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.

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.

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 Integration of Storage and Transportation Engineering Technologies in Potato Producing Areas of China

Considering the development of potato （Solanum tuberosum） industry in China, the existing technologies of potato storage and transportation in the produc- ing area were analyzed through investigation on four main potato production areas. Unear classification was used to conduct the technology classification. According to the technical attributes and characteristics, the potato technologies of storage and transportation in producing area were classified with large classes, middle classes, small classes and subclasses, into the agricultural production area processing and storage engineering technology system, to reveal the structure and functions. Mean- while, the widely used technologies were integrated and summarized into 5 principal technology integration programs, which could be used for the technology integration of the new management subjects such as planting professional cooperatives, family farms, enterprises and so on.

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.

Methods of extracting change information and updating land use/cover map based on 3S technology

A fully automated approach for detecting land use/cover change using remote sensing data, GIS data, GPS data is presented. The integrating raster with vector methods of updating the basic land use/land cover map based on 3S technology is becoming one of the most important developing directions in 3S application fields, land-use and cover change fields over the world. It has been successful applied in two tasks of the Ministry of Land and Resources of China, and takes some benefit.

Ecological Risk Assessment of Shan Xin Mining Area Based on Remote Sensing and Geography Information System Technology

In this paper, introducing new remote sensing and geographic information technology to solve the problem of data collection and analysis, this makes the study of ecological risk assessment very quick and accurate. Taking the Shan Xin mining area of the tin mine in Lengshuijiang of Hunan Province as the research object, using the remote sensing image data of three periods in 2005, 2010 and 2015, the remote sensing image is classified carefully and the landscape classification map of the mining area is obtained. The ecological risk index is introduced and the ecological risk values are sampled and interpolated on the ArcGIS platform. The ecological risk spatial distribution map based on the landscape pattern index was obtained. The ecological risk was divided into 5 levels by using the Jenks natural classification method, and each ecological risk grade area was counted. The research results show that: from year 2005 to year 2010, landscape ecological risk trend of the mining area is growing up;the trend rising area of landscape ecological risk is mainly in the southwest and northeast of the Shan Xin mining field;the area of higher and high ecological risk is increasing year by year;and the trend of dispersed development in space is obvious;the development trend of ecological risk in the mining area is rapidly increasing;in 2010 - 2015, the higher and high ecological risk area decrease slightly with the increasing of area of grassland and residential low vulnerability of landscape types;the ecological risk area showed a slow decreasing trend. The research results provide an objective reference for decision making of ecological environment governance.

Clastic compaction unit classification based on clay content and integrated compaction recovery using well and seismic data

Compaction correction is a key part of paleogeomorphic recovery methods. Yet, the influence of lithology on the porosity evolution is not usually taken into account. Present methods merely classify the lithologies as sandstone and mudstone to undertake separate porositydepth compaction modeling. However, using just two lithologies is an oversimplification that cannot represent the compaction history. In such schemes, the precision of the compaction recovery is inadequate. To improve the precision of compaction recovery, a depth compaction model has been proposed that involves both porosity and clay content. A clastic lithological compaction unit classification method, based on clay content, has been designed to identify lithological boundaries and establish sets of compaction units. Also, on the basis of the clastic compaction unit classification, two methods of compaction recovery that integrate well and seismic data are employed to extrapolate well-based compaction information outward along seismic lines and recover the paleo-topography of the clastic strata in the region. The examples presented here show that a better understanding of paleo-geomorphology can be gained by applying the proposed compaction recovery technology.

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.

A Brief Review of 3S Technology Application on Biodiversity Monitoring and Assessment

Remote sensing,geographic information system and GPS(3S)technology have been well recognized as comprehensive,accurate and up-to-date information collection methods,which are increasingly adopted in biodiversity conservation.This review summarizes the application of object-oriented classification methods on biodiversity monitoring projects based on high-resolution remote sensing imagines in China.Biodiversity conservation research based on GIS technology in China is also discussed,with emphasis on the advantages of GIS analysis and modeling function.

NSTL科技文献资源分类体系构建研究

相对科学的分类体系是实现科技文献资源学科特征有效揭示的重要依据。面向国家科技图书文献中心(National Science and Technology Library,NSTL)资源集成与知识服务的总体需求,通过主体及重点学科揭示、多体系融合、多维同位类设置、多学科列类、新兴学科类目扩展、综合性类目设置、双重语义编码等方法,编制可基本覆盖NSTL主体资源的NSTL科技文献分类体系,共包含61个基本大类,类目深度为4~5级,类目数达5350个。同时建立与现用核心分类体系的映射关系,支撑资源分类数据规范处理、馆藏目录系统服务等方面的应用。该分类体系可支撑国家重点扶持产业资源保障分析与学科布局评估。

中医护理技术分级标准及分级预测模型的构建

目的:制定中医护理技术分级标准及分级预测模型,为中医护理技术的分级提供科学依据。方法:2020年11月至2021年10月,采用德尔菲专家函询法及专家现场论证制定中医护理技术分级标准,邀请浙江省内中医护理专家应用中医护理技术分级标准对中医护理技术进行分级评定并构建分级预测模型。结果:根据技术的安全性、学习掌握难易程度及操作难易度将技术分为一级技术、二级技术和三级技术。咨询专家积极系数为94.44%,权威系数为0.913,肯德尔和谐系数为0.892~1.000。构建的技术分级预测模型总准确度为74.5%,ROC曲线下面积为0.904。结论:本研究构建的中医护理技术分级标准具有可靠性、科学性及实用性。技术分级预测模型具有较好的预测准确度,能为中医护理技术的分级评定提供科学依据。

Object-oriented CAD for Hydraulic Circuits of Pressing Machines

This paper describes the design and implementation of an intelligent computer-aided design (ICAD) system for hydraulic circuits of pressing machines. The CAD system is developed based on the object-oriented approach. The application domain of hydraulic presses has been studied thoroughly. The engineering data and the design knowledge are organized in an object-oriented database. A case study has been selected to illustrate the usefulness of the object-oriented CAD system in real applications.

稠油开发技术进展及新分类标准建立与应用实践——以胜利油田稠油开发为例

稠油油藏是一种重要的战略资源,对保障国家能源安全起到重要的作用。国内外稠油开发主要有蒸汽吞吐、蒸汽驱、驱泄复合(SAGD)、火烧驱油4项技术,受技术适应性、成本高及对环境不友好的影响,其推广和运用存在一定局限性。胜利油田根据自身油藏特点,形成了薄层水平井、热化学复合和化学降黏3项新技术,拓展了开发技术界限,使稠油油藏开发的有效厚度界限低至2m、油藏埋深为2000 m、储层渗透率界限低至100×10^(-3)µm^(2)。根据各项技术特点和矿场应用效果,建立了以技术适应性为基础的稠油新分类标准,把稠油油藏分为5大类,指导矿场稠油开发技术方向的选择。结合目前技术发展方向和新的形势要求,指出“多元热复合”“非热力开发”“纳米材料应用”将是稠油开发技术3个趋势。

背部痧象特征要素数显划分方法

目的通过对痧象图片数据的研究,提出一种数显的客观方法来对背部痧象特征要素进行准确辨识,以避免主观认知不同带来的偏差,帮助人们快速辨识痧象的相关特征,为中医医师的诊断提供一定的客观依据,减少主观化成分。方法首先,采用深度语义分割算法模型对采集的背部痧象图片进行分割,以获取只包含痧象的部分区域,去除图片中其他无关部分,尽可能减少对后续实验所带来的影响。然后针对分割得到的图像,结合中医五脏六腑在背部的映射区,采用关键区域检测的方法对痧象颜色特征进行3种区间的识别;根据中医主观上对于痧象形状的划分,采用像素统计方法将痧象形状划分为片状和点状两种。最后,通过准确度、精确度、召回率、F1值对客观识别的结果进行数据上的评价。结果本文提出的客观数显方法在痧象颜色特征划分上的准确率达到了80.56%,在痧象形状特征的划分上准确率达到了89.60%,能够较为准确地对痧象特征要素进行划分,具有一定的可行性。结论本文提出的数显方法能够对痧象的颜色和形状特征进行较为准确的辨识,可以在很大程度上避免主观认知所造成的辨识不同问题,在辅助医师诊断以及用于中医痧象特征的识别教学方面具有较大的应用前景,可为正确了解痧象特征信息提供帮助。

区块链技术下垃圾分类管理平台的试验及熵权-云模型评价

文章基于区块链技术(blockchain technology,BT)的去中心化、可溯源性、不可篡改和激励机制4大特征,构建具有积分激励、宣传教育、线上交易和数据溯源功能的区块链技术下垃圾分类管理平台,并选取合肥市某垃圾分类试点小区进行试验,通过2022年1—9月的问卷调查、实地采样和物理组分分析检验平台的垃圾分类管理效果。结果表明:可回收垃圾回收潜力较试验前增长20.4%,厨余垃圾混杂率降低23.5%;截止到9月公众支持率和参与率超95%,违规事件成功溯源率达83.2%。同时文章首次引入熵权-云模型对区块链平台的垃圾分类管理的试验效果进行评价分析,结果表明,S1(1—3月)、S2(4—6月)、S3(7—9月)试验阶段的垃圾分类管理综合确定度分别为2.53、2.66、2.82,综合评价等级分别为Ⅳ(较差)、Ⅲ(中等)和Ⅱ(良好)。区块链技术下垃圾分类管理平台能够解决垃圾分类过程中分类质量差、公众参与度低等普遍性问题,适用于我国强制性垃圾分类的时代要求。熵权-云模型能考虑问卷调查和采样结果的随机性,实现定性概念到定量数据的转变,在垃圾分类管理领域具有创新性和可行性。

基于机器学习与眼动追踪的认知风格模型构建

认知风格反映了学生接近、获取、组织、处理和解释信息的模式,可用来解释和指导学生的行为。将认知风格集成到智能系统中,有助于开发个性化的用户模型,推动智能教育发展。当前有关认知风格自动分类的研究较为匮乏,尚未将机器学习与眼动追踪技术联合起来进行应用。基于机器学习与眼动追踪的认知风格模型,选取注视时长、注视点数量、扫视时长、眼跳次数、眼跳距离与瞳孔直径等6个与认知有着密切关系的眼动指标,归一化处理后借助机器学习算法进行认知风格自动分类。实验结果表明:在进行同样时长的视频学习时,不同场认知风格的学习者表现出不同的视觉行为模式;场依存型学习者注视点较为分散,表现出有较多的扫视行为、较少的注视行为、较长的眼跳距离与较大的瞳孔直径变化,信息搜索效率较低;而场独立型学习者有着较为密集与定向的视觉注意模式,信息搜索效率更高。对5种机器学习算法进行性能对比后发现,逻辑回归算法的分类效果最好,准确率达到89.01%,Kappa值达到0.774。该认知风格自动化分类模型既可用于智能学习系统的课程资源优化设计,也可用于个性化学习路径的推荐。未来可整合更多生理数据,通过不同模态数据之间的信息互补,提升数据分析的准确性以及对学习者认知能力评估的可靠性。