％,识别率提高了2％;对于云型识别研究,经过特征选择后可知纹理特征优于形状特征,基于形状的Zernike矩优于HU不变矩,基于纹理的灰度共生矩阵为最优特征提取方法.  相似文献