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.

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 land cover classification using ALS and GeoEye imagery over mining area

An object oriented coal mining land cover classification method based on semantically meaningful image segmentation and image combination of GeoEye imagery and airborne laser scanning (ALS) data was presented. First, DEM, DSM and nDSM (normalized Digital Surface Model, nDSM) were extracted from ALS data. The GeoEye imagery and DSM data were combined to create segmented objects based on neighbor regions merge method. Then 10 kinds of objects were extracted. Different kinds of vegetation objects, including crop, grass, shrub and tree, can be extracted by using NDVI and height value of nDSM. Water and coal pile field was extracted by using NDWI and the standard deviation of DSM method. Height differences also can be used to distinguish buildings from road and vacant land, and accurate building contour information can be extracted by using relationship of neighbor objects and morphological method. The test result shows that the total classification accuracy of the presented method is 90.78% and the kappa coefficient is 0.891 4.

The Building Extraction Based on Object Oriented Classification Method in High Vegetation Coverage Area

Quickly extraction of building information technology is an important application in urban development planning, electronic information, national defense and others. This paper takes Landsat-8 multispectral and panchromatic data as data source, using the local variance method to select the optimal segmentation scale, normalized difference vegetation index (NDVI) and the normalized building index (NDBI) and panchromatic brightness value of an object oriented classification rule extraction. The high vegetation coverage area of buildings, and through the spatial relationships and distinguishing feature of collections of buildings independent buildings and villages. The results showed that Google earth high resolution image analysis and accuracy evaluation. the results of the extraction based on the overall accuracy of village extraction was 83%, the accuracy of extraction of independent buildings was 70%, according to the L8 remote sensing data, object oriented classification method can quickly and accurately extract the high vegetation coverage area of the building.

Research on Modeling and Reusing of Computer Numerical Control Software with Object-oriented Technology

To improve the reusable and configurable ability of computer numerical control ( CNC ) software, a new method to construct reusable model of CNC software with object-oriented (OO) technology is proposed. Based on analyzing function of CNC software, the article presents how to construct a general class library of CNC software with OO technology. Most function modules of CNC software can he reused because of inheritable capability of classes. Besides, the article analyzes the object relational model in request/report mode, and multitask concurrent management model, which can he applied on double-CPU hardware platform and Windows 95/NT environment. Finally, the method has been successfully applied on a turning CNC system and a milling CNC system, and some function modules have been reused.

Product Family Design Methodology Using Object-Oriented Approach

The serious competition environment of global marke t requests the enterprise to change traditional product development strategy and a dopt new theory in order to meet diverse customer needs while maintaining near m ass production efficiency, which is the main philosophy of mass customization. P roduct family design is research focus at present and also is the core technolog y of DFMC (design for mass customization). Firstly, this paper explores the fund amental issues of product family, such as concepts of modularity, commonality/di versity, product platform and product family architecture etc. We compare the te rminology between product family and object-oriented approach in the next step. Thirdly, this paper puts forwards one product family design methodology based o n product platform and under different phase of product life cycle constrains, f or example, functional, assembly and service etc. At the end section of this pap er, we applied, the object-oriented approach in above mentioned product family design methodology to realize the design process. In one word, this paper propos ed one product family design methodology based on object oriented approach and p roduct life cycle consideration, especially the conjointness of characteristic o f OOA and concepts of product family. The main property of OOA are encapsulation , inherence and polymorphism. Encapsulation can represent the module or building blocks of product family. Inherence can be extended to describe the modularity and commonality, and also be used to construct variant space. Alternative specif ic of product family architecture can be embodied with polymorphism. And fin aly, we give the future work contents. In order to derive the product platform a nd achieve modularity and commonality/diversity, interface management between bu ilding block is necessary. The question is how the OOA can be applied in interfa ce management to get our aim OOA is the basis of many information management sy stem, then the question is how to build one system to manage the information of product family and support mass customization The third question is how to deve lop one computer aided tool to facilitate the application of OOA for product fam ily design, even be used to category of design for mass customization.

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.

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.

A Comprehensive Review on Pixel Oriented and Object Oriented Methods for Information Extraction from Remotely Sensed Satellite Images with a Special Emphasis on Cryospheric Applications

Image classification is one of the most basic operations of digital image processing. The present review focuses on the strengths and weaknesses of traditional pixel-based classification (PBC) and the advances of object-oriented classification (OOC) algorithms employed for the extraction of information from remotely sensed satellite imageries. The state-of-the-art classifiers are reviewed for their potential usage in urban remote sensing (RS), with a special focus on cryospheric applications. Generally, classifiers for information extraction can be divided into three catalogues: 1) based on the type of learning (supervised and unsupervised), 2) based on assumptions on data distribution (parametric and non-parametric) and, 3) based on the number of outputs for each spatial unit (hard and soft). The classification methods are broadly based on the PBC or the OOC approaches. Both methods have their own advantages and disadvantages depending upon their area of application and most importantly the RS datasets that are used for information extraction. Classification algorithms are variedly explored in the cryosphere for extracting geospatial information for various logistic and scientific applications, such as to understand temporal changes in geographical phenomena. Information extraction in cryospheric regions is challenging, accounting to the very similar and conflicting spectral responses of the features present in the region. The spectral responses of snow and ice, water, and blue ice, rock and shadow are a big challenge for the pixel-based classifiers. Thus, in such cases, OOC approach is superior for extracting information from the cryospheric regions. Also, ensemble classifiers and customized spectral index ratios (CSIR) proved extremely good approaches for information extraction from cryospheric regions. The present review would be beneficial for developing new classifiers in the cryospheric environment for better understanding of spatial-temporal changes over long time scales.

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.

PESOI: Process Embedded Service-Oriented Architecture

Service-Oriented Architecture (SOA) has drawn significant attention recently, and numerous architecture approaches have been proposed to represent SOA-based applications. The architecture of SOA-based applications is different from traditional software architecture, which is mainly static. The architecture of an SOA-based application is dynamic, i.e., the application can be composed at runtime using existing services, and thus the architecture is really determined at runtime, instead of design time. SOA applications have provided a new direction for software architecture study, where the architecture can be dynamically changed at runtime to meet the new application requirements. This paper proposes a Process-Embedded Service-Oriented Infrastructure to build SOA-based applications. This infrastructure embeds the entire software lifecycle management and service-oriented system engineering into the application developed on this infrastructure. Thus, the users can easily re-develop the applications during operation to meet the changing environments and requirements, through the supports provided by the embedded infrastructure.

Product Image Classification Based on Fusion Features

Two key challenges raised by a product images classification system are classification precision and classification time. In some categories, classification precision of the latest techniques, in the product images classification system, is still low. In this paper, we propose a local texture descriptor termed fan refined local binary pattern, which captures more detailed information by integrating the spatial distribution into the local binary pattern feature. We compare our approach with different methods on a subset of product images on Amazon/e Bay and parts of PI100 and experimental results have demonstrated that our proposed approach is superior to the current existing methods. The highest classification precision is increased by 21% and the average classification time is reduced by 2/3.

基于Gaofen-2影像和面向对象的椰子林分类研究

椰子是重要的热带经济作物,海南椰子种植面积占全国的90%以上。快速获取椰子种植面积及其空间分布信息对热带作物产业规划具有十分重要的作用。本研究基于国产Gaofen-2高分辨率卫星影像,以文昌市东郊镇为试验区,开展椰子林遥感分类研究。基于最优分割尺度的面向对象分类方法,选取4个光谱特征、5个植被指数和32个纹理特征为辅助参量,构建了4种不同的面向对象分类组合(光谱特征、光谱特征+纹理特征组合、光谱特征+植被指数组合、光谱特征+纹理特征+植被指数特征组合)进行椰子林分类提取,并与基于像元的椰子林分类结果进行对比分析。结果表明:(1)仅采用基于像元分类方法,椰子林的总体分类精度(overall accuracy,OA)和用户精度(user’s accuracy,UA)分别达到87.05%和85.21%。(2)相比基于像元分类,4种面向对象分类组合的OA值提高了5.51%~8.72%。(3)光谱特征和纹理特征组合提取椰子林分类结果最优,OA值和UA值分别达到95.77%和97.15%;光谱特征和植被指数的组合也得到了较好的分类结果,OA值和UA值分别为94.88%和94.42%;所有的光谱特征、植被指数和纹理特征全部参与分类得到的OA值和UA值分别为94.67%和94.17%,低于仅使用光谱特征或者植被指数的组合。综上,国产高分辨率Gaofen-2影像在椰子林遥感精准识别中具有很大的潜力,结合纹理特征的面向对象分类方法可以更准确地提取椰子林分类信息,研究结果可为多云多雨地区大尺度椰子林遥感识别提供技术参考。

双向拉伸聚乙烯薄膜专用料的结构剖析

利用凝胶渗透色谱仪(GPC)、差示扫描量热仪(DSC)和核磁共振波谱仪(NMR)等对2种进口双向拉伸聚乙烯(BOPE)薄膜专用料(SP3022和TF80)和国产茂金属线型低密度聚乙烯(mLLDPE)薄膜专用料(EZP2010HA)的结构和性能进行了分析对比。结果表明:与EZP2010HA相比,SP3022和TF80的熔体流动速率更低,相对分子质量及其分布更宽,密度、熔融温度、结晶温度、结晶度均更高,厚晶片含量更多,剪切黏度更小,更适合双向拉伸加工。

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.

基于“布鲁姆认知—成果导向”的“机车制动系统”课程教学模式研究

文章针对“机车制动系统”课程中存在的教学模式单一,教学资源缺乏系统性,教学评价不全面等问题,进一步将先进的教育理念深度地融入混合型教学中,提出一种基于布鲁姆认知目标理论和成果导向教育理念的“机车制动系统”课程教学模式研究,研究结果表明对该课程进行改革与实践研究具有非常大的意义,可以为其他专业核心课程的建设与改革做一次有益的探索和尝试。

Landsat和GF数据面向对象土地覆盖分类研究

针对中分辨率遥感数据面向对象分类,以河北省北部山区和南部平原Landsat8 OLI,Landsat5 TM及高分一号(GF1)数据为研究对象,对支持向量机(support vector machine,SVM)、随机森林(random forest,RF)、决策树(decision tree,DT)及朴素贝叶斯(naive Bayes,NB)4种分类器的土地覆盖分类效果进行对比,并分析SVM,RF和DT中关键参数对分类结果的影响。结果表明:在2个研究区,各分类器结果略有差异,从整体上看其优劣排序为SVM,NB,RF和DT。其中SVM和DT分类精度随参数变化波动较大:对于SVM,当参数C取值不小于103且gamma不大于10^(-1)时,无论哪种情况其分类精度均优于90%;对于DT,当参数树深(Depth)大于3时,各情况下的分类精度相对较高且趋于稳定。RF分类精度随参数变化波动较小且没有明显的变化规律。研究结果可为中分辨率遥感数据面向对象土地覆盖分类研究提供参考。

基于无人机遥感影像和面向对象技术的荒漠草原植被分类

探究适合荒漠草原植被遥感分类方法,明确荒漠草原地区植物物种类型及其分布状况,可以提高荒漠草原精细化生物多样性监测能力,对于荒漠草原的保护管理与生态可持续发展均具有重要意义。以贺兰山东麓洪积扇荒漠草原典型植被短花针茅、松叶猪毛菜、刺旋花、斑子麻黄为研究对象,利用无人机遥感影像,采用面向对象的分类回归树(classification and regression tree,CART)、K最邻近(K-nearest neighbor,KNN)、随机森林(random forest,RF)和支持向量机(support vector machine,SVM)分类方法,结合特征优选算法对影像特征进行优选,在此基础上选择最优特征进行荒漠草原植被精细化分类研究。结果表明:1)特征优选能够有效提高分类精度,应予以充分利用,当选取的特征组合为贡献度大于1.00%时,分类精度最高;2)基于无人机遥感影像挖掘的植被光谱、纹理特征,结合面向对象分类方法能有效实现贺兰山东麓荒漠草原典型植被精细化分类,其中RF分类精度最高,分类总体精度达到87.77%,Kappa系数为0.79。研究结果可为荒漠草原植被分类研究提供参考,对荒漠草原生物多样性保护管理与生态可持续发展均具有重要意义。

高强度训练:概念厘清、功能分类和应用导向

采用文献资料、逻辑分析等方法,剖析高强度训练概念、功能分类,并提出实践应用导向。基于明确训练强度、持续时间和训练手段等本质特征,认为高强度训练是训练强度大于无氧阈或最大乳酸稳态,持续时间相对短暂,以及训练手段多元变化的综合体能训练体系,涵盖多种功能差异的训练模式。基于本质特征与实践效果差异,高强度训练可以分为高强度间歇、耐力、抗阻、冲刺、功能训练5种训练模式,应用于特定的人群、场景等,发展出改善有氧与无氧系统、肌肉力量或整体体能素质等训练效果。高强度训练在分类功能、项目需求、训练安排、训练内容与训练创新上,以满足精确化选择、专项化体能、复合化训练、整体化设计以及科学化进程等诉求为未来应用导向,对训练强度定量细分、功能效果精专应用、单一模式交互实践、训练板块合理安排以及训练模式创新发展,促进高强度训练专门性训练模式发展和针对性功能实践。

融合多源地理数据与高分辨率遥感影像的尾矿库识别与监测——以云南省个旧市为例

尾矿库是具有高势能的重大危险源,对尾矿库空间范围开展快速识别与监测,及时掌握尾矿库数量及分布情况,对我国尾矿库的环境监管与治理具有重要意义。现有单纯基于遥感影像识别提取的思路,因缺乏尾矿库潜在目标针对性,易将裸露地表等混淆为尾矿库,给实际的尾矿库监测应用带来较大误差。为此,提出一种融合企业名录与空间分布点位、数字高程模型(digital elevation model,DEM)、道路网等多源地理数据与高分遥感影像的尾矿库提取方法。以云南省个旧市为研究区的应用验证结果表明:融合多源地理数据可有效排除不存在尾矿库的干扰区域,尾矿库提取结果的精确率和召回率分别为83.9%和72.4%。该技术方案在全国尺度的尾矿库高频次、自动化遥感监测中具有广阔的应用前景。