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Double Polarization SAR Image Classification based on Object-Oriented Technology 被引量:2
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作者 Xiuguo Liu Yongsheng Li +1 位作者 Wei Gao Lin Xiao 《Journal of Geographic Information System》 2010年第2期113-119,共7页
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 u... 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. 展开更多
关键词 SYNTHETIC APERTURE RADAR Image classification object-oriented Pixel-Based DEM
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Entropy Based Feature Fusion Using Deep Learning for Waste Object Detection and Classification Model
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作者 Ehab Bahaudien Ashary Sahar Jambi +1 位作者 Rehab B.Ashari Mahmoud Ragab 《Computer Systems Science & Engineering》 SCIE EI 2023年第12期2953-2969,共17页
Object Detection is the task of localization and classification of objects in a video or image.In recent times,because of its widespread applications,it has obtained more importance.In the modern world,waste pollution... Object Detection is the task of localization and classification of objects in a video or image.In recent times,because of its widespread applications,it has obtained more importance.In the modern world,waste pollution is one significant environmental problem.The prominence of recycling is known very well for both ecological and economic reasons,and the industry needs higher efficiency.Waste object detection utilizing deep learning(DL)involves training a machine-learning method to classify and detect various types of waste in videos or images.This technology is utilized for several purposes recycling and sorting waste,enhancing waste management and reducing environmental pollution.Recent studies of automatic waste detection are difficult to compare because of the need for benchmarks and broadly accepted standards concerning the employed data andmetrics.Therefore,this study designs an Entropy-based Feature Fusion using Deep Learning forWasteObject Detection and Classification(EFFDL-WODC)algorithm.The presented EFFDL-WODC system inherits the concepts of feature fusion and DL techniques for the effectual recognition and classification of various kinds of waste objects.In the presented EFFDL-WODC system,two major procedures can be contained,such as waste object detection and waste object classification.For object detection,the EFFDL-WODC technique uses a YOLOv7 object detector with a fusionbased backbone network.In addition,entropy feature fusion-based models such as VGG-16,SqueezeNet,and NASNetmodels are used.Finally,the EFFDL-WODC technique uses a graph convolutional network(GCN)model performed for the classification of detected waste objects.The performance validation of the EFFDL-WODC approach was validated on the benchmark database.The comprehensive comparative results demonstrated the improved performance of the EFFDL-WODC technique over recent approaches. 展开更多
关键词 object detection object classification waste management deep learning feature fusion
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Part-level 3-D object classification with improved interpretation tree
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作者 邢薇薇 刘渭滨 袁保宗 《Journal of Southeast University(English Edition)》 EI CAS 2007年第2期221-225,共5页
For classifying unknown 3-D objects into a set of predetermined object classes, a part-level object classification method based on the improved interpretation tree is presented. The part-level representation is implem... For classifying unknown 3-D objects into a set of predetermined object classes, a part-level object classification method based on the improved interpretation tree is presented. The part-level representation is implemented, which enables a more compact shape description of 3-D objects. The proposed classification method consists of two key processing stages: the improved constrained search on an interpretation tree and the following shape similarity measure computation. By the classification method, both whole match and partial match with shape similarity ranks are achieved; especially, focus match can be accomplished, where different key parts may be labeled and all the matched models containing corresponding key parts may be obtained. A series of experiments show the effectiveness of the presented 3-D object classification method. 展开更多
关键词 3-D object classification shape match similarity measure interpretation tree
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Object oriented land cover classification using ALS and GeoEye imagery over mining area 被引量:6
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作者 YU Hai-yang, CHENG Gang, GE Xiao-san, LU Xiao-ping Key Laboratory of Mine Spatial Information Technologies of State Bureau of Surveying and Mapping, Henan Polytechnic University, Jiaozuo 454000, China 《中国有色金属学会会刊:英文版》 CSCD 2011年第S3期733-737,共5页
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, ... 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. 展开更多
关键词 AIRBORNE laser SCANNING GeoEye nDSM object oriented classification MINING areas
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Object-based forest gaps classification using airborne LiDAR data 被引量:4
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作者 Xuegang Mao Jiyu Hou 《Journal of Forestry Research》 SCIE CAS CSCD 2019年第2期617-627,共11页
Object-based classification differentiates forest gaps from canopies at large regional scale by using remote sensing data. To study the segmentation and classification processes of object-based forest gaps classificat... Object-based classification differentiates forest gaps from canopies at large regional scale by using remote sensing data. To study the segmentation and classification processes of object-based forest gaps classification at a regional scale, we sampled a natural secondary forest in northeast China at Maoershan Experimental Forest Farm.Airborne light detection and ranging(LiDAR; 3.7 points/m2) data were collected as the original data source and the canopy height model(CHM) and topographic dataset were extracted from the LiDAR data. The accuracy of objectbased forest gaps classification depends on previous segmentation. Thus our first step was to define 10 different scale parameters in CHM image segmentation. After image segmentation, the machine learning classification method was used to classify three kinds of object classes, namely,forest gaps, tree canopies, and others. The common support vector machine(SVM) classifier with the radial basis function kernel(RBF) was first adopted to test the effect of classification features(vegetation height features and some typical topographic features) on forest gap classification.Then the different classifiers(KNN, Bayes, decision tree,and SVM with linear kernel) were further adopted to compare the effect of classifiers on machine learning forest gaps classification. Segmentation accuracy and classification accuracy were evaluated by using Mo¨ller's method and confusion metrics, respectively. The scale parameter had a significant effect on object-based forest gap segmentation and classification. Classification accuracies at different scales revealed that there were two optimal scales(10 and 20) that provided similar accuracy, with the scale of 10 yielding slightly greater accuracy than 20. The accuracy of the classification by using combination of height features and SVM classifier with linear kernel was91% at the optimal scale parameter of 10, and it was highest comparing with other classification classifiers, such as SVM RBF(90%), Decision Tree(90%), Bayes(90%),or KNN(87%). The classifiers had no significant effect on forest gap classification, but the fewer parameters in the classifier equation and higher speed of operation probably lead to a higher accuracy of final classifications. Our results confirm that object-based classification can extract forest gaps at a large regional scale with appropriate classification features and classifiers using LiDAR data. We note, however, that final satisfaction of forest gap classification depends on the determination of optimal scale(s) of segmentation. 展开更多
关键词 FOREST GAP Scale segmentation classification FEATURE LIDAR CHM object based Machine learning
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Classification of underwater still objects based on multi-field features and SVM 被引量:4
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作者 TIAN Jie XUE Shan-hua HUANG Hai-ning ZHANG Chun-hua 《Journal of Marine Science and Application》 2007年第1期36-40,共5页
A Support Vector Machine is used as a classifier to the automatic detection and recognition of underwater still objects. Discrimination between the objects can be transferred into different projection spaces by the pr... A Support Vector Machine is used as a classifier to the automatic detection and recognition of underwater still objects. Discrimination between the objects can be transferred into different projection spaces by the process of multi-field feature extraction. The multi-field feature vector includes time-domain, spectral, time-frequency distribution and bi-spectral features. Underwater target recognition can be considered as a problem of small sample recognition. SVM algorithm is appropriate to this kind of problems because of its outstanding generalizability. The SVM is contrasted with a Gaussian classifier and a k-nearest classifier in some experiments using real data of lake or sea trial. The experimental results indicate that SVM is better than the others two. 展开更多
关键词 underwater still objects classification feature support vector machine (SVM)
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Lightning Search Algorithm with Deep Transfer Learning-Based Vehicle Classification
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作者 Mrim M.Alnfiai 《Computers, Materials & Continua》 SCIE EI 2023年第3期6505-6521,共17页
There is a drastic increase experienced in the production of vehicles in recent years across the globe.In this scenario,vehicle classification system plays a vital part in designing Intelligent Transportation Systems(... There is a drastic increase experienced in the production of vehicles in recent years across the globe.In this scenario,vehicle classification system plays a vital part in designing Intelligent Transportation Systems(ITS)for automatic highway toll collection,autonomous driving,and traffic management.Recently,computer vision and pattern recognition models are useful in designing effective vehicle classification systems.But these models are trained using a small number of hand-engineered features derived fromsmall datasets.So,such models cannot be applied for real-time road traffic conditions.Recent developments in Deep Learning(DL)-enabled vehicle classification models are highly helpful in resolving the issues that exist in traditional models.In this background,the current study develops a Lightning Search Algorithm with Deep Transfer Learning-based Vehicle Classification Model for ITS,named LSADTL-VCITS model.The key objective of the presented LSADTL-VCITS model is to automatically detect and classify the types of vehicles.To accomplish this,the presented LSADTL-VCITS model initially employs You Only Look Once(YOLO)-v5 object detector with Capsule Network(CapsNet)as baseline model.In addition,the proposed LSADTL-VCITS model applies LSA with Multilayer Perceptron(MLP)for detection and classification of the vehicles.The performance of the proposed LSADTL-VCITS model was experimentally validated using benchmark dataset and the outcomes were examined under several measures.The experimental outcomes established the superiority of the proposed LSADTL-VCITS model compared to existing approaches. 展开更多
关键词 Intelligent transportation system object detection vehicle classification deep learning machine learning
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High efficient moving object extraction and classification in traffic video surveillance 被引量:1
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作者 Li Zhihua Zhou Fan Tian Xiang Chen Yaowu 《Journal of Systems Engineering and Electronics》 SCIE EI CSCD 2009年第4期858-868,共11页
Moving object extraction and classification are important problems in automated video surveillance systems. A background model based on region segmentation is proposed. An adaptive single Gaussian background model is ... Moving object extraction and classification are important problems in automated video surveillance systems. A background model based on region segmentation is proposed. An adaptive single Gaussian background model is used in the stable region with gradual changes, and a nonparametric model is used in the variable region with jumping changes. A generalized agglomerative scheme is used to merge the pixels in the variable region and fill in the small interspaces. A two-threshold sequential algorithmic scheme is used to group the background samples of the variable region into distinct Gaussian distributions to accelerate the kernel density computation speed of the nonparametric model. In the feature-based object classification phase, the surveillance scene is first partitioned according to the road boundaries of different traffic directions and then re-segmented according to their scene localities. The method improves the discriminability of the features in each partition. AdaBoost method is applied to evaluate the relative importance of the features in each partition respectively and distinguish whether an object is a vehicle, a single human, a human group, or a bike. Experimental results show that the proposed method achieves higher performance in comparison with the existing method. 展开更多
关键词 background model nonparametric model adaptive single Gaussian model object classification
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Applications of Hyperspectral Remote Sensing in Ground Object Identification and Classification 被引量:1
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作者 Yu Wei Xicun Zhu +4 位作者 Cheng Li Xiaoyan Guo Xinyang Yu Chunyan Chang Houxing Sun 《Advances in Remote Sensing》 2017年第3期201-211,共11页
Hyperspectral remote sensing has become one of the research frontiers in ground object identification and classification. On the basis of reviewing the application of hyperspectral remote sensing in identification and... Hyperspectral remote sensing has become one of the research frontiers in ground object identification and classification. On the basis of reviewing the application of hyperspectral remote sensing in identification and classification of ground objects at home and abroad. The research results of identification and classification of forest tree species, grassland and urban land features were summarized. Then the researches of classification methods were summarized. Finally the prospects of hyperspectral remote sensing in ground object identification and classification were prospected. 展开更多
关键词 HYPERSPECTRAL REMOTE Sensing GROUND object Identification and classification STATISTICAL Model Spectral MATCHING
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Classification of Object Tracking Techniques in Wireless Sensor Networks 被引量:1
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作者 Mohsin Fayyaz 《Wireless Sensor Network》 2011年第4期121-124,共4页
Object tracking is one of the killer applications for wireless sensor networks (WSN) in which the network of wireless sensors is assigned the task of tracking a particular object. The network employs the object tracki... Object tracking is one of the killer applications for wireless sensor networks (WSN) in which the network of wireless sensors is assigned the task of tracking a particular object. The network employs the object tracking techniques to continuously report the position of the object in terms of Cartesian coordinates to a sink node or to a central base station. A family tree of object tracking techniques has been prepared.In this paper we have summarized the object tracking techniques available so far in wireless sensor networks. 展开更多
关键词 object TRACKING in WSN classification of object TRACKING TECHNIQUES in WSN
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Classification and Extraction of Urban Land-Use Information from High-Resolution Image Based on Object Multi-features 被引量:7
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作者 孔春芳 徐凯 吴冲龙 《Journal of China University of Geosciences》 SCIE CSCD 2006年第2期151-157,共7页
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 noti... 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. 展开更多
关键词 urban land-use multi-features object-oriented SEGMENTATION classification extraction.
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Performance of Object Classification Using Zernike Moment
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作者 Ariffuddin Joret Mohammad Faiz Liew Abdullah +2 位作者 Muhammad Suhaimi Sulong Asmarashid Ponniran Siti Zuraidah Zainudin 《Journal of Electronic Science and Technology》 CAS 2014年第1期90-94,共5页
Moments have been used in all sorts of object classification systems based on image. There are lots of moments studied by many researchers in the area of object classification and one of the most preference moments is... Moments have been used in all sorts of object classification systems based on image. There are lots of moments studied by many researchers in the area of object classification and one of the most preference moments is the Zernike moment. In this paper, the performance of object classification using the Zernike moment has been explored. The classifier based on neural networks has been used in this study. The results indicate the best performance in identifying the aggregate is at 91.4% with a ten orders of the Zernike moment. This encouraging result has shown that the Zernike moment is a suitable moment to be used as a feature of object classification systems. 展开更多
关键词 Features extraction neural network object classification Zernike moment.
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Information-theoretic Measures for Objective Evaluation of Classifications 被引量:1
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作者 HU Bao-Gang HE Ran YUAN Xiao-Tong 《自动化学报》 EI CSCD 北大核心 2012年第7期1169-1182,共14页
关键词 评价技术 信息论 分类 ITMS 错误类型 性能指标 离子阱质谱 信息理论
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IMAGE RECONSTRUCTION AND OBJECT CLASSIFICATION IN CT IMAGING SYSTEM
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作者 张晓明 蒋大真 卢宋林 《Nuclear Science and Techniques》 SCIE CAS CSCD 1995年第2期108-112,共5页
By obtaining a feasible filter function,reconstructed images can be got with linear interpolation and liftered backprojection techniques.Considering the gray and spstial correlation neighbour informations of each pixe... By obtaining a feasible filter function,reconstructed images can be got with linear interpolation and liftered backprojection techniques.Considering the gray and spstial correlation neighbour informations of each pixel,a new supervised classification method is put forward for the reconstructed images,and an experiment with noise image is done,the result shows that the method is feasible and accurate compared with ideal phantoms. 展开更多
关键词 Filter function Backprojection Image reconstruction Fuzzy clustering object classification
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Deep Reinforcement Learning Enabled Smart City Recycling Waste Object Classification
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作者 Mesfer Al Duhayyim Taiseer Abdalla Elfadil Eisa +5 位作者 Fahd NAl-Wesabi Abdelzahir Abdelmaboud Manar Ahmed Hamza Abu Sarwar Zamani Mohammed Rizwanullah Radwa Marzouk 《Computers, Materials & Continua》 SCIE EI 2022年第6期5699-5715,共17页
The Smart City concept revolves around gathering real time data from citizen,personal vehicle,public transports,building,and other urban infrastructures like power grid and waste disposal system.The understandings obt... The Smart City concept revolves around gathering real time data from citizen,personal vehicle,public transports,building,and other urban infrastructures like power grid and waste disposal system.The understandings obtained from the data can assist municipal authorities handle assets and services effectually.At the same time,the massive increase in environmental pollution and degradation leads to ecological imbalance is a hot research topic.Besides,the progressive development of smart cities over the globe requires the design of intelligent waste management systems to properly categorize the waste depending upon the nature of biodegradability.Few of the commonly available wastes are paper,paper boxes,food,glass,etc.In order to classify the waste objects,computer vision based solutions are cost effective to separate out the waste from the huge dump of garbage and trash.Due to the recent developments of deep learning(DL)and deep reinforcement learning(DRL),waste object classification becomes possible by the identification and detection of wastes.In this aspect,this paper designs an intelligence DRL based recycling waste object detection and classification(IDRL-RWODC)model for smart cities.The goal of the IDRLRWODC technique is to detect and classify waste objects using the DL and DRL techniques.The IDRL-RWODC technique encompasses a twostage process namely Mask Regional Convolutional Neural Network(Mask RCNN)based object detection and DRL based object classification.In addition,DenseNet model is applied as a baseline model for the Mask RCNN model,and a deep Q-learning network(DQLN)is employed as a classifier.Moreover,a dragonfly algorithm(DFA)based hyperparameter optimizer is derived for improving the efficiency of the DenseNet model.In order to ensure the enhanced waste classification performance of the IDRL-RWODC technique,a series of simulations take place on benchmark dataset and the experimental results pointed out the better performance over the recent techniques with maximal accuracy of 0.993. 展开更多
关键词 Smart cities deep reinforcement learning computer vision image classification object detection waste management
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Object-Based vs. Pixel-Based Classification of Mangrove Forest Mapping in Vien An Dong Commune, Ngoc Hien District, Ca Mau Province Using VNREDSat-1 Images 被引量:1
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作者 Nguyen Thi Quynh Trang Le Quang Toan +2 位作者 Tong Thi Huyen Ai Nguyen Vu Giang Pham Viet Hoa 《Advances in Remote Sensing》 2016年第4期284-295,共12页
Many researches have been performed comparing object-based classification (OBC) and pixel-based classification (PBC), particularly in classifying high-resolution satellite images. VNREDSat-1 is the first optical remot... Many researches have been performed comparing object-based classification (OBC) and pixel-based classification (PBC), particularly in classifying high-resolution satellite images. VNREDSat-1 is the first optical remote sensing satellite of Vietnam with resolution of 2.5 m (Panchromatic) and 10 m (Multispectral). The objective of this research is to compare two classification approaches using VNREDSat-1 image for mapping mangrove forest in Vien An Dong commune, Ngoc Hien district, Ca Mau province. ISODATA algorithm (in PBC method) and membership function classifier (in OBC method) were chosen to classify the same image. The results show that the overall accuracies of OBC and PBC are 73% and 62.16% respectively, and OBC solved the “salt and pepper” which is the main issue of PBC as well. Therefore, OBC is supposed to be the better approach to classify VNREDSat-1 for mapping mangrove forest in Ngoc Hien commune. 展开更多
关键词 object-Based classification Pixel-Based classification VNREDSat-1 Mangrove Forest Ca Mau
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3D Object Recognition by Classification Using Neural Networks
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作者 Mostafa Elhachloufi Ahmed El Oirrak +1 位作者 Aboutajdine Driss M. Najib Kaddioui Mohamed 《Journal of Software Engineering and Applications》 2011年第5期306-310,共5页
In this Paper, a classification method based on neural networks is presented for recognition of 3D objects. Indeed, the objective of this paper is to classify an object query against objects in a database, which leads... In this Paper, a classification method based on neural networks is presented for recognition of 3D objects. Indeed, the objective of this paper is to classify an object query against objects in a database, which leads to recognition of the former. 3D objects of this database are transformations of other objects by one element of the overall transformation. The set of transformations considered in this work is the general affine group. 展开更多
关键词 RECOGNITION classification 3D object NEURAL Network AFFINE TRANSFORMATION
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ORDERED-OBJECT-ORIENTED METHOD:A NEW APPROACH OF SAMPLE PART CALCULATION AND DESIGN
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作者 李蓓智 《Journal of China Textile University(English Edition)》 EI CAS 1997年第1期6-11,共6页
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 ... 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. 展开更多
关键词 part classification NEURAL networks fuzzy CLUSTERING algorithm pattern recognition object-oriented
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Objective Synoptic Weather Classification on Air Pollution during Winter Seasons in Hangzhou
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作者 Jiaqi Zhao Chenggang Wang 《Journal of Atmospheric Science Research》 2021年第3期1-13,共13页
Using the 2015-2018 Hangzhou city PM2.5,PM10,SO2,CO,NO2 and O3 mass concentration data,ERA5 reanalysis data and ground observation data,through the PCT classification method,the objective analysis of the winter air po... Using the 2015-2018 Hangzhou city PM2.5,PM10,SO2,CO,NO2 and O3 mass concentration data,ERA5 reanalysis data and ground observation data,through the PCT classification method,the objective analysis of the winter air pollution weather situation in Hangzhou was obtained.The results showed that the winter air quality concentration in Hangzhou continued to be high from 2015 to 2018,and the air pollution was the most significant.Through objective classification,it is concluded that the main weather conditions affecting the region in winter are divided into 6 types,namely high pressure control,high pressure bottom control equalizing field,L-shaped high pressure control,high pressure front control equalizing field,low pressure control,low pressure front control Equalizing field.Among them,when high pressure control,high pressure bottom control equalizing field,L high pressure control,low pressure control are affected by local sources,the impact of external sources has a greater impact on the air quality in Hangzhou,and air pollution is prone to occur;before low pressure When the pressure equalization field is controlled by the Ministry and the pressure equalization field is controlled by the high pressure front,the local wind and precipitation in Hangzhou are relatively high,which is not conducive to the accumulation of air pollutants.The probability of occurrence of air pollution is small,and air pollution is not easy to occur. 展开更多
关键词 PM2.5 PCT objective weather classification
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Object-Based Classification of Urban Distinct Sub-Elements Using High Spatial Resolution Orthoimages and DSM Layers
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作者 Ali Nouh Mabdeh A'kif Al-Fugara Mu’men Al jarah 《Journal of Geographic Information System》 2018年第4期323-343,共21页
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... 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. 展开更多
关键词 object-oriented classification Real Time KINEMATICS DSM UAV Orthoimages MOSAIC URBAN DISTINCT Sub-Elements
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