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object-oriented classification OF MULTISPECTRAL LANDSAT 7 SATELLITE IMAGES

METALURGIA INTERNATIONAL 2013年第4期18卷 206-215页

作者： Drobnjak, Sinisa Cirovic, Goran Sekulovic, Dragoljub Regodic, Miodrag Mil Geog Inst Republ Serbia Belgrade Serbia Univ Belgrade Coll Civil Engn & Geodesy Belgrade Serbia Univ Def Belgrade Minist Def Republ Serbia Belgrade Serbia

In this study, land cover types and degree of urbanization in Belgrade test area were analyzed on the basis of the classification results acquired using object-oriented image analysis approaches. LANDSAT 7 with 7 spectral bands was used to carry out the image classification and ground truth data were collected from the available maps and personal knowledge. object-oriented image analysis was evaluated through the eCognition software. During the implementation, several different sets of parameters were tested for image segmentation and the nearest neighbor method was used as the classifier. Outcome from the classification works show that the object-oriented approach gave very accurate results (including higher producer's and user's accuracy for most of the land cover classes) for extraction land cover classes and degree of urbanization from satellite images.

关键词： Remote Sensing Land Cover object-oriented classification LANDSAT 7

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object-oriented classification FOR ECOLOGICALLY SOUND LAND BASED ON HIGH-RESOLUTION IMAGES 38

OBJECT-ORIENTED CLASSIFICATION FOR ECOLOGICALLY SOUND LAND B...

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38th IEEE International Geoscience and Remote Sensing Symposium (IGARSS)

作者： Wang, Jing Zhang, Xiaoxiang Du, Yingkun Jia, Xue Lin, Yifan Wuhan Univ Sch Resource & Environm Sci Wuhan Hubei Peoples R China Hohai Univ Inst Geog Informat Sci & Engn Nanjing Jiangsu Peoples R China Peking Univ Coll Urban & Environm Beijing Peoples R China

ISBN: (纸本)9781538671504

Fine and fast classification for ecologically sound land use types is the perquisite for ecosystem management and protection. However, few studies focused on the fine classification of ecologically sound land use especially using high-resolution remote sensing data. Therefore, this paper is to test the feasibility and applicability of classifying ecologically sound land using object-oriented classification method based on high-resolution remote sensing images, Pleiades-1 satellite images in Dafeng County, Jiangsu Province. Results showed that the overall classification accuracy was 87.43%, the users' accuracies and producers' accuracies of classification results of seven ecologically sound land types were higher than 80.00%. The classification result was in agreement with the field survey results. In addition, the experiment also indicated the segmentation result based on multi-threshold algorithm was closer to the real features in the shape, size, figure and texture comparing with that based on quadtree or chessboard algorithm. Different land use types can easily and efficiently extracted based on their difference in features using the assign classification method. Our research provides data support for evaluating ecologically sound land and it also provides a relatively rapid, accurate and practical way for finely classifying ecologically sound land.

关键词： ecologically sound land object-oriented classification high-resolution image remote sensing assign classification

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object-oriented classification and application in land use classification using SPOT-5 PAN imagery

Object-oriented classification and application in land use c...

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IEEE International Geoscience and Remote Sensing Symposium

作者： Wang, ZY Wei, WX Zhao, SH Chen, XW Peking Univ Inst Remote Sensing & GIS Beijing 100871 Peoples R China

ISBN: (纸本)0780387422

High-resolution remotely sensed data have been actively employed in urban land use/cover. object-oriented classification techniques based on image segmentation are being actively Studied in the high-resolution image process and interpretation to extract a variety of thematic, information. Different from the pixel-based image analysis, the processing of the object-oriented method are based on image object or segment. not single pixel. The object-oriented classification includes two consecutive processes. An image is subdivided into separated regions according to the spectral and spatial heterogeneity in the image segmentation process. Then the objects are assigned to a specific class according to the class'S detailed description in the image classification process. As, a case study, the study area is a part of the planning Beijing Olympic Game,,, Cottage, which has changed greatly with the advent of the year of 2008. The panchromatic SPOT-5 image in August of 2002 is segmented and these segments then are classified to hierarchically, linked objects by the eCognition software. The overall classification accuracy is up to 87%.

关键词： object-oriented classification land use SPOT-5 imagery

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object-oriented classification of very high-resolution remote sensing imagery based on improved CSC and SVM

Object-oriented classification of very high-resolution remot...

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15th International Conference on Geoinformatics

作者： Li, Haitao Gu, Haiyan Han, Yanshun Yang, Jinghui Chinese Acad Surveying & Mapping Inst Photogrammetry & Remote Sensing Beijing 100039 Peoples R China Liaoning Univ Inst Surveying & Geog Sci Liaoning 123000 Peoples R China

ISBN: (纸本)9780819469120

We present a new object-oriented land cover classification method integrating raster analysis and vector analysis, which adopted improved Color Structure Code (CSC) for segmentation and Support Vector Machine (SVM) for classification using Very High Resolution (VHR) QuickBird data. It synthesized the advantage of digital image processing, Geographical Information System (GIS) (vector-based feature selection) and Data Mining (intelligent SVM classification) to interpret image from pixels to segments and then to thematic information. Compared with the pixel-based SVM classification in ENVI 4.3, both of the accuracy of land cover classification by the proposed method and the computational performance for classification were improved. Moreover, the land cover classification map can update GIS database in a quick and convenient way.

关键词： GIS Color Structure Code land cover classification object-oriented classification QuickBird Support Vector Machine

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object-oriented classification of very high resolution airborne imagery for the extraction of hedgerows and field margin cover in agricultural areas

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APPLIED GEOGRAPHY 2009年第2期29卷 145-157页

作者： Tansey, Kevin Chambers, Ian Anstee, Andrew Denniss, Anthony Lamb, Alistair Univ Leicester Dept Geog Leicester LE1 7RH Leics England Infoterra Ltd Leicester LE3 1UT Leics England

The recent advancement in technology for the airborne collection and subsequent processing and analysis of digital remotely sensed data has been significant. An investigation into the contributions that recent developments have made to the assessment and extraction of hedgerow and field margins is presented. Research into the active legislation by which hedgerows and agricultural biodiversity in England are bound is brought together in the concept of an ideal classification. Using Leica Geosystems ADS40 data, collected for Berkshire (UK), the degree to which the ideal classification can be reached is investigated, focusing on the extraction of hedgerows and unploughed field margin protective boundary strips as image objects. Elevation data were derived from the ADS40 imagery data to accompany true-colour and colour-infrared spectral information. By buffering the detected hedgerow objects in a GIS, we facilitated an investigation of the presence of measures that satisfy the minimum hedgerow protection requirements stipulated by the UK Department for Environment, Food and Rural Affairs (DEFRA). In our study area, it was estimated that 68% of areas that need to be classified as 'grassy hedgerow boundary strip' to satisfy the minimum requirements of the legislation were established with low-lying grassy cover before the introduction of the legislation in July 2005. The implications of this research for the development of automated classification methods for field scale agricultural mapping are discussed. (C) 2008 Elsevier Ltd. All rights reserved.

关键词： Very high resolution object-oriented classification Hedgerows Agriculture

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object-oriented classification of Rubber Plantations from Landsat Satellite Imagery 3

Object-Oriented Classification of Rubber Plantations from La...

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3rd International Conference on Agro-Geoinformatics (Agro-Geoinformatics)

作者： Dai, ShengPei Li, HaiLiang Luo, HongXia Li, MaoFen Fang, JiHua Wang, LingLing Cao, JianHua Luo, Wei Chinese Acad Trop Agr Sci ISTI CATAS Inst Sci & Tech Informat Danzhou Peoples R China Chinese Acad Trop Agr Sci RRI CATAS Rubber Res Inst Danzhou Peoples R China

ISBN: (纸本)9781479941575

Due to increasing global demand for natural rubber products, rubber (Hevea brasiliensis) plantation expansion has occurred in many regions where it was originally considered unsuitable. However, accurate maps of rubber plantations are not available, which substantially constrain our understanding of the environmental and socio-economic impacts of rubber plantation expansion. In this study, the rubber plantations was accurate mapped from Landsat satellite imagery based on object-oriented classification method in Yangjiang State Farm in Hainan Island in 2010. The results show that: (1) The rubber plantation area in Yangjiang State Farm was estimated at 5866 hm(2) in 2010, which was slightly higher than the stand inventory data (5190 hm(2)) in 2009. (2) The resulting rubber plantation map has a high accuracy according to the confusion matrix by using the ground truth ROIs. The overall accuracy is 90% and the kappa coefficient is 0.9. It showed that object-oriented classification method is suitable for mapping rubber plantation from Landsat satellite imagery.

关键词： object-oriented classification Rubber (Hevea brasiliensis) plantation Landsat Satellite Imagery Yangjiang State Farm Hainan Island

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

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Journal of Forestry Research 2022年第4期33卷 1377-1389页

作者： Zhao Sun Yifu Wang Lei Pan Yunhong Xie Bo Zhang Ruiting Liang Yujun Sun National Forestry and Grassland Administration Key Laboratory of Forest Resources and Environmental Management Beijing Forestry UniversityBeijing 100083People’s Republic of China

Pine wilt disease(PWD)is currently one of the main causes of large-scale forest *** control the spread of PWD,it is essential to detect affected pine trees *** study investigated the feasibility of using the object-oriented multi-scale segmentation algorithm to identify trees discolored by *** 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 ***,the optimal segmentation scale was implemented using the estimation of scale parameter(ESP2)*** feature space of the segmentation results was optimized,and appropriate features were selected for *** results showed that the optimal scale,shape,and compactness values of the tree crown segmentation algorithm were 56,0.5,and 0.8,*** producer’s accuracy(PA),user’s accuracy(UA),and F1 score were 0.722,0.605,and 0.658,*** 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 *** multi-scale segmentation and object-oriented classification method could accurately identify trees discolored by PWD with a straightforward and rapid *** study provides a technical method for monitoring the occurrence of PWD and identifying the discolored trees of disease using UAV-based high-resolution images.

关键词： object-oriented classification Multi-scale segmentation UAV images Pine wilt disease

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Graph-Based Feature Selection for object-oriented classification in VHR Airborne Imagery

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IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 2011年第1期49卷 353-365页

作者： Chen, Xi Fang, Tao Huo, Hong Li, Deren Shanghai Jiao Tong Univ Inst Image Proc & Pattern Recognit Automat Dept Shanghai 200240 Peoples R China Wuhan Univ State Key Lab Informat Engn Surveying Mapping & R Wuhan 430072 Peoples R China

Linearly nonseparability and class imbalance of very high resolution (VHR) imagery make feature selection for object-oriented classification quite challenging, while such characteristics, especially class imbalance, have usually been ignored in open literature. To cope with the challenges, this paper proposes a new graph-based feature selection method named locally weighted discriminating projection (LWDP). First, the popular graph-based criteria of feature selection are reformulated to present linear or nonlinear mapping in feature space. Second, weight matrices of graphs characterize dissimilarity rather than similarity between pairwise neighbors, to well-preserved local structure when the difference of distance between a sample and its neighbors is large. Finally, LWDP provides a new perspective to alleviate class imbalance at both global and local levels, by restricting the pairwise relationships in the weight matrices. Specifically, neighborhood unions are introduced to employ the local class distribution and class size to constrain pairwise relationships in the weight matrices when classifying unbalanced sample sets. To evaluate the performances of LWDP in low dimensions, a holistic scoring scheme is proposed to stress the performances under low dimensions. In addition, overall accuracy curves and Kappa Index of Agreement (KIA) curves, which exhibit KIA in dimensions, are also used. The experimental results show that LWDP and its kernel extension outperform the other classic or latest methods in processing unbalanced sample set of VHR airborne imagery.

关键词： Class imbalance feature selection linear nonseparability neighborhood unions object-oriented classification

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Changes Detection and object-oriented classification of Major Wetland Cover Types in Response to Driving Forces in Zoige County, Eastern Qinghai-Tibetan Plateau

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IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING 2021年 14卷 9297-9305页

作者： Zhang, Xuexia Fu, Shujing Hu, Zheyuan Zhou, Jinxing Zhang, Xue Beijing Forestry Univ Key Lab State Forestry Adm Soil & Water Conservat Beijing 100083 Peoples R China Beijing Forestry Univ Sch Soil & Water Conservat Beijing 100083 Peoples R China Shijiazhuang Univ Coll Resource & Environm Sci Shijiazhuang 050035 Hebei Peoples R China

The Zoige alpine wetland of eastern Qinghai-Tibetan Plateau is one of the most important wetlands in the world, which suffered stronger human regulations of decades' drainage and recent restoration. It is so fragile that great efforts should be made to strengthen wetland protection. Based on wetland type classification for seven periods by the object-oriented classification method, we quantitatively discussed dynamically changes of wetland cover types and their driving forces. Conclusions show that alpine meadows accounts for 44.59% of the whole area of the county, and the transition between alpine meadows and swamp meadows mostly occurs in the process of wetland landscape changes. Changes of wetland cover types have taken place in Zoige County from 1990 to 2018 in which wetland loss was mainly caused by the decrease of marsh areas during 1995-2005. Particularly, shift from alpine meadows to swamp meadows led to wetland slightly expansion in two periods, i.e., 1990-1995 and 2005-2018. There is a significant negative correlation between marsh area and average temperature in growing season, average wind speed and rural population, and a significant negative correlation between water area and maximum wind speed. The increase of air temperature and wind speed lead to an enhancement of surface evapotranspiration and a decrease of water retention capacity. Whereas, positive feedback of ecosystem lead to an increase in air temperature and a decrease of marsh area. Both climatic changes and human activities have caused changes in wetland cover type, especially temperature in growing season, wind speed, agricultural development, and animal husbandry.

关键词： Wetlands Rivers Image segmentation Remote sensing Lakes Wind speed Water conservation Driving factors object-oriented classification swamp meadow wetland cover types Zoige alpine wetland

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Detecting an Optimal Scale Parameter in object-oriented classification

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IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING 2011年第4期4卷 890-895页

作者： Lowe, Sarah Heather Guo, Xulin Univ Saskatchewan Dept Geog & Planning Saskatoon SK S7N 5C8 Canada

Avoiding spatial autocorrelation is the key to many research questions especially for field design, remote sensing data selection, and maximum spatial variation caption. Spatial variation across land cover types as well as the gradients inherent in ecotones can be captured in reflectance which is a spatially continuous variable. The spatial variation between reflectance values of any two pixels will depend on the lag distance beyond which pixels are no longer spatially autocorrelated. This paper demonstrates the utility of semivariogram for determining the lag distance in which pixels will be spatially autocorrelated. According to sampling theorem, objects should be sampled at half their width such that spatial resolution should be half of the semivariogram lag distance. As object-oriented classification is now the most broadly accepted classification method, scale parameter determination is the foremost important decision for determining the size of image objects. The scale parameter was adjusted during image segmentation to test how the size of image objects changed. The optimal scale parameter was chosen when the average distance between neighbouring image object centroids was near to the lag distance of the semivariogram. Results showed that the size of image objects reached a scaling threshold as the scale parameter was increased. When the scale parameter was adjusted to create image objects that exceeded this threshold, the segmentation was not able to accurately represent the spatial variation observed on the ground.

关键词： object-oriented classification scale parameter segmentation spatial autocorrelation

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