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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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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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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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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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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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Monitoring land use changes associated with urbanization: An object based image analysis approach

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EUROPEAN JOURNAL OF REMOTE SENSING 2015年第1期48卷 85-99页

作者： Samal, Dipak R. Gedam, Shirish S. Indian Inst Technol Ctr Studies Resources Engn Bombay 400076 Maharashtra India

Land use/land cover (LULC) change occurs due to natural and anthropogenic causes. In developing countries, rapid industrialization and urbanization imposes a major threat to natural environment. Present study was carried out to monitor the LULC changes due to urbanization in a rapidly changing river basin, India. The purpose of choosing the river basin was to analyze past changes and predict possible consequences within a defined natural boundary. Multi-temporal images acquired from Landsat and Indian Remote Sensing (IRS) satellites between 1992-2009 as well as a digital elevation model was used to generate historical and current LULC pattern in the basin. An object based image analysis technique was employed for precise classification of multi-temporal images followed by GIS-based change detection studies. The study reveals that the built up area has increased significantly and added 288 km(2) between 1992 - 2009. Increase in built up area is attributed to decrease in wastelands and agricultural land. The expansion of built up area along major transportation networks, specifically after the year 2000 shows the rapid rate of urbanization in the basin.

关键词： GIS Upper Bhima basin urbanization object based image analysis change matrix

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A REGION-based MULTI-SCALE APPROACH FOR object-based image analysis 23

A REGION-BASED MULTI-SCALE APPROACH FOR OBJECT-BASED IMAGE A...

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23rd Congress of the International-Society-for-Photogrammetry-and-Remote-Sensing (ISPRS)

作者： Kavzoglu, T. Erdemir, M. Yildiz Tonbul, H. Gebze Tech Univ Geomat Engn Dept TR-41400 Gebze Turkey

Within the last two decades, object-based image analysis (OBIA) considering objects (i.e. groups of pixels) instead of pixels has gained popularity and attracted increasing interest. The most important stage of the OBIA is image segmentation that groups spectrally similar adjacent pixels considering not only the spectral features but also spatial and textural features. Although there are several parameters (scale, shape, compactness and band weights) to be set by the analyst, scale parameter stands out the most important parameter in segmentation process. Estimating optimal scale parameter is crucially important to increase the classification accuracy that depends on image resolution, image object size and characteristics of the study area. In this study, two scale-selection strategies were implemented in the image segmentation process using pan-sharped Qickbird-2 image. The first strategy estimates optimal scale parameters for the eight sub-regions. For this purpose, the local variance/rate of change (LV-RoC) graphs produced by the ESP-2 tool were analysed to determine fine, moderate and coarse scales for each region. In the second strategy, the image was segmented using the three candidate scale values (fine, moderate, coarse) determined from the LV-RoC graph calculated for whole image. The nearest neighbour classifier was applied in all segmentation experiments and equal number of pixels was randomly selected to calculate accuracy metrics (overall accuracy and kappa coefficient). Comparison of region-based and image-based segmentation was carried out on the classified images and found that region-based multi-scale OBIA produced significantly more accurate results than image based single-scale OBIA. The difference in classification accuracy reached to 10% in terms of overall accuracy.

关键词： object based image analysis Region-based scale Classification Multiresolution Segmentation Estimation of Scale Parameter (ESP)

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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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Identifying urban built-up areas using CORONA imagery in the pre-Landsat era: a case study of Shanghai in 1965

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INTERNATIONAL JOURNAL OF REMOTE SENSING 2024年第6期45卷 2028-2048页

作者： Li, Shuang Fudan Univ Ctr Hist Geog Studies Shanghai Peoples R China Fudan Univ Ctr Hist Geog Studies 220 Handan Rd Shanghai 200433 Peoples R China

The Corona project (1959-1972) created a large number of high-resolution images that provided valuable historical research data before the advent of Landsat (1972) and even compensated for the lack of accuracy in early stages. In this study, we collected eight available Corona images to create a historical remote sensing image of Shanghai in the 1960 and developed an automatic image segmentation and classification methodology. In the experimental part, we verified its accuracy, and discussed the urbanization process in Shanghai. The results showed that 1) In 1965, the urban area of Shanghai was mainly distributed in Puxi, and the urbanization degree of the entire hinterland of Pudong was still very low. Built-up areas were highly concentrated in the main urban areas. 2) The overall accuracy was 91.5% and the kappa coefficient was 0.762. 3) We compared the built-up area in 1965 with the current urban area, which increased from 306.23 to 2,804.25 km2. Besides, historical imagery also captured the state of environmental pollution at that time. Overall, our method could inform the mapping and classification of remotely sensed land surfaces on a global scale in the 1960s, extending pre-Landsat era research to studies of urban history, archaeology and cultural heritage.

关键词： Corona pre-Landsat object based image analysis archaeology Keyhole

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Crop type detection using an object-based classification method and multi-temporal Landsat satellite images

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PADDY AND WATER ENVIRONMENT 2022年第3期20卷 395-412页

作者： Karimi, Neamat Sheshangosht, Sara Eftekhari, Mortaza Water Res Inst Dept Water Resources Study & Res Tehran Iran

Crop type detection is of great importance in water resource allocation and planning mostly in arid and semi-arid regions of Iran. Landsat-OLI 16-day inter-annual images are invaluable sources obviating crop monitoring into issues of crop types detection, crop yield prediction, and crop pattern studies. Although many classification methods such as decision tree (DT), support vector machine (SVM), and maximum likelihood (ML) were implied for crop type mapping, recent researches often use an object-based classification approach. In this study, an object-based image analysis (OBIA) classifier based on rule-based decision tree (RBDT) and object-based nearest neighbor (OBNN) used to delineate five common crop types (includes Wheat and Barley together in one class, rice, multiple crop (MC), Alfalfa and Spring crops) in Isfahan city and nearby areas. The classification was applied in five scenarios using different vegetation indexes including normalized difference vegetation index (NDVI), normalized difference water index (NDWI), green normalized difference vegetation index GNDVI and their combination. All scenarios property and accuracy assessed both with by class separation distance matrix and confusion matrix. The overall accuracy of classification with using only one vegetation index was lower than other scenarios. It was the lowest for GNDVI rating 37% whereas combination of Indexes resulted better accuracy. In final map with combination of NDVI, GNDVI and NDWI, overall accuracy and kappa achieved to 88% and 0/83 successively. Comparing individual accuracy of different crops showed that MC crops with 66% has the lowest accuracy and Wheat-Barely crops with 94.8% individual accuracy has the Maximum accuracy. Other crop types accuracy alters between 66 and 94.8%.

关键词： Crop type object based image analysis Segmentation NDVI NDWI

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