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An open-source approach to characterizing Chihuahuan Desert vegetation communities using object-based image analysis

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JOURNAL OF ARID ENVIRONMENTS 2021年 188卷 104383-104383页

作者： Bankert, Andrew R. Strasser, Erin H. Burch, Cristy G. Correll, Maureen D. Bird Conservancy Rockies Ft Collins CO 80521 USA Texas Pk & Wildlife Medard TX USA

Methods: for quantifying vegetative cover across landscapes have, until recently, been limited to ground-based surveys or remote sensing via satellites or aircraft, both of which can limit the spatial scale of resulting data. Unmanned Aircraft Systems (UAS) can efficiently collect high-resolution sub-decimeter imagery of landscapes;geographic, object-based image analysis (GEOBIA) of the collected imagery can then be used to estimate vegetation cover. To date, few researchers have utilized open-source programs for GEOBIA. We developed GEOBIA methods in the open-source Program R to analyze visible spectrum UAS imagery from four sites in the Chihuahuan Desert of North America. These desert grasslands are difficult to quantify due to the patchiness of ground cover at small scales (e.g. <1 m) and the rarity of shrubs on the landscape. We used site-specific training data and multiple segmentation parameters to create vegetative and shrub cover data layers at a 15 cm resolution. We report overall accuracies of 77.2%-88.8% for vegetation classification and 95.7%-99.2% for shrub classification. Our work is some of the first to use open-source GEOBIA in grasslands and provides objective, reproducible data layers of desert vegetation, particularly shrubs, at the spatial scale necessary to inform management and conservation of Chihuahuan Desert grassland communities.

关键词： object-based image analysis Vegetation mapping Unmanned aerial systems Grassland vegetation Automated image analysis Habitat quantification

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CognitionMaster: an object-based image analysis framework

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DIAGNOSTIC PATHOLOGY 2013年第1期8卷 34-34页

作者： Wienert, Stephan Heim, Daniel Kotani, Manato Lindequist, Bjoern Stenzinger, Albrecht Ishii, Masaru Hufnagl, Peter Beil, Michael Dietel, Manfred Denkert, Carsten Klauschen, Frederick Charite Inst Pathol D-10117 Berlin Germany VMscope GmbH D-10117 Berlin Germany Osaka Univ Lab Cellular Dynam WPI IFReC Suita Osaka 5650871 Japan Japan Sci & Technol Agcy JST CREST Chiyoda Ku Tokyo 1020075 Japan Univ Appl Sci Berlin D-12459 Berlin Germany Univ Heidelberg Hosp Inst Pathol D-69120 Heidelberg Germany Univ Ulm Dept Med 1 D-89081 Ulm Germany

Background: Automated image analysis methods are becoming more and more important to extract and quantify image features in microscopy-based biomedical studies and several commercial or open-source tools are available. However, most of the approaches rely on pixel-wise operations, a concept that has limitations when high-level object features and relationships between objects are studied and if user-interactivity on the object-level is desired. Results: In this paper we present an open-source software that facilitates the analysis of content features and object relationships by using objects as basic processing unit instead of individual pixels. Our approach enables also users without programming knowledge to compose "analysis pipelines" that exploit the object-level approach. We demonstrate the design and use of example pipelines for the immunohistochemistry-based cell proliferation quantification in breast cancer and two-photon fluorescence microscopy data about bone-osteoclast interaction, which underline the advantages of the object-based concept. Conclusions: We introduce an open source software system that offers object-based image analysis. The object-based concept allows for a straight-forward development of object-related interactive or fully automated image analysis solutions. The presented software may therefore serve as a basis for various applications in the field of digital image analysis.

关键词： Software Open source image analysis object-based image analysis

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image Segmentation and object-based image analysis for Environmental Monitoring: Recent Areas of Interest, Researchers' Views on the Future Priorities

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REMOTE SENSING 2020年第11期12卷 1772-1772页

作者： Johnson, Brian Alan Ma, Lei Inst Global Environm Strategies Nat Resources & Ecosyst Serv 2108-11 Kamiyamaguchi Hayama Kanagawa 2400115 Japan Nanjing Univ Sch Geog & Ocean Sci Nanjing 210023 Peoples R China Tech Univ Munich Signal Proc Earth Observat D-80333 Munich Germany

image segmentation and geographic object-based image analysis (GEOBIA) were proposed around the turn of the century as a means to analyze high-spatial-resolution remote sensing images. Since then, object-based approaches have been used to analyze a wide range of images for numerous applications. In this Editorial, we present some highlights of image segmentation and GEOBIA research from the last two years (2018-2019), including a Special Issue published in the journal Remote Sensing. As a final contribution of this special issue, we have shared the views of 45 other researchers (corresponding authors of published papers on GEOBIA in 2018-2019) on the current state and future priorities of this field, gathered through an online survey. Most researchers surveyed acknowledged that image segmentation/GEOBIA approaches have achieved a high level of maturity, although the need for more free user-friendly software and tools, further automation, better integration with new machine-learning approaches (including deep learning), and more suitable accuracy assessment methods was frequently pointed out.

关键词： GEOBIA object-based image analysis high-spatial-resolution image segmentation parameter optimization

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A building extraction approach for Airborne Laser Scanner data utilizing the object based image analysis paradigm

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INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION 2016年第0期52卷 137-148页

作者： Tomljenovic, Ivan Tiede, Dirk Blaschke, Thomas Salzburg Univ Dept Geoinformat Z GIS Schillerstr 30 A-5020 Salzburg Austria

In the past two decades object-based image analysis (OBIA) established itself as an efficient approach for the classification and extraction of information from remote sensing imagery and, increasingly, from non-image based sources such as Airborne Laser Scanner (ALS) point clouds. ALS data is represented in the form of a point cloud with recorded multiple returns and intensities. In our work, we combined OBIA with ALS point cloud data in order to identify and extract buildings as 2D polygons representing roof outlines in a top down mapping approach. We performed rasterization of the ALS data into a height raster for the purpose of the generation of a Digital Surface Model (DSM) and a derived Digital Elevation Model (DEM). Further objects were generated in conjunction with point statistics from the linked point cloud. With the use of class modelling methods, we generated the final target class of objects representing buildings. The approach was developed for a test area in Biberach an der Riss (Germany). In order to point out the possibilities of the adaptation-free transferability to another data set, the algorithm has been applied "as is" to the ISPRS Benchmarking data set of Toronto (Canada). The obtained results show high accuracies for the initial study area (thematic accuracies of around 98%, geometric accuracy of above 80%). The very high performance within the ISPRS Benchmark without any modification of the algorithm and without any adaptation of parameters is particularly noteworthy. (C) 2016 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license.

关键词： object-based image analysis LiDAR Building delineation Building identification Point cloud analysis DIM generation

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object based image analysis for the classification of the growth stages of Avocado crop, in Michoacan State, Mexico 5

Object based image analysis for the classification of the gr...

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Conference on Multispectral, Hyperspectral, and Ultraspectral Remote Sensing Technology, Techniques and Applications V

作者： Gao, Yan Marpu, Prashanth Morales Manilla, Luis M. UNAM Ctr Invest Geog Ambiental Antigua Carretera Patzcuaro 8701 Morelia 58190 Michoacan Mexico Masdar Inst Sci & Technol Abu Dhabi U Arab Emirates

ISBN: (纸本)9781628413304

This paper assesses the suitability of 8-band Worldview-2 (WV2) satellite data and object-based random forest algorithm for the classification of avocado growth stages in Mexico. We tested both pixel-based with minimum distance (MD) and maximum likelihood (MLC) and object-based with Random Forest (RF) algorithm for this task. Training samples and verification data were selected by visual interpreting the WV2 images for seven thematic classes: fully grown, middle stage, and early stage of avocado crops, bare land, two types of natural forests, and water body. To examine the contribution of the four new spectral bands of WV2 sensor, all the tested classifications were carried out with and without the four new spectral bands Classification accuracy assessment results show that object-based classification with RF algorithm obtained higher overall higher accuracy (93.06%) than pixel-based MD (69.37%) and MLC (64.03%) method. For both pixel-based and object-based methods, the classifications with the four new spectral bands (overall accuracy obtained higher accuracy than those without: overall accuracy of object-based RF classification with vs without: 93.06% vs 83.59%, pixel-based MD: 69.37% vs 67.2%, pixel-based MLC: 64.03% vs 36.05%, suggesting that the four new spectral bands in WV2 sensor contributed to the increase of the classification accuracy.

关键词： Avocado crop image segmentation object-based image analysis WorldView-2 Pixel-based image classification

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AN object based image analysis OF MULTISPECTRAL SATELLITE AND DRONE imageS FOR PRECISION AGRICULTURE MONITORING

AN OBJECT BASED IMAGE ANALYSIS OF MULTISPECTRAL SATELLITE AN...

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IEEE International Geoscience and Remote Sensing Symposium (IGARSS)

作者： Dwivedi, Arun Kant Singh, Arun Kumar Singh, Dharmendra Indian Inst Technol Roorkee Dept Comp Sci & Engn Roorkee Uttar Pradesh India Indian Inst Technol Roorkee Dept Elect & Commun Engn Roorkee Uttar Pradesh India

ISBN: (数字)9781665427920

ISBN: (纸本)9781665427920

Accurate information on spatial distribution of crop and vegetation indices for crop health monitoring is important for precision agriculture monitoring. However, freely available multispectral satellite images and unmanned aerial vehicle based multispectral images provides great opportunities for crop area estimation and extraction of vegetation indices. An object-based image analysis is better than pixel-based analysis because it is used statistical, geometrical, and topographic feature of the objects. Therefore, this paper presents an object-based image analysis of multispectral satellite and drone images for crop area estimation and extraction of vegetation indices for precision agriculture monitoring. object segmentation, feature extraction, and classification of multispectral satellite and drone images was done. The experimental results show that the high-resolution drone imagery provides better crop area estimation and vegetation indices compared to freely available coarse resolution satellite imagery due to mixed pixels especially boundary of the crop classes.

关键词： Precision agriculture monitoring area estimation object-based image analysis mixed pixels

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An object based image analysis applied for volcanic and glacial landforms mapping in Sahand Mountain, Iran

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CATENA 2021年 198卷 105073-105073页

作者： Feizizadeh, Bakhtiar Garajeh, Mohammad Kazemi Blaschke, Thomas Lakes, Tobia Univ Tabriz Dept Remote Sensing & GIS Tabriz Iran Univ Tabriz Inst Environm Tabriz Iran Univ Salzburg Dept Geoinformat Z GIS Salzburg Austria Humboldt Univ Dept Geog Lab Geoinformat Sci Berlin Germany

Landform mapping has increasingly become part of the digital domain. While the majority of approaches evaluates Digital Elevation Models (DEM) on a per-pixel basis, some examples exist were object-based image analysis (OBIA) has been applied to terrain data to identify a variety of landforms, including glacial landforms. The main objective of this study is to develop a semi-automated object-based rule set for detecting and delineating volcanic and glacier landforms in the area of the Sahand Mountain, Northern Iran. First, we applied a multi-resolution segmentation algorithm on a freely available Sentinel-2 optical satellite image and then selected the relevant features to define appropriate segmentation scales for each landform category. object-based rule sets were then developed using spatial (DEM and its derivatives, e.g. slope, aspect, curvature and flow accumulation) and spectral information. Volcanic and glacial landforms were detected and classified into eight classes: caldera, volcanic cone, tuff formation, andesite lava, dacite lava, glacier valley, suspension valley, glacier cirque. An accuracy assessment was applied based on the fuzzy synthetic evaluation technique, together with the error matrix and kappa coefficient, using field data and geomorphological units derived from geological maps and very high resolution aerial photographs. The resulting overall accuracies for each class were 96.2%, 93.3%, 92.4%, 94.2%, 93.01, 95.1, 90.1 and 90.5, respectively. Our research demonstrated that spatial (e.g. density, shape index, length/width) and spectral (e.g. mean, brightness and standard deviation) algorithms together with a grey-level co-occurrence matrix (GLCMs) were efficient features for detecting and mapping volcanic and glacial landforms. We conclude that the OBIA-based algorithms and features provide a high potential for detecting and classifying landforms. Results of this study are of great importance for geomorphology and geology as well as geo-tourism

关键词： object-based image analysis Semi-automated approach Volcanic and glacier landform Sahand Mountain Iran

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Multi resolution appraisal of Cork Harbour estuary: An object based image analysis approach

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GEOMORPHOLOGY 2023年第1期439卷

作者： Summers, Gerard Lim, Aaron Wheeler, Andrew J. Univ Coll Cork Environm Res Inst Sch Biol Earth & Environm Sci Distillery FieldsNorth Mall Cork T23 TK30 Ireland Univ Coll Cork UCC Dept Geog Cork T12 K8AF Ireland Univ Coll Cork Environm Res Inst Cork Ireland Univ Coll Cork Irish Ctr Appl Geosci ICRAG Marine & Renewable Energy Inst MaREI Cork P43 C573 Ireland

Lithic sedimentary benthic habitats are often organised by the overlying hydrodynamic regime, generating seabed sedimentary bedforms (SSBs) whose morphology and sediment composition are controlled by the current flow properties including speed, direction, and duration. Furthermore, SSBs can manifest at a variety of spatial scales reflecting different aspects of the hydrodynamic regime that operate at distinct intensities and temporal scales. Acoustic seabed mapping of SSBs permits the quantification of the complex seafloor hydrodynamics without in situ measurements. As such, seabed mapping efforts must determine the minimum spatial resolution required to effectively capture the complete seabed hydrodynamic picture. Here, we provide a semi-automated, object-based image analysis (OBIA) of SSBs occurring over 0.5, 1.0, 1.5, 2.0, and 2.5 m spatial resolutions of multibeam echosounder (MBES) data acquired in Cork Harbour, Ireland, to ascertain the optimum resolution at which to derive current velocity attributes. Estimations of seabed sediment grain size were completed through the angular range analysis (ARA) of co-located 400 kHz multibeam backscatter data. Results show that current flow estimations derived at 0.5, 1.0 and 1.5 m resolutions of MBES bathymetry agree with previous in situ current speed measurements. Moreover, sediment grain size estimations produced during the ARA of the 400 kHz backscatter data displayed a clear pattern of sediment resuspension at each section of the harbour, and this was effectively depicted at 0.5, 1.0, and 1.5 m resolution of MBES bathymetry data. However, current flow estimates derived from 2.0 and 2.5 m resolution failed to portray the short-term current variation induced by the ebb phase of the tide. Therefore, it is recommended that MBES data with a pixel size of >50 % of the wavelength of the SSB observed is insufficient for inclusion in an OBIA workflow.

关键词： object-based image analysis Seabed sedimentary bedforms Geomorphology Spatial resolution Current velocities

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A DETERMINATION OF THE EARTHQUAKE DISASTER AREA BY object-based analysis USING A SINGLE SATELLITE image 37

A DETERMINATION OF THE EARTHQUAKE DISASTER AREA BY OBJECT-BA...

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IEEE International Geoscience & Remote Sensing Symposium

作者： Park, Jonggeol Harada, Ippei Kwak, Youngjoo Tokyo Univ Informat Sci Wakaba Ku 4-1 Onaridai Chiba 2658501 Japan Int Ctr Water Hazard & Risk Management Tsukuba Ibaraki 3058516 Japan

ISBN: (纸本)9781509049516

The final goal of this study is to create data to support emergency efforts in a disaster affected area by locating damaged buildings shortly after the disaster. In this study, prioritizing the practicality of the method for emergency purposes, we designed a method only to use a single satellite image of an affected area, eliminating the use of complex algorithms and auxiliary data. The uniqueness of our method lies in the application of an object-based region segmentation to images and the use of features of objects obtained from texture, hierarchical and other information in order to extract damaged buildings. Out of 26 features resulting from the analysis of objects, we found one feature and three combinations of two different features that are effective in extracting damaged buildings, such as Rectangular fit, Homogeneity, Number of sub-objects/Area, and Length of longest of edge/Area.

关键词： Earthquake disaster area object-based image analysis GeoEye-1

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object based and geo-spatial image analysis: a semi-automatic pre-operational system

Object based and geo-spatial image analysis: a semi-automati...

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Conference on image and Signal Processing for Remote Sensing XVI

作者： Michel, Julien Inglada, Jordi Malik, Julien CNES DCT SI AP 18 Av E Belin F-31401 Toulouse 9 France CNES CESBIO F-31401 Toulouse 9 France CS IGI F-31506 Toulouse 5 France

ISBN: (纸本)9780819483478

High resolution optical remote sensing images allow to produce accurate land-cover maps. This is usually achieved using an ad-hoc mixture of image segmentation and supervised classification. The main drawback of this approach is that it does not scale for real world complete scenes. In this paper we present a framework which allows to implement this kind of image analysis without scale issues.

关键词： Land-cover maps object-based image analysis Geographical databases

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