Linear logistic regression with weight thresholding for flow regime classification of a stratified wake

A stratified wake has multiple flow regimes,and exhibits different behaviors in these regimes due to the competing physical effects of momentum and buoyancy.This work aims at automated classification of the weakly and the strongly stratified turbulence regimes based on information available in a full Reynolds stress model.First,we generate a direct numerical simulation database with Reynolds numbers from 10,000 to 50,000 and Froude numbers from 2 to 50.Order(100)independent realizations of temporally evolving wakes are computed to get converged statistics.Second,we train a linear logistic regression classifier with weight thresholding for automated flow regime classification.The classifier is designed to identify the physics critical to classification.Trained against data at one flow condition,the classifier is found to generalize well to other Reynolds and Froude numbers.The results show that the physics governing wake evolution is universal,and that the classifier captures that physics.

Review of Remotely Sensed Imagery Classification Patterns Based on Object-oriented Image Analysis

With the wide use of high-resolution remotely sensed imagery, the object-oriented remotely sensed informa- tion classification pattern has been intensively studied. Starting with the definition of object-oriented remotely sensed information classification pattern and a literature review of related research progress, this paper sums up 4 developing phases of object-oriented classification pattern during the past 20 years. Then, we discuss the three aspects of method- ology in detail, namely remotely sensed imagery segmentation, feature analysis and feature selection, and classification rule generation, through comparing them with remotely sensed information classification method based on per-pixel. At last, this paper presents several points that need to be paid attention to in the future studies on object-oriented RS in- formation classification pattern: 1) developing robust and highly effective image segmentation algorithm for multi-spectral RS imagery; 2) improving the feature-set including edge, spatial-adjacent and temporal characteristics; 3) discussing the classification rule generation classifier based on the decision tree; 4) presenting evaluation methods for classification result by object-oriented classification pattern.

Pine wilt disease detection in high-resolution UAV images using object-oriented classification

Pine wilt disease(PWD)is currently one of the main causes of large-scale forest destruction.To control the spread of PWD,it is essential to detect affected pine trees quickly.This study investigated the feasibility of using the object-oriented multi-scale segmentation algorithm to identify trees discolored by PWD.We used an unmanned aerial vehicle(UAV)platform equipped with an RGB digital camera to obtain high spatial resolution images,and multiscale segmentation was applied to delineate the tree crown,coupling the use of object-oriented classification to classify trees discolored by PWD.Then,the optimal segmentation scale was implemented using the estimation of scale parameter(ESP2)plug-in.The feature space of the segmentation results was optimized,and appropriate features were selected for classification.The results showed that the optimal scale,shape,and compactness values of the tree crown segmentation algorithm were 56,0.5,and 0.8,respectively.The producer’s accuracy(PA),user’s accuracy(UA),and F1 score were 0.722,0.605,and 0.658,respectively.There were no significant classification errors in the final classification results,and the low accuracy was attributed to the low number of objects count caused by incorrect segmentation.The multi-scale segmentation and object-oriented classification method could accurately identify trees discolored by PWD with a straightforward and rapid processing.This study provides a technical method for monitoring the occurrence of PWD and identifying the discolored trees of disease using UAV-based high-resolution images.

Double Polarization SAR Image Classification based on Object-Oriented Technology

This paper proposed to use double polarization synthetic aperture radar (SAR) image to classify surface feature, based on DEM. It takes fully use of the polarization information and external information. This pa-per utilizes ENVISAT ASAR APP double-polarization data of Poyang lake area in Jiangxi Province. Com-pared with traditional pixel-based classification, this paper fully uses object features (color, shape, hierarchy) and accessorial DEM information. The classification accuracy improves from the original 73.7% to 91.84%. The result shows that object-oriented classification technology is suitable for double polarization SAR’s high precision classification.

Object-oriented crop classification based on UAV remote sensing imagery

UAV remote sensing images have the advantages of high spatial resolution,fast speed,strong real-time performance,and convenient operation,etc.,and have become a recently developed,vital means of acquiring surface information.It is an important research task for precision agriculture to make full use of the spectrum,texture,color and other characteristic information of crops,especially the spatial arrangement and structure information of features,to explore effective methods for the classification of multiple varieties of crops.In order to explore the applicability of the object-oriented method to achieve accurate classification of UAV high-resolution images,the paper used the object-oriented classification method in ENVI to classify the UAV high-resolution remote sensing image obtained from the orderly structured 28 species of crops in the test field,which mainly includes image segmentation and object classification.The results showed that the plots obtained after classification were continuous and complete,basically in line with the actual situation,and the overall accuracy of crop classification was 91.73%,with Kappa coefficient of 0.87.Compared with the crop planting area based on remote sensing interpretation and field survey,the area error of 17 species of crops in this study was controlled within 15%,which provides a basis for object-oriented crop classification of UAV remote sensing images.

Classification and Extraction of Urban Land-Use Information from High-Resolution Image Based on Object Multi-features

Urban land provides a suitable location for various economic activities which affect the development of surrounding areas. With rapid industrialization and urbanization, the contradictions in land-use become more noticeable. Urban administrators and decision-makers seek modern methods and technology to provide information support for urban growth. Recently, with the fast development of high-resolution sensor technology, more relevant data can be obtained, which is an advantage in studying the sustainable development of urban land-use. However, these data are only information sources and are a mixture of ＂information＂ and ＂noise＂. Processing, analysis and information extraction from remote sensing data is necessary to provide useful information. This paper extracts urban land-use information from a high-resolution image by using the multi-feature information of the image objects, and adopts an object-oriented image analysis approach and multi-scale image segmentation technology. A classification and extraction model is set up based on the multi-features of the image objects, in order to contribute to information for reasonable planning and effective management. This new image analysis approach offers a satisfactory solution for extracting information quickly and efficiently.

ORDERED-OBJECT-ORIENTED METHOD:A NEW APPROACH OF SAMPLE PART CALCULATION AND DESIGN

This paper proposed a new approach of sample part classification and design, a so called Or-dered-object-oriented method (O-O-O method). Based on the theory of neural networks, fuzzy clustering algorithm and adaptive pattern recognition, O-O-O method can be used to classify and design the sample parts automatically. The basic theory, the main step as well as the characteristics of the method are analysed. The construction of the ordered object in application is also presented in this paper.

A classification method of building structures based on multi-feature fusion of UAV remote sensing images

In order to improve the accuracy of building structure identification using remote sensing images,a building structure classification method based on multi-feature fusion of UAV remote sensing image is proposed in this paper.Three identification approaches of remote sensing images are integrated in this method:object-oriented,texture feature,and digital elevation based on DSM and DEM.So RGB threshold classification method is used to classify the identification results.The accuracy of building structure classification based on each feature and the multi-feature fusion are compared and analyzed.The results show that the building structure classification method is feasible and can accurately identify the structures in large-area remote sensing images.

Object-Based Classification of Urban Distinct Sub-Elements Using High Spatial Resolution Orthoimages and DSM Layers

This paper aims to assess the ways in which multi-resolution object-based classification methods can be used to group urban environments made up of a mixture of buildings, sub-elements such as car parks, roads, shades and pavements and foliage such as grass and trees. This involves using both unmanned aerial vehicles (UAVs) which provide high-resolution mosaic Orthoimages and generate a Digital Surface Model (DSM). For the study area chosen for this paper, 400 Orthoimages with a spatial resolution of 7 cm each were used to build the Orthoimages and DSM, which were georeferenced using well distributed network of ground control points (GCPs) of 12 reference points (RMSE = 8 cm). As these were combined with onboard RTK-GNSS-enabled 2-frequency receivers, they were able to provide absolute block orientation which had a similar accuracy range if the data had been collected by traditional indirect sensor orientation. Traditional indirect sensor orientation involves the GNSS receiver in the UAV receiving a differential signal from the base station through a communication link. This allows for the precise position of the UAV to be established, as the RTK uses correction, allowing position, velocity, altitude and heading to tracked, as well as the measurement of raw sensor data. By assessing the results of the confusion matrices, it can be seen that the overall accuracy of the object-oriented classification was 84.37%. This has an overall Kappa of 0.74 and the data that had poor classification accuracy included shade, parking lots and concrete pavements. These had a producer accuracy (precision) of 81%, 74% and 74% respectively, while lakes and solar panels each scored 100% in comparison, meaning that they had good classification accuracy.

增强劳动技能与改善劳动条件:农村低收入人口增收路径分析

扎实推进共同富裕,需要推动更多农村低收入人口迈入中等收入行列。如何对农村低收入人口进行类型细分,并探索其有效增收路径成为亟待解决的问题。从劳动视角进一步探源农村低收入人口收入困境发现,城乡二元体制、农村内部“共享”机制、人口生物性特征和社会性特征综合影响农村人口“劳动条件”和“劳动技能”,造成了部分群体收入低的现状;单一的劳动技能视角不能从根本上解决增收问题,必须促进劳动者的劳动技能与劳动对象的结合,在授人以渔的同时,还要确保他们有“捕鱼机会”。基于“劳动技能-劳动条件”结合情况,农村低收入人口类型可以划分为技能不足型、条件不足型以及技能与条件不足型共3种。促进农村低收入人口增收,在基本保障方面要建设关于体力劳动者的社会保护网,维持基本收入;在能力建设方面要构建基础性和专业性技能并重的人力资本增量体系,促进持续增收;在资产建设方面要实现规模农业与非农就业的和谐共进,释放增收潜力;在优化组合方面要实施劳动技能与劳动条件以强带弱的策略,形成增收合力。

Object-oriented land cover classification using HJ-1 remote sensing imagery

The object-oriented information extraction technique was used to improve classification accuracy,and addressed the problem that HJ-1 CCD remote sensing images have only four spectral bands with moderate spatial resolution.We used two key techniques:the selection of optimum image segmentation scale and the development of an appropriate object-oriented information extraction strategy.With the principle of minimizing merge cost of merging neighboring pixels/objects,we used spatial autocorrelation index Moran's I and the variance index to select the optimum segmentation scale.The Nearest Neighborhood(NN) classifier based on sampling and a knowledge-based fuzzy classifier were used in the object-oriented information extraction strategy.In this classification step,feature optimization was used to improve information extraction accuracy using reduced data dimension.These two techniques were applied to land cover information extraction for Shanghai city using a HJ-1 CCD image.Results indicate that the information extraction accuracy of the object-oriented method was much higher than that of the pixel-based method.

扬水曝气对峡谷分层型水库藻类控制机制研究

为探明扬水曝气对峡谷分层型水库中不同径向藻类生物量和结构的影响,以李家河水库为对象,对扬水曝气系统(WLAs)运行期间水体理化性质参数和藻类数量及物种进行了高频监测,采用藻类传统和功能分类法,建立了控制条件下藻类的演替规律和控制机制.结果表明,WLAs运行12d后,径向S1~S4点藻密度削减率分别为92.7%、92.9%、92.1%和89.2%;径向S1~S4点藻属结构发生演替,绿藻(珊藻)和硅藻(短缝藻和羽纹藻)转向硅藻(针杆藻,小环藻),即“高温、大型且低比表面积(S/V)藻”转向“低温、小型且高S/V藻”;水温、光可利用率(Zeu/Zmix)、营养盐的降低和混合层深度(Zmix)的增加是扬水曝气系统控藻的主要原因;径向S1~S4点Q指数升高且TLI指数降低,表明富营养化得到改善.

基于Caprini危险分级分层护理模式预防老年膝关节置换术后下肢深静脉血栓的应用观察

目的 探讨基于Caprini危险分级分层护理模式在预防老年膝关节置换术后下肢深静脉血栓(DVT)中的应用效果。方法 将本院2021年6月至2022年6月收治的131例行膝关节置换术的老年患者按抽签法分为对照组65例和观察组66例,对照组予常规护理,观察组予基于Caprini危险分级分层护理,为期1个月,观察两组患者DVT发生风险、DVT发生率、下肢静脉血流情况及相关血液指标变化。结果 干预1月后,观察组DVT风险等级程度低于对照组,差异有统计学意义(P<0.05);观察组DVT发生率为1.52%,对照组DVT发生发生率为10.77%,观察组下肢DVT的发生率低于对照组,差异有统计学意义(P<0.05)。干预1个月后,观察组患者股静脉、腘静脉及胫后静脉血流流速均高于对照组,差异有统计学意义(P<0.05)。干预1个月后,观察组患者D-二聚体(D-D)及纤维蛋白原(FIB)水平均低于对照组,差异有统计学意义(P<0.05)。结论 对行膝关节置换术的老年患者采取基于Caprini危险分级分层护理,能有效改善患者下肢静脉血流流速,抑制血液高凝状态,降低DVT发生风险。

地理国情监测与水土流失土地利用分类共享研究

本研究在分析地理国情监测分类与水土流失土地利用分类的空间分辨率、时间要求、采集精度基础上,探讨二者分类共享的可行性,实际验证分类共享方案。结果表明:二者在精度与现势性等方面具有相似性、共同性,满足相关标准。以塔里木河流域监测区为例,完成地理国情地表覆盖分类与水土流失土地利用分类转换。利用分层不等概率系统抽样方法,确定调查单元339个,对共享方案野外验证,其中正确调查单元274个,准确率为80.8%,进一步表明共享方案可实现全要素共享,可提高水土流失土地利用分类提取效率。

一种增强差异性的半监督协同分类算法

半监督学习中的Tr-i Training算法打破了以往算法对充分冗余视图的限制,并通过利用三个分类器处理标记置信度和样本预测问题提高了标记效率.为进一步增强协同训练过程中分类器之间的差异性以提高性能,本文在其理论基础上提出了一种增强差异性的半监督协同分类算法.该算法利用三个不同的分类器进行学习;考虑到分类模型在更新过程中,可能会因随机抽样导致性能恶化,该算法利用基于标记类别的分层抽样法来对已标记样本集进行抽样,并通过基于分类正确率的加权投票法实现了分类器的集成,提高了预测准确率.本文通过实验对所提出算法与Tr-i Training算法做了性能比较,实验结果表明本文所提出的方法在分类问题上具有较好的性能,验证了该算法的有效性和可行性.

分层抽样支持的广州市南沙区湿地景观遥感分类

南沙区是广州市"南拓"战略的重点发展区域,在城市化过程中若能合理利用与保护境内具有重要生态功能的湿地资源,将有利于促进区域可持续发展.基于分层抽样技术,通过使用Erdas Imagine软件的Frame Sampling Tool工具和Landsat OLI影像,对南沙区的湿地景观进行分类.结果表明:1基于分层抽样的分类方法具有较高的分类精度,如湿地景观分类总精度为84%,Kappa系数为0.8;2该方法通过Erdas Imagine软件的Frame Sampling Tool平台可以对样本进行更有效地估计、训练及管理;3广州市南沙区内湿地资源丰富,占研究区总面积的40.84%,主要分布在珠江出海口及各支流的附近.

四川某公路边坡缓倾角层状岩体结构面分级及成因分析

通过对四川某公路边坡缓倾角层状岩体结构面的研究，认为结构面分布具有独特性，提出应按岩性及结构面性状进行分级的方案；同时分析研究了各类结构面的成因，认为Ⅰ级结构面是沉积作用形成的，Ⅱ级结构面是在Ⅲ级结构面基础上形成的，Ⅲ级结构面是构造作用形成的。

高职化工类专业“基础化学”分层分类教学的探索实践

"基础化学"课程化工类专业的第一门专业基础课,针对本校化学工程学院生源多样化的问题,从生源特点分析入手,对不同专业、不同类型的专业需求分析,从学情分析入手,本着"应知"、"应会"的原则,确定对应的教学目标,在调查的基础上细化知识技能点,以生为本,精心设计教学方法,充分调动学生学习的积极性,让学生真正成为学习的主体,以达到较好的教学效果。

改进的BP算法在路面裂缝分类中的应用

针对传统BP神经网络训练速度慢,误差大且易陷入局部极小值的缺点,设计了一种改进的复合误差函数来代替传统的全局均方误差函数以提高其学习率,同时采用了改进的分层动态调整不同学习率的新BP神经网络对路面裂缝图片进行分类。实验结果表明,与传统方法相比,改进后的算法在检测精度和速度上有了明显的提高。

矿区地表彩色点云的自动分类

以矿区的彩色三维激光点云数据为研究对象,提出了矿区点云快速自动分类及目标提取的方法。首先根据彩色点云的RGB值计算HSV空间中的H值,根据各地物间H值的差异,分别对地面点与非地面点根据地物颜色先验值进行点的提取。然后对提取的点进行聚类计算,利用各类地物点云在空间分布上的显著差异,采用分层截面投影,由投影点最小包围盒的长宽比及面积比对矿区地物点云进行自动分类与提取。最后以Riegl VZ-1000扫描仪采集的某矿区地表点云数据为试验对象,验证本文算法的可行性和实用性。