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作者： Stijn Wijdeven Wageningen University and Research

学位级别：硕士

There is need for a standardized method for automated tree crown identification. In this study, two methods are analysed to identify their best practices and to develop a new, hybrid method. Method one is based on a Digital Surface Model (DSM), which determines the presence of trees based on their height compared to their neighbouring pixels. Method two is based on object-based image analysis (OBIA), which determines the presence of trees based on pixel characteristics of high resolution aerial images. based on the best practices of both methods the hybrid method is created with the aim to improve tree crown identification accuracy. The results of the DSM-, OBIA- and hybrid methods are compared to a validation dataset by their correct number of crowns counted and crown area size. This is done for single solitary trees, for trees standing in rows and for trees as part of a group. For identifying individual tree crowns, the results show that single trees are more easily identified than trees in rows and groups. Furthermore, the performances of both the DSM-method and OBIA-method increase when using input data of a higher resolution. based on the results it is not possible to conclude that the hybrid method is an overall improvement compared to the DSM- and OBIA-methods. Rather, it depends on what somebody wants to achieve with the crown identification method to determine what method is most suitable. The hybrid method has proven to be generally more useful for crown area estimation, but is relatively less accurate for crown count estimation.

关键词： Tree crown identification crown delineation Digital Surface Models DSM Airborne Laser Scanning LiDAR OBIA object-based image analysis Remote Sensing pointclouds Boomregister AHN

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URBAN object EXTRACTION from DIGITAL SURFACE MODEL and DIGITAL AERIAL imageS 22

URBAN OBJECT EXTRACTION from DIGITAL SURFACE MODEL and DIGIT...

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22nd Congress of the International Society for Photogrammetry and Remote Sensing: Imaging a Sustainable Future, ISPRS 2012

作者： Grigillo, D. Kanjir, U. Faculty of Civil and Geodetic Engineering University of Ljubljana Jamova cesta 2 Ljubljana Slovenia Institute of Anthropological and Spatial Studies ZRC SAZU Novi trg 2 Ljubljana Slovenia

The paper describes two different methods for extraction of two types of urban objects from lidar digital surface model (DSM) and digital aerial images. Within the preprocessing digital terrain model (DTM) and orthoimages for three test areas were generated from aerial images using automatic photogrammetric methods. Automatic building extraction was done using DSM and multispectral orthoimages. First, initial building mask was created from the normalized digital surface model (nDSM), then vegetation was eliminated from the building mask using multispectral orthoimages. The final building mask was produced employing several morphological operations and buildings were vectorised using Hough transform. Automatic extraction of other green urban features (trees and natural ground) started from orthoimages using iterative object-based classification. This method required careful selection of segmentation parameters;in addition to basic spectral bands also information from nDSM was included. After the segmentation of images the segments were classified based on their attributes (spatial, spectral, geometrical, texture) using rule set classificator. First iteration focused on visible (i.e. unshaded) urban features, and second iteration on objects in deep shade. Results from both iterations were merged into appropriate classes. Evaluation of the final results (completeness, correctness and quality) was carried out on a per-area level and on a per-object level by ISPRS Commission III, WG III/4.

关键词： Building extraction Digital surface model object classification object-based image analysis Segmentation

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object based BUILDING EXTRACTION and BUILDING PERIOD ESTIMATION from UNMANNED AERIAL VEHICLE DATA

OBJECT BASED BUILDING EXTRACTION and BUILDING PERIOD ESTIMAT...

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2018 ISPRS TC III Mid-Term Symposium on Developments, Technologies and Applications in Remote Sensing

作者： Comert, Resul Kaplan, Onur Department of Geomatics Engineering Faculty of Engineering and Natural Sciences Gumushane University Gumushane Turkey Earth and Space Sciences Institute Anadolu University Eskisehir26555 Turkey

The aim of this study is to examine whether it is possible to estimate the building periods with respect to the building heights in the urban scale seismic performance assessment studies by using the building height retrieved from the unmanned aerial vehicle (UAV) data. For this purpose, a small area, which includes eight residential reinforced concrete buildings, was selected in Eskisehir (Turkey) city center. In this paper, the possibilities of obtaining the building heights that are used in the estimation of building periods from UAV based data, have been investigated. The investigations were carried out in 3 stages;(i) Building boundary extraction with object based image analysis (OBIA), (ii) height calculation for buildings of interest from nDSM and accuracy assessment with the terrestrial survey. (iii) Estimation of building period using height information. The average difference between the periods estimated according to the heights obtained from field measurements and from the UAV data is 2.86 % and the maximum difference is 13.2 %. Results of this study have shown that the building heights retrieved from the UAV data can be used in the building period estimation in the urban scale vulnerability assessments. © Authors 2018.

关键词： Building Height Building Period object-based image analysis UAV

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Advanced Techniques for object-based Classification of Hyperspectral Remote Sensing

Advanced Techniques for Object-Based Classification of Hyper...

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作者： SHAH,MAQSOOD 南京理工大学

学位级别：硕士

Land-Use Land-Cover(LULC)mapping is an important issue in agriculture *** demand for improved and more accurate LULC mapping has led to a methodology known as object-based image analysis(OBIA).The focus of this thesis is about the design and implementation of new methods for object-based(OB)classification of hyperspectral remote sensing *** earth observation instruments collect these ***,in this thesis we investigate the integration of methods for object-based hyperspectral classification in synergic *** images mapping is a crucial step in environmental monitoring,disaster management,agriculture management and military *** recent researches to improved and accurate hyperspectral mapping have led to key methodology known as object-based classification of remotely sensed *** core intention of the OB images analysis is to group-of-pixels named "objects" through segmentation into a specific ***-oriented image analysis segments the data and constructs hierarchical network of homogeneous ***-based enable the analysis of aggregated sets of pixels,exploit shape-related variation,as well as spectral *** pixel-based classification methods always produce mixed pixel problems due to the independency of neighbor *** problem motivates researchers to combine segmentation,color and many other parameters to illuminate the wrongly classified or mixed pixels into their relevant *** the new methodology solved problems and improved accuracy,but it also raises new challenges such as the loss of accuracy in terms of less abundant,but potentially *** key challenges in hyperspectral images classification techniques are the high dimensionality of data,limited number of training samples,and combination of spatial and spectral *** recent years,availability of new remotely sensed data with high dimensions,spectral and spatial resolution rese

关键词： Land-use Land-Cover object-based image analysis support vector machines object-based Multi-resolution segmentation Rotation-based object-oriented principle component analysis rotation-matrix

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Análise da qualidade da informação produzida por classificação baseada em orientação a objeto e SVM visando a estimativa do volume do reservatório Jaguari-Jacareí

Análise da qualidade da informação produzida por classifi...

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作者： Leão Junior, Emerson Universidade Estadual Paulista

Considerando o cenário durante a crise hídrica de 2014 e a situação crítica dos reservatórios do sistema Cantareira no estado de São Paulo, este estudo realizado no reservatório Jaguari-Jacareí, consistiu na extração de informações a partir de imagens multiespectrais e análise da qualidade da informação relacionada com a acurácia no cálculo do volume de água do reservatório. Inicialmente, a superfície do espelho d’água foi obtida pela classificação da cobertura da terra a partir de imagens multiespectrais RapidEye tomadas antes e durante a crise hídrica (2013 e 2014, respectivamente), utilizando duas abordagens distintas: classificação orientada a objeto (object-based image analysis - OBIA) e classificação baseada em pixel (Support Vector Machine – SVM). A acurácia do usuário por classe permitiu expressar o erro para detectar a superfície do espelho d’água para cada abordagem de classificação de 2013 e 2014. O segundo componente da estimação do volume foi a representação do relevo submerso, que considerou duas fontes de dados na construção do modelo numérico do terreno (MNT): dados topográficos provenientes de levantamento batimétrico disponibilizado pela Sabesp e o modelo de superfície AW3D30 (ALOS World 3D 30m mesh), para complementar a informação não disponível além da cota 830,13 metros. A comparação entre as duas abordagens de classificação dos tipos de cobertura da terra do entorno do reservatório Jaguari-Jacareí mostrou que SVM resultou em indicadores de acurácia ligeiramente superiores à OBIA, para os anos de 2013 e 2014. Em relação à estimação de volume do reservatório, incorporando a informação do nível de água divulgado pela Sabesp, a abordagem SVM apresentou menor discrepância relativa do que OBIA. Apesar disso, a qualidade da informação produzida na estimação de volume, resultante da propagação da variância associada aos dados envolvidos no processo, ambas as abordagens produziram valores similares de incerteza, mas com uma sutil superioridade de OBIA,

关键词： Classificação de imagens Máquina de vetor de suporte Análise orientada a objeto Qualidade de dados espaciais image classification Support vector machine object-based image analysis Spatial data quality Dissertação de mestrado

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Extracting Municipal Solid Waste Dumps based on High Resolution images

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SPECTROSCOPY AND SPECTRAL analysis 2013年第8期33卷 2024-2030页

作者： Zhang Fang-li Du Shi-hong Guo Zhou Peking Univ Inst Remote Sensing Beijing 100871 Peoples R China Peking Univ GIS Beijing 100871 Peoples R China

The dramatically increasing informal MSW dumps are endangering the urban environment. Remote sensing (RS) technologies are more efficient to monitor and manage municipal solid wastes (MSW) than traditional survey-based methods. In high spatial resolution remotely sensed images, these irregularly distributed dumps have complex compositions and strong heterogeneities, thus it is still hard to extract them automatically no matter the pixel-or object-based image analysis method is used. Therefore, based on the analysis of MSW characteristics, the present study develops a multiresolution strategy to extract MSW dumps by combining image features at both high resolution and resampled lowheterogeneity images, while the high resolution images can provide detailed information and the low resolution images can suppress the strong heterogeneities of informal MSW dumps. Taking the QuickBird image covering part of Beijing as an example, this multi-resolution strategy produced a high accuracy (75%), indicating that this multi-resolution strategy is quite effective for extracting the open-air informal MSW dumps.

关键词： Municipal solid waste Highresolution remote sensing Multi-resolution object-based image analysis

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Rapid Invasion of Spartina alterniflora in the Coastal Zone of Mainland China: New Observations from Landsat OLI images

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REMOTE SENSING 2018年第12期10卷 1933-1933页

作者： Liu, Mingyue Mao, Dehua Wang, Zongming Li, Lin Man, Weidong Jia, Mingming Ren, Chunying Zhang, Yuanzhi Chinese Acad Sci Northeast Inst Geog & Agroecol Key Lab Wetland Ecol & Environm Changchun 130102 Jilin Peoples R China North China Univ Sci & Technol Coll Min Engn Tangshan 063210 Peoples R China Indiana Univ Purdue Univ Dept Earth Sci Indianapolis IN 46202 USA Chinese Univ Hong Kong Ctr Housing Innovat Shatin Hong Kong Peoples R China Chinese Acad Sci Key Lab Lunar Sci & Deep Explorat Natl Astron Observ Beijing 100101 Peoples R China

Plant invasion imposes significant threats to biodiversity and ecosystem function. Thus, monitoring the spatial pattern of invasive plants is vital for effective ecosystem management. Spartina alterniflora (S. alterniflora) has been one of the most prevalent invasive plants along the China coast, and its spread has had severe ecological consequences. Here, we provide new observation from Landsat operational land imager (OLI) images. Specifically, 43 Landsat-8 OLI images from 2014 to 2016, a combination of object-based image analysis (OBIA) and support vector machine (SVM) methods, and field surveys covering the whole coast were used to construct an up-to-date dataset for 2015 and investigate the spatial variability of S. alterniflora in the coastal zone of mainland China. The classification results achieved good estimation, with a kappa coefficient of 0.86 and 96% overall accuracy. Our results revealed that there was approximately 545.80 km(2) of S. alterniflora distributed in the coastal zone of mainland China in 2015, from Hebei to Guangxi provinces. Nearly 92% of the total area of S. alterniflora was distributed within four provinces: Jiangsu, Shanghai, Zhejiang, and Fujian. Seven national nature reserves invaded by S. alterniflora encompassed approximately one-third (174.35 km(2)) of the total area of S. alterniflora over mainland China. The Yancheng National Nature Reserve exhibited the largest area of S. alterniflora (115.62 km(2)) among the reserves. Given the rapid and extensive expansion of S. alterniflora in the 40 years since its introduction and its various ecological effects, geospatially varied responding decisions are needed to promote sustainable coastal ecosystems.

关键词： invasive plants Spartina alterniflora CAS S alterniflora object-based image analysis Landsat OLI

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object Oriented Classification for Mapping Mixed and Pure Forest Stands Using Very-High Resolution imagery

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REMOTE SENSING 2021年第13期13卷 2508-2508页

作者： Oreti, Loredana Giuliarelli, Diego Tomao, Antonio Barbati, Anna Univ Tuscia Dept Innovat Biol Agrofood & Forestry Syst DIBAF Via San Camillo Lellis I-01100 Viterbo Italy Council Agr Res & Econ Res Ctr Forestry & Wood Viale S Margherita 80 I-52100 Arezzo Italy

The importance of mixed forests is increasingly recognized on a scientific level, due to their greater productivity and efficiency in resource use, compared to pure stands. However, a reliable quantification of the actual spatial extent of mixed stands on a fine spatial scale is still lacking. Indeed, classification and mapping of mixed populations, especially with semi-automatic procedures, has been a challenging issue up to date. The main objective of this study is to evaluate the potential of object-based image analysis (OBIA) and Very-High-Resolution imagery (VHR) to detect and map mixed forests of broadleaves and coniferous trees with a Minimum Mapping Unit (MMU) of 500 m(2). This study evaluates segmentation-based classification paired with non-parametric method K- nearest-neighbors (K-NN), trained with a dataset independent from the validation one. The forest area mapped as mixed forest canopies in the study area amounts to 11%, with an overall accuracy being equal to 85% and K of 0.78. Better levels of user and producer accuracies (85-93%) are reached in conifer and broadleaved dominated stands. The study findings demonstrate that the very high resolution images (0.20 m of spatial resolutions) can be reliably used to detect the fine-grained pattern of rare mixed forests, thus supporting the monitoring and management of forest resources also on fine spatial scales.

关键词： mixed forests very-high-resolution imagery object-based image analysis multiresolution segmentation semi-automatic classification forest mapping Italy

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Comparative assessment of methods for estimating tree canopy cover across a rural-to-urban gradient in the mid-Atlantic region of the USA

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ENVIRONMENTAL MONITORING AND ASSESSMENT 2016年第5期188卷 297-297页

作者： Riemann, Rachel Liknes, Greg O'Neil-Dunne, Jarlath Toney, Chris Lister, Tonya US Forest Serv Forest Inventory & Anal USDA No Res Stn 425 Jordan Rd Troy NY 12180 USA US Forest Serv Forest Inventory & Anal USDA No Res Stn 1992 Folwell Ave St Paul MN 55108 USA Univ Vermont Spatial Anal Lab George D Aiken Ctr 205 Burlington VT 05405 USA US Forest Serv USDA Rocky Mt Res Stn 5775 US West Highway 10 Missoula MT 59808 USA US Forest Serv Forest Inventory & Anal USDA No Res Stn 11 Campus BlvdSuite 200 Newtown Sq PA 19073 USA

Tree canopy cover significantly affects human and wildlife habitats, local hydrology, carbon cycles, fire behavior, and ecosystem services of all types. In addition, changes in tree canopy cover are both indicators and consequences of a wide variety of disturbances from urban development to climate change. There is growing demand for this information nationwide and across all land uses. The extensive inventory plot system managed by the USDA Forest Service Forest Inventory and analysis (FIA) offers a unique opportunity for acquiring unbiased tree canopy cover information across broad areas. However, the estimates it produces had not yet been examined for comparative accuracy with other sources. In this study, we compared four different methods readily available and with significant potential for application over broad areas. The first two, field-collected and photointerpreted, are currently acquired by FIA on approximately 44,000 plots annually nationwide. The third method is a stem-mapping approach that models tree canopy cover from variables regularly measured on forested plots and is efficient enough to calculate nationwide. The fourth is a Geographic-object-based image analysis (GEOBIA) approach that uses both high-resolution imagery and leaf-off LiDAR data and has reported very high accuracies and spatial detail at state-wide levels of application. Differences in the spatial and temporal resolution and coverage of these four datasets suggest that they could provide complementary information if their relationships could be better understood. Plot-and county-level estimates of tree canopy cover derived from each of the four data sources were compared for 11 counties in Maryland, Pennsylvania, and West Virginia across a range of urbanization levels. We found high levels of systematic agreement between field and photointerpreted, stem-mapped and field, photointerpreted and GEOBIA estimates. In several cases, the relationship changed with the level of tree canopy co

关键词： Tree canopy cover object-based image analysis Accuracy Comparative assessment Forest inventory

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Detailed Mapping of Lava and Ash Deposits at Indonesian Volcanoes by Means of VHR PlanetScope Change Detection

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REMOTE SENSING 2022年第5期14卷 1168-1168页

作者： Roesch, Moritz Plank, Simon Univ Wurzburg Dept Geog & Geol D-97074 Wurzburg Germany German Aerosp Ctr DLR German Remote Sensing Data Ctr DFD Muenchener Str 20 D-82234 Wessling Germany

Mapping of lava flows in unvegetated areas of active volcanoes using optical satellite data is challenging due to spectral similarities of volcanic deposits and the surrounding background. Using very high-resolution PlanetScope data, this study introduces a novel object-oriented classification approach for mapping lava flows in both vegetated and unvegetated areas during several eruptive phases of three Indonesian volcanoes (Karangetang 2018/2019, Agung 2017, Krakatau 2018/2019). For this, change detection analysis based on PlanetScope imagery for mapping loss of vegetation due to volcanic activity (e.g., lava flows) is combined with the analysis of changes in texture and brightness, with hydrological runoff modelling and with analysis of thermal anomalies derived from Sentinel-2 or Landsat-8. Qualitative comparison of the mapped lava flows showed good agreement with multispectral false color time series (Sentinel-2 and Landsat-8). Reports of the Global Volcanism Program support the findings, indicating the developed lava mapping approach produces valuable results for monitoring volcanic hazards. Despite the lack of bands in infrared wavelengths, PlanetScope proves beneficial for the assessment of risk and near-real-time monitoring of active volcanoes due to its high spatial (3 m) and temporal resolution (mapping of all subaerial volcanoes on a daily basis).

关键词： lava volcanoes PlanetScope change detection object-based image analysis

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