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object-based image analysis of WORLDVIEW-2 SATELLITE DATA for the CLASSIFICATION of MANGROVE AREAS in the CITY of SÃO LUÍS, MARANHÃO STATE, BRAZIL 22

OBJECT-BASED IMAGE ANALYSIS of WORLDVIEW-2 SATELLITE DATA fo...

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

作者： Kux, H.J.H. Souza, U.D.V. INPE Andndash National Institute for Space Research Avenida Dos Astronautas 1758 andndashJardim da Granja São José dos Campos andndashSP12.227-010 Brazil

Taking into account the importance of mangrove environments for the biodiversity of coastal areas, the objective of this paper is to classify the different types of irregular human occupation on the areas of mangrove vegetation in São Luis, capital of Maranhão State, Brazil, considering the OBIA (object-based image analysis) approach with WorldView-2 satellite data and using Interimage, a free image analysis software. A methodology for the study of the area covered by mangroves at the northern portion of the city was proposed to identify the main targets of this area, such as: marsh areas (known locally as Apicum), mangrove forests, tidal channels, blockhouses (irregular constructions), embankments, paved streets and different condominiums. Initially a databank including information on the main types of occupation and environments was established for the area under study. An image fusion (multispectral bands with panchromatic band) was done, to improve the information content of WorldView-2 data. Following an ortho-rectification was made with the dataset used, in order to compare with cartographical data from the municipality, using Ground Control Points (GCPs) collected during field survey. Using the data mining software GEODMA, a series of attributes which characterize the targets of interest was established. Afterwards the classes were structured, a knowledge model was created and the classification performed. The OBIA approach eased mapping of such sensitive areas, showing the irregular occupations and embankments of mangrove forests, reducing its area and damaging the marine biodiversity.

关键词： High-resolution images Highresolution images Irregular occupation Mangrove object-based image analysis Urban area. WorldView-2

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Incorporating DeepLabv3+and object-based image analysis for semantic segmentation of very high resolution remote sensing images

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International Journal of Digital Earth 2021年第3期14卷 357-378页

作者： Shouji Du Shihong Du Bo Liu Xiuyuan Zhang Institute of Remote Sensing and GIS Peking UniversityBeijingPeople’s Republic of China

Semantic segmentation of remote sensing images is an important but unsolved problem in the remote sensing *** image semantic segmentation models,such as DeepLabv3+,have achieved astonishing performance for semantically labeling very high resolution(VHR)remote sensing ***,it is difficult for these models to capture the precise outlines of ground objects and explore the context information that revealing relationships among image objects for optimizing segmentation ***,this study proposes a semantic segmentation method for VHR images by incorporating deep learning semantic segmentation model(DeepLabv3+)and objectbased image analysis(OBIA),wherein DSM is employed to provide geometric information to enhance the interpretation of VHR *** proposed method first obtains two initial probabilistic labeling predictions using a DeepLabv3+network on spectral image and a random forest(RF)classifier on hand-crafted features,*** two predictions are then integrated by Dempster-Shafer(D-S)evidence theory to be fed into an object-constrained higher-order conditional random field(CRF)framework to estimate the final semantic labeling results with the consideration of the spatial contextual *** proposed method is applied to the ISPRS 2D semantic labeling benchmark,and competitive overall accuracies of 90.6%and 85.0%are achieved for Vaihingen and Potsdam datasets,respectively.

关键词： Semantic segmentation DeepLabv3+ object-based image analysis DempsterShafer evidence theory conditional random field VHR images

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Integrated object-based image analysis for semi-automated geological lineament detection in southwest England

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COMPUTERS & GEOSCIENCES 2019年 123卷 137-148页

作者： Yeomans, Christopher M. Middleton, Maarit Shail, Robin K. Grebby, Stephen Lusty, Paul A. J. Univ Exeter CEMPS Camborne Sch Mines Penryn Campus Penryn TR10 9FE Cornwall England British Geol Survey Ctr Environm Sci Keyworth NG12 5GG Notts England Geol Survey Finland POB 77 FI-96101 Rovaniemi Finland Univ Nottingham Nottingham Geospatial Inst Innovat Pk Nottingham NG7 2TU England

Regional lineament detection for mapping of geological structure can provide crucial information for mineral exploration. Manual methods of lineament detection are time consuming, subjective and unreliable. The use of semi-automated methods reduces the subjectivity through applying a standardised method of searching. object based image analysis (OBIA) has become a mainstream technique for landcover classification, however, the use of OBIA methods for lineament detection is still relatively under-utilised. The Southwest England region is covered by high-resolution airborne geophysics and LiDAR data that provide an excellent opportunity to demonstrate the power of OBIA methods for lineament detection. Herein, two complementary but stand-alone OBIA methods for lineament detection are presented which both enable semi-automatic regional lineament mapping. Furthermore, these methods have been developed to integrate multiple datasets to create a composite lineament network. The top-down method uses threshold segmentation and sub-levels to create objects, whereas the bottom-up method segments the whole image before merging objects and refining these through a border assessment. Overall lineament lengths are longest when using the top-down method which also provides detailed metadata on the source dataset of the lineament. The bottom-up method is more objective and computationally efficient and only requires user knowledge to classify lineaments into major and minor groups. Both OBIA methods create a similar network of lineaments indicating that semi-automatic techniques are robust and consistent. The integration of multiple datasets from different types of spatial data to create a comprehensive, composite lineament network is an important development and demonstrates the suitability of OBIA methods for enhancing lineament detection.

关键词： object-based image analysis Airborne geophysics Lineament detection Semi-automated Southwest England

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Integrated approach using data mining-based decision tree and object-based image analysis for high-resolution urban mapping of WorldView-2 satellite sensor data

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JOURNAL OF APPLIED REMOTE SENSING 2016年第2期10卷

作者： Hamedianfar, Alireza Shafri, Helmi Zulhaidi Mohd Univ Putra Malaysia Fac Engn Dept Civil Engn Serdang 43400 Selangor Malaysia Islamic Azad Univ Dept Surveying Engn Estahban Branch Estahban Fars Iran Islamic Azad Univ Young Researchers & Elite Club Estahban Branch Estahban Fars Iran Univ Putra Malaysia Fac Engn GISRC Serdang 43400 Selangor Malaysia

This paper integrates decision tree-based data mining (DM) and object-based image analysis (OBIA) to provide a transferable model for the detailed characterization of urban land-cover classes using WorldView-2 (WV-2) satellite images. Many articles have been published on OBIA in recent years based on DM for different applications. However, less attention has been paid to the generation of a transferable model for characterizing detailed urban land cover features. Three subsets of WV-2 images were used in this paper to generate transferable OBIA rule-sets. Many features were explored by using a DM algorithm, which created the classification rules as a decision tree (DT) structure from the first study area. The developed DT algorithm was applied to object-based classifications in the first study area. After this process, we validated the capability and transferability of the classification rules into second and third subsets. Detailed ground truth samples were collected to assess the classification results. The first, second, and third study areas achieved 88%, 85%, and 85% overall accuracies, respectively. Results from the investigation indicate that DM was an efficient method to provide the optimal and transferable classification rules for OBIA, which accelerates the rule-sets creation stage in the OBIA classification domain. (C) 2016 Society of Photo-Optical Instrumentation Engineers (SPIE)

关键词： data mining object-based image analysis remote sensing urban mapping WorldView-2 image

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Discrepancy measures for selecting optimal combination of parameter values in object-based image analysis

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ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING 2012年第1期68卷 144-156页

作者： Liu, Yong Bian, Ling Meng, Yuhong Wang, Huanping Zhang, Shifu Yang, Yining Shao, Xiaomin Wang, Bo Lanzhou Univ Coll Earth & Environm Sci Lanzhou 730000 Peoples R China SUNY Buffalo Dept Geog Amherst NY 14261 USA

Most object-based image analysis use parameters to control the size, shape, and homogeneity of segments. Because each parameter may take a range of possible values, different combinations of value between parameters may produce different segmentation results. Assessment of segmentation quality, such as the discrepancy between reference polygons and corresponding image segments, can be used to identify the optimal combination of parameter values. In this research, we (1) evaluate four existing indices that describe the discrepancy between reference polygons and corresponding segments, (2) propose three new indices to evaluate both geometric and arithmetic discrepancies, and (3) compare the effectiveness of the existing and proposed indices in identifying optimal combinations of parameter values for image segmentation through a case study. A Landsat 5 Thematic Mapper (TM) image and an ALOS image of arid Northwestern China were used in the case study. The four existing indices include Quality Rate (QR), Over-segmentation Rate (OR), Under-segmentation Rate (UR), and Euclidean Distance 1 (ED1). The three proposed discrepancy indices include Potential Segmentation Error (PSE), Number-of-Segments Ratio (NSR), and Euclidean Distance 2 (ED2). These indices measure overlap, over-segmentation, and under-segmentation between reference polygons and corresponding image segments. Results show that the three proposed indices PSE, NSR, and ED2 are more effective than the four existing indices QR, OR, UR, and ED1 in their ability to identify optimal combinations of parameter values. ED2 that represents both geometric (PSE) and arithmetic (NSR) discrepancies is most effective. (C) 2012 International Society for Photogrammetry and Remote Sensing, Inc. (ISPRS) Published by Elsevier B.V. All rights reserved.

关键词： object-based image analysis image segmentation Discrepancy measures Optimal parameter value combinations Under-segmentation Over-segmentation

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Automated spine and vertebrae detection in CT images using object-based image analysis

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INTERNATIONAL JOURNAL FOR NUMERICAL METHODS IN BIOMEDICAL ENGINEERING 2013年第9期29卷 938-963页

作者： Schwier, M. Chitiboi, T. Huelnhagen, T. Hahn, H. K. Fraunhofer MEVIS Inst Med Image Comp Bremen Germany

Although computer assistance has become common in medical practice, some of the most challenging tasks that remain unsolved are in the area of automatic detection and recognition. The human visual perception is in general far superior to computer vision algorithms. object-based image analysis is a relatively new approach that aims to lift image analysis from a pixel-based processing to a semantic region-based processing of images. It allows effective integration of reasoning processes and contextual concepts into the recognition method. In this paper, we present an approach that applies object-based image analysis to the task of detecting the spine in computed tomography images. A spine detection would be of great benefit in several contexts, from the automatic labeling of vertebrae to the assessment of spinal pathologies. We show with our approach how region-based features, contextual information and domain knowledge, especially concerning the typical shape and structure of the spine and its components, can be used effectively in the analysis process. The results of our approach are promising with a detection rate for vertebral bodies of 96% and a precision of 99%. We also gain a good two-dimensional segmentation of the spine along the more central slices and a coarse three-dimensional segmentation. Copyright (c) 2013 John Wiley & Sons, Ltd.

关键词： spine detection vertebrae detection object-based image analysis

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Assessing and monitoring semi-arid shrublands using object-based image analysis and multiple endmember spectral mixture analysis

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ENVIRONMENTAL MONITORING AND ASSESSMENT 2013年第4期185卷 3173-3190页

作者： Hamada, Yuki Stow, Douglas A. Roberts, Dar A. Franklin, Janet Kyriakidis, Phaedon C. Argonne Natl Lab Argonne IL 60439 USA San Diego State Univ Dept Geog San Diego CA 92182 USA Univ Calif Santa Barbara Dept Geog Santa Barbara CA 93106 USA Arizona State Univ Sch Geog Sci & Urban Planning Tempe AZ 85287 USA Univ Aegean Dept Geog Mitilini Greece

Arid and semi-arid shrublands have significant biological and economical values and have been experiencing dramatic changes due to human activities. In California, California sage scrub (CSS) is one of the most endangered plant communities in the US and requires close monitoring in order to conserve this important biological resource. We investigate the utility of remote-sensing approaches-object-based image analysis applied to pansharpened QuickBird imagery (QBPS/OBIA) and multiple endmember spectral mixture analysis (MESMA) applied to SPOT imagery (SPOT/MESMA)-for estimating fractional cover of true shrub, subshrub, herb, and bare ground within CSS communities of southern California. We also explore the effectiveness of life-form cover maps for assessing CSS conditions. Overall and combined shrub cover (i.e., true shrub and subshrub) were estimated more accurately using QBPS/OBIA (mean absolute error or MAE, 8.9 %) than SPOT/MESMA (MAE, 11.4 %). Life-form cover from QBPS/OBIA at a 25 x 25 m grid cell size seems most desirable for assessing CSS because of its higher accuracy and spatial detail in cover estimates and amenability to extracting other vegetation information (e.g., size, shape, and density of shrub patches). Maps derived from SPOT/MESMA at a 50 x 50 m scale are effective for retrospective analysis of life-form cover change because their comparable accuracies to QBPS/OBIA and availability of SPOT archives data dating back to the mid-1980s. The framework in this study can be applied to other physiognomically comparable shrubland communities.

关键词： Remote sensing Habitat monitoring Shrublands Mixture models object-based image analysis

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Combining data mining algorithm and object-based image analysis for detailed urban mapping of hyperspectral images

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JOURNAL OF APPLIED REMOTE SENSING 2014年第1期8卷

作者： Hamedianfar, Alireza Shafri, Helmi Zulhaidi Mohd Mansor, Shattri Ahmad, Noordin Univ Putra Malaysia Dept Civil Engn Fac Engn Serdang 43400 Selangor Malaysia Univ Putra Malaysia GISRC Fac Engn Serdang 43400 Selangor Malaysia Natl Space Agcy Bangunan Komersil PJH Putrajaya 62570 Malaysia

image classification of roofing types, road pavements, and natural features can assist land-cover maps in further examining the effects of such features on health, pollution, and the microclimate in urban settings. Airborne hyperspectral sensors with high spectral and spatial resolutions can be employed for detailed characterization of urban areas. This study aims to develop a procedure that is instrumental for automated knowledge discovery and mapping of urban surface materials from a large feature space of hyperspectral images. Two different images over Universiti Putra Malaysia (UPM) and Kuala Lumpur (KL), Malaysia, were captured by using hyperspectral sensors with 20 and 128 bands. The images were used to explore the combined performance of a data mining (DM) algorithm and object-based image analysis (OBIA). A large number of attributes were discovered with the C4.5 DM algorithm, which also generated the classification model as a decision tree. The UPM and KL classified images achieved 93.42 and 88.36% overall accuracy. The high accuracy of object-based classification can be linked to the knowledge discovery produced by the DM algorithm. This algorithm increased the productivity of OBIA, expedited the process of attribute selection, and resulted in an easy-to-use representation of a knowledge model from a decision tree structure. (C) 2014 Society of Photo-Optical Instrumentation Engineers (SPIE)

关键词： data mining object-based image analysis urban area hyperspectral imagery urban mapping

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OBIA4RTM-towards an operational open-source solution for coupling object-based image analysis with radiative transfer modelling

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EUROPEAN JOURNAL OF REMOTE SENSING 2021年第sup1期54卷 59-70页

作者： Graf, Lukas Papp, Levente Lang, Stefan Univ Salzburg Dept Geoinformat Z GIS Salzburg Austria

Radiative transfer models (RTM) provide universally applicable, highly accurate prospects for plant parameter retrieval. Due to the ill-posed nature of radiative transfer theory, however, the retrieval of plant parameters requires sophisticated strategies for model inversion. We argue that object-based image analysis (OBIA) works as an effective regularization measure to cope with this ill-posedness. Despite similar findings reported in the literature, OBIA and RTM are rarely used in a combined manner. Additionally, there is a clear lack of software solutions ready for operational usage. Therefore, we propose OBIA4RTM as an approach to combine OBIA and RTM using Python and PostgreSQL/PostGIS spatial databases in a fully Open Geospatial Consortium (OGC) compliant way. First results obtained in agricultural regions in southern Germany and Austria using Sentinel-2 data during the 2017 and 2018 growing season show root mean squared errors (RMSE) in the leaf area index (LAI) of 1.47 m(2)/m(2) in the case of silage maize and 1.31 m(2)/m(2) in the case of winter cereals. Issues of integrating space and time as well as defining appropriate validation strategies, however, require further research.

关键词： Vegetation parameters object-based image analysis radiative transfer modelling lookup-table based inversion open-source software leaf area index

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Comparison of pixel- and object-based image analysis for tea plantation mapping using hyperspectral Gaofen-5 and synthetic aperture radar data

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JOURNAL OF APPLIED REMOTE SENSING 2020年第4期14卷

作者： Chen, Yujia Tian, Shufang China Univ Geosci Beijing Sch Earth Sci & Resources Beijing Peoples R China

Accurately mapping tea plantation distribution is crucial to environmental protection and sustainable development. Hyperspectral and synthetic aperture radar (SAR) data have recently been widely used in land cover classification, but their ability to extract tea plantation regions still needs to be confirmed. Compared with traditional pixel-based image analysis (PBIA), object-based image analysis (OBIA) for tea plantation mapping is more worthy of implementation. This study explored the performance of Gaofen-5 (GF-5) and copolarized SAR data for tea plantation mapping using pixel- and object-based support vector machine algorithms in Wuyishan, China. Comparison of PBIA and OBIA demonstrated the significant differences in visual effect and classification accuracy. The object-based classifications especially offered a more contiguous depiction with fewer speckles of tea plantations than pixel-based classifications did. Moreover, object-based classifications improved overall accuracy (OA) between 1.7% and 7.9% in all scenarios when compared to pixel-based classifications. As for datasets, classifications using only GF-5 data obtained an OA of over 85%, while fusing images decreased classification accuracy due to the lower separability between tea and forest, showcasing that the fusion of hyperspectral and SAR data does not guarantee the improvement of classification accuracy. The integration of GF-5, horizontal transmit and horizontal receive (HH), and vertical transmit and vertical receive (VV) polarized data outperformed other data combinations in both pixel- and object-based classifications and achieved the highest OA (95.58%) in OBIA, with a 98.13% producer accuracy and 93.34% user accuracy of tea plantations. The results indicated OBIA could overcome the shortcomings of the PBIA and effectively improve the mapping accuracy of tea plantation, and OBIA integrating GF-5, HH, and VV polarized data could play a distinguished role in tea plantation mapping. This work pr

关键词： tea plantation mapping hyperspectral copolarized data image fusion object-based image analysis

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