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object based image analysis for remote sensing

ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING 2010年第1期65卷 2-16页

作者： Blaschke, T. Salzburg Univ Z GIS Ctr Geoinformat Hellbrunner St 34 A-5020 Salzburg Austria Salzburg Univ Dept Geog & Geol A-5020 Salzburg Austria

Remote sensing imagery needs to be converted into tangible information which can be utilised in conjunction with other data sets, often within widely used Geographic Information Systems (GIS). As long as pixel sizes remained typically coarser than, or at the best, similar in size to the objects of interest, emphasis was placed on per-pixel analysis, or even sub-pixel analysis for this conversion, but with increasing spatial resolutions alternative paths have been followed, aimed at deriving objects that are made up of several pixels. This paper gives an overview of the development of object based methods, which aim to delineate readily usable objects from imagery while at the same time combining image processing and GIS functionalities in order to utilize spectral and contextual information in an integrative way. The most common approach used for building objects is image segmentation, which dates back to the 1970s. Around the year 2000 GIS and image processing started to grow together rapidly through object based image analysis (OBIA - or GEOBIA for geospatial object based image analysis). In contrast to typical Landsat resolutions, high resolution images support several scales within their images. Through a comprehensive literature review several thousand abstracts have been screened, and more than 820 OBIA-related articles comprising 145 journal papers, 84 book chapters and nearly 600 conference papers, are analysed in detail. It becomes evident that the first years of the OBIA/GEOBIA developments were characterised by the dominance of 'grey' literature, but that the number of peer-reviewed journal articles has increased sharply over the last four to five years. The pixel paradigm is beginning to show cracks and the OBIA methods are making considerable progress towards a spatially explicit information extraction workflow, such as is required for spatial planning as well as for many monitoring programmes. (C) 2009 International Society for Photogrammetry and Remot

关键词： object based image analysis OBIA GEOBIA GIScience Multiscale image analysis

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object based image analysis Of Geo-eye VHR data To Model Above ground Carbon Stock In himalayan Mid-hill Forests, Nepal

Object Based Image analysis Of Geo-eye VHR data To Model Abo...

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作者： Nandin-erdene Tsendbazar University of Twente

学位级别：硕士

Recently, interest in implementing projects on reducing carbon emission from deforestation and forest degradation (REDD) for mitigating carbon dioxide emission has been increased. Consequently, an accurate and precise measurement of carbon stock in cost effective ways is needed. Fine resolution satellite imagery, together with object based image analysis (OBIA) techniques provide new opportunities to improve aboveground carbon stock estimation on the basis of allometric relationship of crown projection area (CPA) and tree biomass. This research aimed to model carbon stock in upper-subtropical forests of Nepal using very high resolution Geo-Eye imagery and OBIA. Individual tree crown delineation approaches of Valley Following and Region Growing using 0.5 meter spatial resolution of Geo-Eye imagery were used in this research for the delineation of tree crowns in complex mixed forests. Valley Following approach was conducted in Individual Tree Crown delineation (ITC) suite in PCI-Geomatica, while Region Growing approach was done in eCognition software by developing specific rule-set. The best tree crown delineation of these approaches was further used for species and forest type classifications at individual tree crown level. based on the field measurements of stem diameters, carbon stock of trees was calculated and the relationship between carbon stock of tree and CPA from high resolution image was analysed using simple linear regression model. The Region Growing approach resulted in better delineation of tree crown (30% error with 75% 1:1 correspondence) than Valley following approach (40% error with 67% 1:1 correspondence). Having more accurate delineation, the delineated tree crowns from Region Growing approach were used for species and forest type classifications. Species classification resulting in 64.5% accuracy (Kappa=0.48) provided much lower accuracy than forest type classification (90.3% accuracy and Kappa=0.80). Modelling the relationship between automatica

关键词： Aboveground carbon stock object based image analysis Tree crown delineation Region Growing Valley Following Crown projection area

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A framework of region-based spatial relations for non-overlapping features and its application in object based image analysis

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ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING 2008年第4期63卷 461-475页

作者： Liu, Yu Guo, Qinghua Kelly, Maggi Univ Calif Merced Sch Engn Merced CA 95344 USA Peking Univ Inst Remote Sensing & Geog Informat Syst Beijing 100871 Peoples R China Univ Calif Berkeley Geospatial Imaging and Infomat Facil Dept Environm Sci Policy & Management Berkeley CA 94720 USA

object based image analysis (OBIA) is an approach increasingly used in classifying high spatial resolution remote sensing images. object based image classifiers first segment an image into objects (or image segments), and then classify these objects based on their attributes and spatial relations. Numerous algorithms exist for the first step of the OBIA process, i.e. image segmentation. However. less research has been conducted on the object classification part of OBIA, in particular the spatial relations between objects that are commonly used to construct rules for classifying image objects and refining classification results. In this paper, we establish a context where objects are areal (not points or lines) and non-overlapping (we call this "single-valued" space), and propose a framework of binary spatial relations between segmented objects to aid in object classification. In this framework, scale-dependent "line-like objects" and "point-like objects" are identified from areal objects based on their shapes. Generally, disjoint and meet are the only two possible topological relations between two non-overlapping areal objects. However, a number of quasitopological relations can be defined when the shapes of the objects involved are considered. Some of these relations are fuzzy and thus quantitatively defined. In addition, we define the concepts of line-like objects (e.g. roads) and point-like objects (e.g. wells), and develop the relations between two line-like objects or two point-like objects. For completeness, cardinal direction relations and distance relations are also introduced in the proposed context. Finally, we implement the framework to extract roads and moving vehicles from an aerial photo. The promising results suggest that our methods can be a valuable tool in defining rules for object based image analysis. Published by Elsevier B.V. on behalf of International Society for Photogrammetry and Remote Sensing, Inc. (ISPRS).

关键词： region-based spatial relations single-valued space object based image analysis

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A GIS and object based image analysis approach to mapping the greenspace composition of domestic gardens in Leicester, UK

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LANDSCAPE AND URBAN PLANNING 2019年 183卷 133-146页

作者： Baker, Fraser Smith, Claire Univ Leicester Dept Geog Univ Rd Leicester LE1 7RH Leics England Manchester Metropolitan Univ Sch Sci & Environm John Dalton BldgChester St Manchester M1 5GD Lancs England

Greenspace provides a range of environmental benefits for urban residents, and is considered an important resource in urban development strategies. However, city-wide greenspace benefit modelling approaches often overestimate greenspace within garden areas, or even omit gardens from such analysis. As combined garden areal extents are significant for UK urban areas, improved estimations of garden greenspace abundance are required to improve urban greenspace analysis. This study investigates the methodological implications of a GIS and object based image analysis (OBIA) remote sensing approach with very high resolution Worldview-2 imagery for classifying UK urban garden surfaces. Gardens for an approximate 7 km(2) study area of the city of Leicester, UK were classified with an overall accuracy similar to 86%. The study demonstrates the applicability of GIS and OBIA analysis for mapping UK urban garden surfaces, with the methodology detailed here useful as a framework for further UK garden studies. Improvements to the current methodology are also considered in lieu of data and methodological limitations. In addition, the resulting garden surface dataset was analysed to examine associations of garden greenspace proportions with physical garden characteristics. Low greenspace proportions are found to be particularly prevalent for Victorian Terraced housing types in the study area;with greenspace proportions also generally associated positively with garden areal extents. Identification of potential predictive characteristics for garden greenspace abundance may prove useful as proxy information for urban greenspace analysis in other UK urban areas.

关键词： Domestic gardens object based image analysis Urban greenspace Worldview-2

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Integrating fuzzy object based image analysis and ant colony optimization for road extraction from remotely sensed images

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ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING 2018年 138卷 151-163页

作者： Maboudi, Mehdi Amini, Jalal Malihi, Shirin Hahn, Michael Tech Univ Carolo Wilhelmina Braunschweig Inst Geodesy & Photogrammetry Bienroder Weg 81 D-38100 Braunschweig Germany Univ Tehran Coll Engn Sch Surveying & Geospatial Engn Tehran Iran Khaje Nasir Toosi Univ Technol Fac Geodesy & Geomat Engn Tehran Iran Univ Appl Sci Fac Geomat Comp Sci & Math Stuttgart Germany

Updated road network as a crucial part of the transportation database plays an important role in various applications. Thus, increasing the automation of the road extraction approaches from remote sensing images has been the subject of extensive research. In this paper, we propose an object based road extraction approach from very high resolution satellite images. based on the object based image analysis, our approach incorporates various spatial, spectral, and textural objects' descriptors, the capabilities of the fuzzy logic system for handling the uncertainties in road modelling, and the effectiveness and suitability of ant colony algorithm for optimization of network related problems. Four VHR optical satellite images which are acquired by Worldview-2 and IKONOS satellites are used in order to evaluate the proposed approach. Evaluation of the extracted road networks shows that the average completeness, correctness, and quality of the results can reach 89%, 93% and 83% respectively, indicating that the proposed approach is applicable for urban road extraction. We also analyzed the sensitivity of our algorithm to different ant colony optimization parameter values. Comparison of the achieved results with the results of four state-of-the-art algorithms and quantifying the robustness of the fuzzy rule set demonstrate that the proposed approach is both efficient and transferable to other comparable images. (C) 2017 International Society for Photogrammetry and Remote Sensing, Inc. (ISPRS). Published by Elsevier B.V. All rights reserved.

关键词： Road extraction Ant colony optimization Fuzzy logic system object based image analysis

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Combining pixel and object based image analysis of ultra-high resolution multibeam bathymetry and backscatter for habitat mapping in shallow marine waters

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MARINE GEOPHYSICAL RESEARCH 2018年第1-2期39卷 271-288页

作者： Ierodiaconou, Daniel Schimel, Alexandre C. G. Kennedy, David Monk, Jacquomo Gaylard, Grace Young, Mary Diesing, Markus Rattray, Alex Deakin Univ Sch Life & Environm Sci Ctr Integrat Ecol Warrnambool CampusSherwood PkPOB 423 Warrnambool Vic 3280 Australia Taihoro Nukurangi Natl Inst Water & Atmospher Res NIWA 301 Evans Bay Parade Wellington 6021 New Zealand Univ Melbourne Sch Geog Parkville Vic 3010 Australia Univ Tasmania Inst Marine & Antarctic Studies Private Bag 49 Hobart Tas 7001 Australia Geol Survey Norway POB 6315 Torgarden N-7491 Trondheim Norway Ctr Environm Fisheries & Aquaculture Sci Pakefield Rd Lowestoft NR33 0HT Suffolk England

Habitat mapping data are increasingly being recognised for their importance in underpinning marine spatial planning. The ability to collect ultra-high resolution (cm) multibeam echosounder (MBES) data in shallow waters has facilitated understanding of the fine-scale distribution of benthic habitats in these areas that are often prone to human disturbance. Developing quantitative and objective approaches to integrate MBES data with ground observations for predictive modelling is essential for ensuring repeatability and providing confidence measures for habitat mapping products. Whilst supervised classification approaches are becoming more common, users are often faced with a decision whether to implement a pixel based (PB) or an object based (OB) image analysis approach, with often limited understanding of the potential influence of that decision on final map products and relative importance of data inputs to patterns observed. In this study, we apply an ensemble learning approach capable of integrating PB and OB image analysis from ultra-high resolution MBES bathymetry and backscatter data for mapping benthic habitats in Refuge Cove, a temperate coastal embayment in south-east Australia. We demonstrate the relative importance of PB and OB seafloor derivatives for the five broad benthic habitats that dominate the site. We found that OB and PB approaches performed well with differences in classification accuracy but not discernible statistically. However, a model incorporating elements of both approaches proved to be significantly more accurate than OB or PB methods alone and demonstrate the benefits of using MBES bathymetry and backscatter combined for class discrimination.

关键词： Multibeam echosounder Marine habitat mapping object based image analysis Random forests

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Exploring the Potential of object based image analysis for Mapping Urban Land Cover

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JOURNAL OF THE INDIAN SOCIETY OF REMOTE SENSING 2012年第3期40卷 507-518页

作者： Jalan, Seema Govt PG Coll Dept Geog Dausa Rajasthan India

The paper investigates the performance and potential of object based image analysis technique (OBIA) for land cover information extraction from high resolution satellite datasets. Efficiency of the technique has been assessed in different urban land cover situations using merged CARTOSAT-1 and IRS-P6 LISS-IV image subsets of Dehradun, India. Multi-scale iterative Nearest Neighbour classification has been applied. Classification results have been quantitatively evaluated. Results reveal that OBIA is a fast, simple, flexible and efficient semi automated tool to deal with high spatial and spectral heterogeneity inherent in high resolution urban datasets. Integration of shape and texture characteristics with traditional spectral signatures, and use of multiple object levels in the classification process yielded classified map units having high correspondence with real world geographical entities. High accuracies were achieved for spectrally and spatially distinct classes like built up structures and tree covered land. Performance of the technique is impaired in case of classes having overlapping spectral characteristics. The parameters for segmentation and class descriptions developed for one area were successfully transferred to the other with minor adaptations. Utility of the technique is further enhanced by flexibility of visualisation of maps at different levels of classification hierarchy and immediate integration of the classified product in GIS environment with direct realization of vector maps for spatial analysis.

关键词： object based image analysis Urban Areas Land Cover Classification High Resolution Data Nearest Neighbour Method

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A combined use of multispectral and SAR images for ship detection and characterization through object based image analysis 8

A combined use of multispectral and SAR images for ship dete...

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17th SPIE Conference on Earth Resources and Environmental Remote Sensing/GIS Applications

作者： Aiello, Martina Gianinetto, Marco Politecn Milan Dept Architecture Built Environm & Construct Engn Piazza Leonardo da Vinci 32 I-20133 Milan Italy

ISBN: (纸本)9781510613218;9781510613201

Marine routes represent a huge portion of commercial and human trades, therefore surveillance, security and environmental protection themes are gaining increasing importance. Being able to overcome the limits imposed by terrestrial means of monitoring, ship detection from satellite has recently prompted a renewed interest for a continuous monitoring of illegal activities. This paper describes an automatic object based image analysis (OBIA) approach to detect vessels made of different materials in various sea environments. The combined use of multispectral and SAR images allows for a regular observation unrestricted by lighting and atmospheric conditions and complementarity in terms of geographic coverage and geometric detail. The method developed adopts a region growing algorithm to segment the image in homogeneous objects, which are then classified through a decision tree algorithm based on spectral and geometrical properties. Then, a spatial analysis retrieves the vessels' position, length and heading parameters and a speed range is associated. Optimization of the image processing chain is performed by selecting image tiles through a statistical index. Vessel candidates are detected over amplitude SAR images using an adaptive threshold Constant False Alarm Rate (CFAR) algorithm prior the object based analysis. Validation is carried out by comparing the retrieved parameters with the information provided by the Automatic Identification System (AIS), when available, or with manual measurement when AIS data are not available. The estimation of length shows R-2= 0.85 and estimation of heading R-2= 0.92, computed as the average of R-2 values obtained for both optical and radar images.

关键词： Copernicus ship detection clandestine immigration object based image analysis multispectral images SAR images European Union

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Forest Fire Damage Assessment Using object based image analysis

Forest Fire Damage Assessment Using Object Based Image Analy...

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22nd IEEE Signal Processing and Communications Applications Conference (SIU)

作者： Kugu, Emin Kisa, Akin Hava Harp Okulu Bilgisayar Muhendisligi Bolumu Istanbul Turkey Tapu & Kadastro Genel Mudurlugu Harita Dairesi Baskanligi Ankara Turkey

ISBN: (纸本)9781479948741

In recent years, developments in satellite and sensor technology brought new technologies for the assessment of the information obtained through these new sensors. One of these technologies is the object based image analysis (OBIA) technology which allows you to make various queries on the high resolution satellite or aerial images. In this work, a damage assessment have been conducted by using object based image analysis method on a high resolution aerial image taken after a forest fire. The eCognition software which provides the object based image analysis opportunities is used in the work and the results revealed that how this software can make such flexible queries. It is possible to make various analysis on the classes obtained by the pixel based segmentation process. In this study, a small part of a large area of a high resolution aerial image is subjected to an object based analysis through the eCognition software and the size of the fire damaged area was uncovered. The analysis of the whole fire area can be obtained after applying the rule base, acquired from this process, on the whole image.

关键词： Forest fire object based image analysis damage assessment

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APPLICATION OF object based image analysis IN EARTHQUAKE EMERGENCY PRODUCTS

APPLICATION OF OBJECT BASED IMAGE ANALYSIS IN EARTHQUAKE EME...

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IEEE International Geoscience and Remote Sensing Symposium (IGARSS)

作者： Chen Yahui Gao Xiaoyue Li Xiaoli Duan Yihao China Earthquake Networks Ctr Beijing 100045 Peoples R China

ISBN: (纸本)9781665403696

In order to provide targeted assessment suggestions after the earthquake and enrich earthquake emergency thematic map products, the paper combines remote sensing with earthquake application to obtain the 10-meters resolution tile data of Bing map before the Songyuan MS5.7, Jilin, China. Using eCognition software for multiresolution segmentation, large-scale is used to extract water, bare land, small-scale is used to extract roads, buildings, etc. After that, the threshold classification method is used to establish the rule set by analyzing the spectral, geometric and textural features of each type of objects. The threshold ranges of brightness, area, length-width ratio, eutrophic water index (EWI), etc. are set to obtain the threshold classification rules, so as to accomplish the fast classification. Summarizing the features and threshold ranges are helpful to improve the efficiency of image information extraction and providing data and products in a timely manner after the earthquake. The object-oriented image classification method could be widely applied in earthquake emergency command, post-earthquake casualties assessment and rapid mapping.

关键词： object based image analysis eCognition Earthquake Emergency

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