结合KPCA和多尺度纹理的IKONOS遥感影像决策树分类

城市地物类型多样,空间分布复杂,具有很强的非线性特征。核主成分分析(KPCA)通过将特征空间映射到高维核空间,可以表达图像像素间的高阶关系,因而可以提取图像的非线性特征,同时提供一组相互独立的主成分。本文在加入多尺度纹理特征的基础上,以应用地物分布的空间细节信息;且利用核主成分分析(KP-CA)方法对光谱和纹理量提取非线性特征信息,增大类别之间的可分性;并结合决策树分类方法对IKONOS遥感影像分类。实验结果表明:KPCA能很好提取地物之间的非线性特征,结合KPCA和多尺度纹理的决策树分类方法能有效地提取地物类型,提取精度为79.3%,KAPPA系数为0.763.     

.基于纹理和边缘的SAR图像SVM分类*

为实现SAR图像地物目标的有效分类,深入研究了基于灰度共生矩阵GLCM的四种纹理特征以及两个边缘特征。分析每个单独纹理或边缘特征在对SAR图像进行支持向量机SVM分类中对不同地物的分辨能力,选取不同的特征组合进行组合特征的SVM分类实验。对各种特征进行主成分分析PCA,并考察使用和不使用PCA两种情况下分类结果之间的差异。实验结果证明能量、边缘长度、对比度和相关度的特征组合在PCA作用下能够改善各类地物的分类精度,将总分类精度提高到90%以上。     

基于纹理信息CART决策树的林芝县森林植被面向对象分类

以西藏自治区林芝县的Landsat-8影像、地形图为信息源,结合样地调查数据及森林资源二类调查数据,研究基于纹理信息的CART决策树的面向对象分类对研究区内的森林地物类别进行提取,分类的总体精度和Kappa系数分别为82.53%和0.768,相较于不利用纹理信息的决策树分类和基于最大似然分类法的研究区地物类别的提取总体精度均高近10%,Kappa系数分别高0.12和0.111。结果表明:基于纹理信息的CART决策树面向对象分类方法对研究区Landsat-8影像进行植被类型提取,分类结果较好,能够满足研究要求。     

基于面向对象的遥感影像分类研究——以河北省柏乡县为例

遥感作为提取土地覆盖类型的主要手段对监测土地利用变化和制定国家政策具有重要意义。通过利用影像的光谱、形状和纹理信息,面向对象分类方法能够比基于像元的分类方法提供更高精度的数据。基于高分一号卫星数据提出一种自动计算最优尺度的方法,基于多尺度分割和3种监督型机器学习算法对研究区典型地物类型(农田、裸地、居民区和道路)进行面向对象分类,并用总体精度和Kappa系数对分类结果进行精度评价,分析了分类精度与训练样本占总样本比例的关系。研究表明,面向对象分类方法在训练样本占总样本比例较小的情况下就可以取得较高的分类精度,总体精度高于94%。总体来看,支持向量的分类精度比神经网络和决策树的分类精度高。     

PCA与决策树相结合的移动通信欠费预测分析

针对日趋严重的移动通信欠费问题,文章根据某地区移动客户的产品信息、呼叫行为信息(CDR)、背景资料、交欠费信息等数据,采用了主成份分析(PCA)的属性归约技术对数据进行预处理,然后使用决策树分类技术对数据分类,从而识别出新用户的欠费类型。在此基础上,进一步与双变量统计的预处理方式进行了比较。实验结果表明,使用PCA与决策树相结合的分类方式具有很好的准确率。     

利用提升树模型综合Gabor和LPQ特征进行遥感地物识别

为充分融合高光谱遥感图像空间域和频率域的特征信息,提出了一种综合多尺度Gabor和LPQ特征的空谱融合遥感地物识别模型（Ms_GLPQ）。首先,在空间域上利用Gabor滤波器组,提取出遥感图像各类地物多尺度、多方向的空间邻域特征信息,以描述图像的边缘和纹理等空间结构信息;其次,在频率域上将局部相位量化（Local Phase Quantization,LPQ）算子应用于高光谱遥感图像,提取出高光谱图像的多尺度频域纹理特征,获得图像的相位不变特征描述;然后针对其中特征冗余的问题采用主成分分析（PCA）算法进行降维,再将空间域、频率域的特征进行特征融合,获得了能充分描述图像信息的特征向量;最后采用基于提升树的机器学习分类器（XGBoost、CatBoost等）进行识别。在Indian Pines、Salinas和茶树等高光谱遥感数据集上进行学习与分类测试,准确率分别为85.88%、94.42%和92.61%。实验结果表明：与传统方法相比,Ms_GLPQ模型能够提取小比例样本图像中的有效特征,取得了区分性更强的多特征区域描述子,且在采用提升树模型进行分类时效果更优,得到了比常用分类器更高的识别精度。     

太湖湖滨敏感区的土地利用遥感分类研究

近年来太湖流域水体污染日趋严重,土地利用是重要的环境变化影响因子,对太湖湖滨敏感区土地利用分类研究具有重要意义。研究基于2010年ALOS多光谱遥感影像,以太湖流域上游的武进港、直湖港流域为研究区,根据研究区实际状况和研究目的,建立太湖流域上游湖滨敏感区的土地利用/土地覆被分类系统,并用于该地区的面向对象遥感分类,研究通过影像的多尺度分割,获得不同层次的影像对象,在不同层次设置对应的分类规则,以充分利用影像中地物的光谱、纹理和不同层对象相互关系等信息,从而提高分类效果。研究表明:在面向对象多尺度影像分割的基础上,基于决策树建立多个分类规则的分类方法,能够有效提取建设用地、道路、水体等几类信息,分类总体精度达到88.00%;同时,该地区主要土地利用类型如耕地、农村居民点和城镇居民点的分类精度也较高,这也表明该分类方法对整个太湖流域以及其他平原河网地区的土地利用相关研究具有一定的实用价值。     

基于面向对象方法的沙化土地遥感信息提取技术研究

针对基于像元光谱特征提取沙化土地信息分类精度偏低的问题,以Landsat\|5 TM为数据源,基于面向对象的方法对沙化土地遥感信息提取技术进行研究。首先采用多尺度分割法对影像进行分割以获得同质区域,然后结合野外调查数据制成不同地物类型的多种特征图,从而确定提取目标地物的特征并建立沙化和非沙化地物提取决策树,最后对影像进行模糊分类,并对分类结果进行精度评价。结果表明,基于面向对象提取沙化土地信息的总精度达84.89%,Kappa系数为0.8077。研究结果为后续深入研究奠定了基础。     

基于纹理的乌兰布和沙漠地区植被信息提取

乌兰布和沙漠是我国主要的沙漠之一,近年来,其快速扩张已严重影响当地的生态安全。荒漠植被是该地区最重要的生态防护屏障,准确掌握植被分布状况具有重要意义。以乌兰布和沙漠的典型地区为研究对象,通过NDVI计算、主成分分析以及基于灰度共生矩阵纹理特征相结合的方法,对ALOS多光谱影像进行分析,综合NDVI和均值纹理作为分类指标,确定合适的阈值范围,采用决策树分类方法进行植被信息提取。研究表明,决策树分类可有效运用纹理等辅助信息,与传统分类方法相比能够取得更好的分类效果。     

基于梯度提升决策树的电力电子电路故障诊断

针对电力电子电路的常见故障类型,提出一种利用主元分析(PCA)提取电路状态的故障信息特征和基于梯度提升决策树(GBDT)分类的电力电子电路故障诊断方法.首先讨论利用PCA进行特征提取的步骤以及GBDT的分类原理;然后研究了基于PCA特征提取以及GBDT分类的电力电子电路故障诊断流程;最后利用三相桥式整流电路进行了建模、仿真、验证,实验结果表明,采用该方法进行电力电子电路故障诊断相比其他方法在低维空间具有更高的诊断准确率和更佳的样本泛化能力.     

Research on Classification of Land Cover based on LiDAR Cloud and Aerial Images

Aerial images contain abundant spectral information,texture information and spatial information,and airborne LiDAR can provide three-dimensional information of ground objects.An object-oriented classification method was researched by taking advantages of the two types of data.Converting LiDAR point cloud into 2-D raster image by preprocessing,and matched it with aerial image.Then,multi-scale segmentation algorithm was applied to image segmentation based on spectral information and height information.Next,XGBoost algorithm were applied to select features extracted from segmented object respectively.The SVM classifier was used to classify and prove the superiority of XGBoost algorithm by comparing with two traditional feature selection algorithms：Relief and RFE.Finally,objects at shadow regions were distinguished and merged into real objects based on certain rules.Testing the method in three regions,the results showed that the method was feasible and effective,and could be well applied to the classification of urban ground object.     

基于多层支持向量机的极化合成孔径雷达特征分析与分类

为了充分利用极化合成孔径雷达（SAR）图像不同极化特征对不同地物目标类型的刻画能力,提出一种基于多层支持向量机（SVM）的极化SAR特征分析与分类方法。该方法首先通过特征分析确定适合不同地物类型的最佳特征子集;然后采用分层分类树的方式,根据每一种地物类型的特征子集逐层进行SVM分类;最终得到整体分类结果。RadarSAT-2极化SAR图像分类实验结果表明所提方法水域、耕地、林地、城区4类地物分类精度为85%左右,总体分类精度达到86%。该算法充分利用了不同地物目标类型的特性,提高了分类精度,也降低了算法时间复杂度。     

基于决策树的面向对象变化信息自动提取研究

为了从不同时相的遥感影像数据中自动提取变化信息且保证其效率,本文结合面向对象分析技术,提出了一种基于决策树变化信息自动提取的新方法。该方法利用影像的特征指数及形状特征、光谱特征、纹理特征等作为特征集,将其作为知识库应用到决策树控制模型中,进而利用该模型实现自动分类。对所得到的分类后影像对象,组织分析其综合属性并作为决策规则再次分类,通过"双重分类"的方式实现面向对象的遥感影像变化信息自动提取。该方法为遥感影像变化信息自动提取提供了新的思路。     

A novel algorithm for land use and land cover classification using RADARSAT-2 polarimetric SAR data

This study proposes a new four-component algorithm for land use and land cover (LULC) classification using RADARSAT-2 polarimetric SAR (PolSAR) data. These four components are polarimetric decomposition, PolSAR interferometry, object-oriented image analysis, and decision tree algorithms. First, polarimetric decomposition can be used to support the classification of PolSAR data. It is aimed at extracting polarimetric parameters related to the physical scattering mechanisms of the observed objects. Second, PolSAR interferometry is used to extract polarimetric interferometric information to support LULC classification. Third, the main purposes of object-oriented image analysis are delineating image objects, as well as extracting various textural and spatial features from image objects to improve classification accuracy. Finally, a decision tree algorithm provides an efficient way to select features and implement classification. A comparison between the proposed method and the Wishart supervised classification which is based on the coherency matrix was made to test the performance of the proposed method. The overall accuracy of the proposed method was 86.64%, whereas that of the Wishart supervised classification was 69.66%. The kappa value of the proposed method was 0.84, much higher than that of the Wishart supervised classification, which exhibited a kappa value of 0.65. The results indicate that the proposed method exhibits much better performance than the Wishart supervised classification for LULC classification. Further investigation was carried out on the respective contribution of the four components to LULC classification using RADARSAT-2 PolSAR data, and it indicates that all the four components have important contribution to the classification. Polarimetric information has significant implications for identifying different vegetation types and distinguishing between vegetation and urban/built-up. The polarimetric interferometric information extracted from repeat-pass RADARSAT-2 images is important in reducing the confusion between urban/built-up and vegetation and that between barren/sparsely vegetated land and vegetation. Object-oriented image analysis is very helpful in reducing the effect of speckle in PolSAR images by implementing classification based on image objects, and the textural information extracted from image objects is helpful in distinguishing between water and lawn. The decision tree algorithm can achieve higher classification accuracy than the nearest neighbor classification implemented using Definiens Developer 7.0, and the accuracy of the decision tree algorithm is similar with that of the support vector classification which is implemented based on the features selected using genetic algorithms. Compared with the nearest neighbor and support vector classification, the decision tree algorithm is more efficient to select features and implement classification. Furthermore, the decision tree algorithm can provide clear classification rules that can be easily interpreted based on the physical meaning of the features used in the classification. This can provide physical insight for LULC classification using PolSAR data.     

Morphological hat-transform scale spaces and their use in pattern classification

In this paper we present a multi-scale method based on mathematical morphology which can successfully be used in pattern classification tasks. A connected operator similar to the morphological hat-transform is defined, and two scale-space representations are built. The most important features are extracted from the scale spaces by unsupervised cluster analysis, and the resulting pattern vectors provide the input of a decision tree classifier. We report classification results obtained using contour features, texture features, and a combination of these. The method has been tested on two large sets, a database of diatom images and a set of images from the Brodatz texture database. For the diatom images, the method is applied twice, once on the curvature of the outline (contour), and once on the grey-scale image itself.     

Classification of Remote Sensing Image based on Object-oriented Method:A Case Study of Baixiang County

Remote sensing is the main means of extracting land cover types,which has important significance for monitoring land use change and developing national policies.Object-based classification methods can provide higher accuracy data than pixel-based methods by using spectral,shape and texture information.In this study,we choose GF-1 satellite’s imagery and proposed a method which can automatically calculate the optimal segmentation scale.The object-based methods for classifying four typical land cover types are compared using multi-scale segmentation and three supervised machine learning algorithms.The relationship between the accuracy of classification results and the training sample proportion is analyzed and the result shows that object-based methods can achieve higher classification results in the case of small training sample ratio,overall accuracies are higher than 94%.Overall,the classification accuracy of support vector machine is higher than that of neural network and decision tree during the process of object-oriented classification.     

地物提取的多尺度特征遥感应用分析

通过空间尺度效应分析,阐述不同属性景观地物在同一分辨率或同一尺度影像中提取的不合理性。为获得精确的地表信息,提出多尺度遥感影像分析方法,解决不同地物在不同空间尺度影像数据中提取的难题。通过多种分辨率影像的多尺度影像信息提取的应用实践,分析地物提取中的多尺度特性、尺度与分辨率关系等。     

基于改进决策树分类算法的遥感影像分类研究

针对现有分类器对遥感影像分类结果存不准确的问题,本文提出了一种基于决策树分类器的遥感影像分类方法,该方法以复合决策树Boost Tree思想为基础,首先利用分形理论中的毯模型提取遥感影像的纹理特征,根据遥感影像分类的特点,构造新的单棵决策树生成算法对遥感影像进行分类。以北京市五环内区域为研究区,使用landsat7 ETM数据源,实现了基于分形纹理特征、光谱特征的改进决策树分类。实验结果表明：通过毯模型提取的纹理特征可以很好地表达表面特征,辅以该纹理信息的改进决策树分类精度相比于只用光谱信息进行分类的精度有一定的提高,改善了分类效果。