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作者： Muhammad Al-Amin Hoque University Of Queensland

学位级别：博士

Tropical cyclones are one of the most catastrophic natural hazards regularly affecting tropical coastal areas globally. The overall impacts of tropical cyclones can be minimized using a range of management approaches, in terms of: prevention/reduction, and preparedness. Accurate and detailed spatial information on the natural and built environment is required to support the development, implementation and evaluation of these cyclone management approaches. There is a lack of appropriate mapping approaches and spatial models at local scales (<1000 km 2 ) to derive this information. This thesis developed and evaluated mapping approaches and spatial models at the local scale (<1000 km 2 ) by integrating remote sensing, field data and spatial analysis for assessing, monitoring and managing tropical cyclone impacts. Three specific objectives were addressed: (1) developing and evaluating an approach for assessing the multiple impacts of tropical cyclones through object- based image analysis applied to moderate spatial resolution satellite imagery; (2) developing and testing a risk mapping approach for tropical cyclone impacts using an analytical hierarchy process (AHP) with data derived from remote sensing, field data and spatial analysis; and (3) producing and evaluating a simple risk modelling approach under present and future climate scenarios for tropical cyclone impacts using remote sensing, field data and spatial analysis. The developed mapping approaches and models were tested to justify the applicability in Sarankhola Upazila (151 km 2 ), a local government area in coastal Bangladesh. Cyclone Sidr (2007) was used as a case study for objective one in the study site. After introductory, review and research approach chapters, chapter four developed a method for mapping the multiple impacts of tropical cyclones. The object-based approach was used to map land cover types in pre- and post-cyclone Sidr (2007) SPOT-5 satellite image data. A post- classification comparison

关键词： Remote sensing tropical cyclone spatial analysis object-based image analysis impact assessment hazard vulnerability risk climate change

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An Automated Technique for Basemap Updating Using UAV Data

An Automated Technique for Basemap Updating Using UAV Data

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Joint Urban Remote Sensing Event (JURSE)

作者： Gevaert, Caroline Persello, Claudio Elberink, Sander Oude Vosselman, George Sliuzas, Richard Univ Twente Fac ITC Enschede Netherlands

ISBN: (纸本)9781509058082

The increased reliance on geospatial data for decision-making in urban planning makes it imperative that the available spatial information is up-to-date and faithfully represents reality. This calls for map updating methods which support the integration of data from different sources in an automated manner. In this paper, we utilize existing basemap information to provide the initial data labels, thus reducing the lengthy process of label acquisition. However, we take into account that a portion of these labels are likely to be incorrect due to changes such as new constructions. We then cast the updating problem as a supervised classification with noisy training labels. Through an iterative approach, training samples which rank low on two criteria (label consistency and contextual consistency) are considered to be unreliable and removed from the training set. This technique is demonstrated in the specific context in which data obtained from an Unmanned Aerial Vehicle (UAV) is used to update building outlines in an informal settlement in Kigali, Rwanda. The proposed approach is able to accurately classify 95.34% of the UAV imagery even though the original labels are based on data obtained from outdated aerial imagery of a lower spatial resolution, causing 14.3% of the segments to have an incorrect training label. In this paper, we describe the proposed method, demonstrate the importance of both the contextual consistency and label consistency for filtering the training samples, discuss the robustness of the method to noise levels, and discuss the implications of this approach for other applications.

关键词： Unmanned Aerial Vehicles (UAVs) map updating supervised classification urban planning object-based image analysis

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Evaluation of clustering algorithms for unsupervised change detection in VHR remote sensing imagery

Evaluation of clustering algorithms for unsupervised change ...

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Joint Urban Remote Sensing Event (JURSE)

作者： Leichtle, Tobias Geiss, Christian Wurm, Michael Lakes, Tobia Taubenboeck, Hannes Co Remote Sensing & Environm Res SLU D-81243 Munich Germany German Aerosp Ctr DLR German Remote Sensing Data Ctr DFD D-82234 Wessling Germany Humboldt Univ Geog Dept D-12489 Berlin Germany

ISBN: (纸本)9781509058082

Remote sensing has proven to be an adequate tool for observation of changes to the Earth's surface. Especially modern space-borne sensors with very-high spatial resolution offer new capabilities for monitoring of dynamic urban environments. In this context, clustering is a well suited technique for unsupervised and thus highly automatic detection of changes. In this study, seven partitioning clustering algorithms from different methodological categories are evaluated regarding their suitability for unsupervised change detection. In addition, object-based feature sets of different characteristics are included in the analysis assessing their discriminative power for classification of changed against unchanged buildings. In general, the most important property of favorable algorithms is that they do not require additional arbitrary input parameters except the number of clusters. Best results were achieved based on the clustering algorithms k-means, partitioning around medoids, genetic k-means and self-organizing map clustering with accuracies in terms of kappa statistics of 0.8 to 0.9 and beyond.

关键词： change detection clustering object-based image analysis very-high resolution (VHR) remote sensing

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Integrating Semi-Supervised Learning and Expert System for wetland vegetation classification using Sentinel-2 data

Integrating Semi-Supervised Learning and Expert System for w...

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作者： NASIR FARSAD LAYEGH University of Twente

学位级别：硕士

In complex classification tasks, such as vegetation classification, the high similarity between different vegetation classes and the low similarity within a vegetation class can confuse the classification algorithm in assigning correct class labels to unlabeled samples. Therefore, the main objective of this study is to design and implement a new classification methodology by integrating expert system and semi-supervised learning (SSLES) for classification of wetland vegetation cover using Sentinel-2 data. Furthermore, the potential of different Sentinel-2 spectral band combinations for the classification of vegetation types are investigated. The vegetation classes of Schiermonnikoog island, the Netherlands, were identified utilizing Sentinel-2 data of 17 July 2016, RapidEye data of 18th July 2015 as well as an existing vegetation map belonging to 2010 and ancillary field data of the study site (the latter were considered as the expert knowledge). The proposed approach consists of three main steps: object-based image analysis (OBIA), SSLES and post-classification. Using OBIA the satellite image is segmented to generate image objects and extract the image features. Using SSLES training samples are increased by labeling some of the most certain unlabeled samples and finally, in post-classification, classification is performed on the training sets. To evaluate the performance of SSLES, its obtained classification accuracy was compared to the one obtained from a standard supervised classifier (i. e. Random Forest), in terms of overall accuracy. To validate the results of both methods, a test set extracted from reference data, i. e. vegetation map, was used. At the end, McNemar's significance test was performed to compare the obtained results of both methods. The obtained results revealed that using our proposed method, the classification performance of a standard supervised method can improve significantly. Moreover, further analysis of the obtained results from our prop

关键词： Semi-supervised learning Expert system object-based image analysis Sentinel-2 wetland vegetation cover

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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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Mapping riparian condition indicators in a sub-tropical savanna environment from discrete return LiDAR data using object-based image analysis

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ECOLOGICAL INDICATORS 2010年第4期10卷 796-807页

作者： Johansen, Kasper Arroyo, Lara A. Armston, John Phinn, Stuart Witte, Christian Univ Queensland Ctr Remote Sensing & Spatial Informat Sci Sch Geog Planning & Environm Management Brisbane Qld 4072 Australia Queensland Govt Ctr Remote Sensing Dept Environm & Resource Management Indooroopilly Qld 4068 Australia

Mapping, monitoring and managing the environmental condition of riparian zones are major focus areas for local and state governments in Australia. New remotely sensed data techniques that can provide the required mapping accuracies, complete spatial coverage and processing and mapping transferability are currently being developed for use over large spatial extents. The research objective was to develop and apply an approach for mapping riparian condition indicators using object-based image analysis of airborne Light Detection and Ranging (LiDAR) data. The indicators assessed were: streambed width;riparian zone width;plant projective cover (PPC);longitudinal continuity;coverage of large trees;vegetation overhang;and stream bank stability. LiDAR data were captured on 15 July 2007 for two 5 km stretches along Mimosa Creek in Central Queensland, Australia. Field measurements of riparian vegetation structural and land form parameters were obtained between 28 May and 5 June 2007. object-based approaches were developed for mapping each riparian condition indicator from the LiDAR data. The validation and empirical modelling results showed that the object-based approach could be used to accurately map the riparian condition indicators (R-2 = 0.99 for streambed width, R-2 = 0.82 for riparian zone width, R-2 = 0.89 for PPC, R-2 = 0.40 for bank stability). These research findings will be used in a 26,000 km mapping project assessing riparian vegetation and physical form indicators from LiDAR data in Victoria, Australia. (C) 2010 Elsevier Ltd. All rights reserved.

关键词： Airborne LiDAR Riparian zone Environmental indicators object-based image analysis Remote sensing

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Reproducibility and Practical Adoption of GEOBIA with Open-Source Software in Docker Containers

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REMOTE SENSING 2017年第3期9卷 290-290页

作者： Knoth, Christian Nuest, Daniel Univ Munster Inst Geoinformat Heisenbergstr 2 D-48149 Munster Germany

Geographic object-based image analysis (GEOBIA) mostly uses proprietary software, but the interest in Free and Open-Source Software (FOSS) for GEOBIA is growing. This interest stems not only from cost savings, but also from benefits concerning reproducibility and collaboration. Technical challenges hamper practical reproducibility, especially when multiple software packages are required to conduct an analysis. In this study, we use containerization to package a GEOBIA workflow in a well-defined FOSS environment. We explore the approach using two software stacks to perform an exemplary analysis detecting destruction of buildings in bi-temporal images of a conflict area. The analysis combines feature extraction techniques with segmentation and object-based analysis to detect changes using automatically-defined local reference values and to distinguish disappeared buildings from non-target structures. The resulting workflow is published as FOSS comprising both the model and data in a ready to use Docker image and a user interface for interaction with the containerized workflow. The presented solution advances GEOBIA in the following aspects: higher transparency of methodology;easier reuse and adaption of workflows;better transferability between operating systems;complete description of the software environment;and easy application of workflows by image analysis experts and non-experts. As a result, it promotes not only the reproducibility of GEOBIA, but also its practical adoption.

关键词： reproducibility GEOBIA Docker conflict monitoring reproducible research object-based image analysis QGIS containerization

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Alternative stable states of tidal marsh vegetation patterns and channel complexity

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ECOHYDROLOGY 2016年第8期9卷 1639-1662页

作者： Moffett, K. B. Gorelick, S. M. Washington State Univ Sch Environm 14204 NE Salmon Creek Ave Vancouver WA 98686 USA Stanford Univ Dept Earth Syst Sci Stanford CA 94305 USA

Intertidal marshes develop between uplands and mudflats, and develop vegetation zonation, via biogeomorphic feedbacks. Is the spatial configuration of vegetation and channels also biogeomorphically organized at the intermediate, marsh-scale? We used high-resolution aerial photographs and a decision-tree procedure to categorize marsh vegetation patterns and channel geometries for 113 tidal marshes in San Francisco Bay estuary and assessed these patterns' relations to site characteristics. Interpretation was further informed by generalized linear mixed models using pattern-quantifying metrics from object-based image analysis to predict vegetation and channel pattern complexity. Vegetation pattern complexity was significantly related to marsh salinity but independent of marsh age and elevation. Channel complexity was significantly related to marsh age but independent of salinity and elevation. Vegetation pattern complexity and channel complexity were significantly related, forming two prevalent biogeomorphic states: complex versus simple vegetation-and-channel configurations. That this correspondence held across marsh ages (decades to millennia) and at both high and low marsh elevations suggests the following: (1) marshes of shared physiography can exhibit highly variable ecosystem structures;(2) young marshes are not necessarily simple nor necessarily develop vegetation complexity with age and elevation;(3) Bay marshes should continue to exhibit both simple/complex configurations in the future despite a likely shift toward low marshes;(4) salt marshes may tend to occupy two alternative stable states characterized by linked complexity in vegetation and channel organization. This final point may help fill the gap at the marsh scale between biogeomorphic models explaining marsh occurrence at larger coastal and smaller vegetation patch scales. Copyright (C) 2016 John Wiley & Sons, Ltd.

关键词： tidal marsh estuary biogeomorphic vegetation zonation stable state San Francisco Bay object-based image analysis

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Mapping tree species diversity of a tropical montane forest by unsupervised clustering of airborne imaging spectroscopy data

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ECOLOGICAL INDICATORS 2016年 64卷 49-58页

作者： Schafer, Elisa Heiskanen, Janne Heikinheimo, Vuokko Pellikka, Petri Univ Helsinki Dept Geosci & Geog POB 64 FI-00014 Helsinki Finland

With the ongoing global biodiversity loss, approaches to measuring and monitoring biodiversity are necessary for effective conservation planning, especially in tropical forests. Remote sensing has much potential for biodiversity mapping, and high spatial resolution imaging spectroscopy (IS) allows for direct prediction of tree species diversity based on spectral reflectance. The objective of this study was to test an approach for mapping tree species alpha diversity that takes advantage of an unsupervised object-based clustering. Tree species diversity of a tropical montane forest in the Taita Hills, Kenya, was mapped based on spectral variation of high spatial resolution IS data. Airborne IS data and species data from 31 field plots were collected in the study area. Species diversity measures were obtained from the IS data by clustering spectrally similar image segments representing tree crowns. In order to do this, the image was segmented to objects that represented tree crowns. Three measures of species diversity were calculated based on the field data and on the clustering results, and the relationships were statistically analyzed. According to the results, the approach succeeded well in revealing tree species diversity patterns. Especially, tree species richness was well predicted (RMSE = 3 species;r(2) = 0.50) directly based on the clustering results. The optimal number of clusters was found to be close to the estimated number of tree species in the forest. Minimum tree size was an important determinant of the relationships, because only part of the trees are visible to the airborne sensor in the multi-layered closed canopy forest. In general, the object-based approach proved to be a viable alternative to a pixel-based clustering. The approach takes advantage of the capability of IS to detect spectral differences among tree crowns, but without the need for spectral training data, which is expensive to collect. With further development, the approach could be ap

关键词： Tree species diversity Imaging spectroscopy Clustering Tropical forest object-based image analysis

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