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Geographic object-based image analysis: A Primer and Future Directions

REMOTE SENSING 2020年第12期12卷 2012-2012页

作者： Kucharczyk, Maja Hay, Geoffrey J. Ghaffarian, Salar Hugenholtz, Chris H. Univ Calgary Dept Geog Calgary AB T2N 1N4 Canada

Geographic object-based image analysis (GEOBIA) is a remote sensing image analysis paradigm that defines and examines image-objects: groups of neighboring pixels that represent real-world geographic objects. Recent reviews have examined methodological considerations and highlighted how GEOBIA improves upon the 30+ year pixel-based approach, particularly for H-resolution imagery. However, the literature also exposes an opportunity to improve guidance on the application of GEOBIA for novice practitioners. In this paper, we describe the theoretical foundations of GEOBIA and provide a comprehensive overview of the methodological workflow, including: (i) software-specific approaches (open-source and commercial);(ii) best practices informed by research;and (iii) the current status of methodological research. Building on this foundation, we then review recent research on the convergence of GEOBIA with deep convolutional neural networks, which we suggest is a new form of GEOBIA. Specifically, we discuss general integrative approaches and offer recommendations for future research. Overall, this paper describes the past, present, and anticipated future of GEOBIA in a novice-accessible format, while providing innovation and depth to experienced practitioners.

关键词： geographic object-based image analysis GEOBIA object-based image analysis OBIA machine learning deep learning convolutional neural network CNN GEOCNN

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Mapping of shallow landslides with object-based image analysis from unmanned aerial vehicle data

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ENGINEERING GEOLOGY 2019年 260卷 105264-000页

作者： Comert, Resul Avdan, Ugur Gorum, Tolga Nefeslioglu, Hakan A. Gumushane Univ Dept Geomat Engn Gumushane Turkey Eskisehir Tech Univ Inst Earth & Space Sci Tepebasi Turkey Istanbul Tech Univ Eurasia Inst Earth Sci Istanbul Turkey Hacettepe Univ Dept Geol Engn Ankara Turkey

The Black Sea Region of Turkey is one of the most landslide prone areas due to its high slope topography, heavy rainfall, and highly weathered hillslope material conditions. Preparation of landslide inventory maps is the first step in producing landslide susceptibility maps. Ground-based methods for mapping landslide occurrences are time-consuming and expensive. Additionally, landslide mapping based on satellite imageries and aerial photographs has some limitations, including climatic conditions, cost, and limited repetitive measurement capacity. Visual interpretation-based landslide mapping, which is based on satellite imageries and aerial photographs, is a time-consuming procedure that requires an experience-based expert opinion. Therefore, the data acquisition based on unmanned aerial vehicle (UAV) and landslide event inventory maps using an object-based classification approach can be superior to other methods in terms of speed and cost. In this study, we developed a semiautomatic model using object-based image analyses for rapid mapping of shallow landslides from the data obtained from UAVs after major landslide events in the Black Sea Region of Turkey. For this purpose, two test sites-Kurucasile (Bartin) and Cayeli (Rize)-were selected. Landslide mapping models were developed in the investigation sites, and the performance of the models was evaluated. The landslides' data obtained with the developed models were compared to the landslides' data produced by the experts. The comparison process revealed that landslides mapped by using UAV data have an accuracy rate higher than 86% according to the number of landslides and 83% according to the landslide area.

关键词： object-based image analysis UAV Landslide Kurucasile (Bartin) Cayeli (Rite)

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Adjusting Emergent Herbaceous Wetland Elevation with object-based image analysis, Random Forest and the 2016 NLCD

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REMOTE SENSING 2019年第20期11卷 2346-2346页

作者： Munoz, David E. Cissell, Jordan R. Moftakhari, Hamed Univ Alabama Dept Civil Construct & Environm Engn Tuscaloosa AL 35487 USA Univ Alabama Dept Geog Tuscaloosa AL 35487 USA Univ Alabama Ctr Complex Hydrosyst Res Tuscaloosa AL 35487 USA

Emergent herbaceous wetlands are characterized by complex salt marsh ecosystems that play a key role in diverse coastal processes including carbon storage, nutrient cycling, flood attenuation and shoreline protection. Surface elevation characterization and spatiotemporal distribution of these ecosystems are commonly obtained from LiDAR measurements as this low-cost airborne technique has a wide range of applicability and usefulness in coastal environments. LiDAR techniques, despite significant advantages, show poor performance in generation of digital elevation models (DEMs) in tidal salt marshes due to large vertical errors. In this study, we present a methodology to (i) update emergent herbaceous wetlands (i.e., the ones delineated in the 2016 National Land Cover Database) to present-day conditions;and (ii) automate salt marsh elevation correction in estuarine systems. We integrate object-based image analysis and random forest technique with surface reflectance Landsat imagery to map three emergent U.S. wetlands in Weeks Bay, Alabama, Savannah Estuary, Georgia and Fire Island, New York. Conducting a hyperparameter tuning of random forest and following a hierarchical approach with three nomenclature levels for land cover classification, we are able to better map wetlands and improve overall accuracies in Weeks Bay (0.91), Savannah Estuary (0.97) and Fire Island (0.95). We then develop a tool in ArcGIS to automate salt marsh elevation correction. We use this 'DEM-correction' tool to modify an existing DEM (model input) with the calculated elevation correction over salt marsh regions. Our method and tool are validated with real-time kinematic elevation data and helps correct overestimated salt marsh elevation up to 0.50 m in the studied estuaries. The proposed tool can be easily adapted to different vegetation species in wetlands, and thus help provide accurate DEMs for flood inundation mapping in estuarine systems.

关键词： National Land Cover Database object-based image analysis random forest salt marsh Savannah Estuary Weeks Bay Fire Island

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Improving Machine Learning Classifications of Phragmites australis Using object-based image analysis

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REMOTE SENSING 2023年第4期15卷 989页

作者： Anderson, Connor J. Heins, Daniel Pelletier, Keith C. Knight, Joseph F. Univ Minnesota Dept Forest Resources 1530 Cleveland Ave N St Paul MN 55108 USA

Uncrewed aircraft systems (UASs) are a popular tool when surveilling for invasive alien plants due to their high spatial and temporal resolution. This study investigated the efficacy of a UAS equipped with a three-band (i.e., red, green, blue;RGB) sensor to identify invasive Phragmites australis in multiple Minnesota wetlands using object-based image analysis (OBIA) and machine learning (ML) algorithms: artificial neural network (ANN), random forest (RF), and support vector machine (SVM). The addition of a post-ML classification OBIA workflow was tested to determine if ML classifications can be improved using OBIA techniques. Results from each ML algorithm were compared across study sites both with and without the post-ML OBIA workflow. ANN was identified as the best classifier when not incorporating a post-ML OBIA workflow with a classification accuracy of 88%. Each of the three ML algorithms achieved a classification accuracy of 91% when incorporating the post-ML OBIA workflow. Results from this study suggest that a post-ML OBIA workflow can increase the ability of ML algorithms to accurately identify invasive Phragmites australis and should be used when possible. Additionally, the decision of which ML algorithm to use for Phragmites mapping becomes less critical with the addition of a post-ML OBIA workflow.

关键词： Phragmites australis UAS invasive species object-based image analysis machine learning

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Urban Area Mapping Using Multitemporal SAR images in Combination with Self-Organizing Map Clustering and object-based image analysis

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REMOTE SENSING 2023年第1期15卷 122-122页

作者： Amitrano, Donato Di Martino, Gerardo Iodice, Antonio Riccio, Daniele Ruello, Giuseppe Italian Aerosp Res Ctr Via Maiorise snc I-81043 Capua Italy Univ Napoli Federico II Dept Elect Engn & Informat Technol Via Claudio 21 I-80125 Naples Italy

Mapping urban areas from space is a complex task involving the definition of what should be considered as part of an urban agglomerate beyond the built-up features, thus modelling the transition of a city into the surrounding landscape. In this paper, a new technique to map urban areas using multitemporal synthetic aperture radar data is presented. The proposed methodology exploits innovative RGB composites in combination with self-organizing map (SOM) clustering and object-based image analysis. In particular, the clustered product is used to extract a coarse urban area map, which is then refined using object-based processing. In this phase, Delaunay triangulation and the spatial relationship between the identified urban regions are used to model the urban-rural gradient between a city and the surrounding landscape. The technique has been tested in different scenarios representative of structurally different cities in Italy and Germany. The quality of the obtained products is assessed by comparison with the Urban Atlas of the European Environmental Agency, showing good agreement with the adopted reference data despite their different taxonomies.

关键词： synthetic aperture radar urban area object-based image analysis self-organizing maps classification mapping

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Advances of Local Climate Zone Mapping and Its Practice Using object-based image analysis

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ATMOSPHERE 2021年第9期12卷 1146页

作者： Ma, Lei Zhu, Xiaoxiang Qiu, Chunping Blaschke, Thomas Li, Manchun Nanjing Univ Jiangsu Prov Key Lab Geog Informat Sci & Technol Key Lab Land Satellite Remote Sensing Applicat Minist Nat ResourcesSch Geog & Ocean Sci Nanjing 210023 Peoples R China Tech Univ Munich TUM Signal Proc Earth Observat D-80333 Munich Germany German Aerosp Ctr DLR Remote Sensing Technol Inst D-82234 Wessling Germany Univ Salzburg Dept Geoinformat Z GIS A-5020 Salzburg Austria

In the context of climate change and urban heat islands, the concept of local climate zones (LCZ) aims for consistent and comparable mapping of urban surface structure and cover across cities. This study provides a timely survey of remote sensing-based applications of LCZ mapping considering the recent increase in publications. We analyze and evaluate several aspects that affect the performance of LCZ mapping, including mapping units/scale, transferability, sample dataset, low accuracy, and classification schemes. Since current LCZ analysis and mapping are based on per-pixel approaches, this study implements an object-based image analysis (OBIA) method and tests it for two cities in Germany using Sentinel 2 data. A comparison with a per-pixel method yields promising results. This study shall serve as a blueprint for future object-based remotely sensed LCZ mapping approaches.

关键词： local climate zones remote sensing mapping unit transferability object-based image analysis

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Identifying Vegetation in Arid Regions Using object-based image analysis with RGB-Only Aerial imagery

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REMOTE SENSING 2019年第19期11卷 2308-2308页

作者： Silver, Micha Tiwari, Arti Karnieli, Arnon Ben Gurion Univ Negev Jacob Blaustein Inst Desert Res Remote Sensing Lab IL-84105 Beer Sheva Israel

Vegetation state is usually assessed by calculating vegetation indices (VIs) derived from remote sensing systems where the near infrared (NIR) band is used to enhance the vegetation signal. However VIs are pixel-based and require both visible and NIR bands. Yet, most archived photographs were obtained with cameras that record only the three visible bands. Attempts to construct VIs with the visible bands alone have shown only limited success, especially in drylands. The current study identifies vegetation patches in the hyperarid Israeli desert using only the visible bands from aerial photographs by adapting an alternative geospatial object-based image analysis (GEOBIA) routine, together with recent improvements in preprocessing. The preprocessing step selects a balanced threshold value for image segmentation using unsupervised parameter optimization. Then the images undergo two processes: segmentation and classification. After tallying modeled vegetation patches that overlap true tree locations, both true positive and false positive rates are obtained from the classification and receiver operating characteristic (ROC) curves are plotted. The results show successful identification of vegetation patches in multiple zones from each study area, with area under the ROC curve values between 0.72 and 0.83.

关键词： segmentation classification vegetation arid regions gray-level co-occurrence matrix texture object-based image analysis threshold optimization

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Landslide mapping using object-based image analysis and open source tools

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ENGINEERING GEOLOGY 2021年 282卷 106000-106000页

作者： Amatya, Pukar Kirschbaum, Dalia Stanley, Thomas Tanyas, Hakan Univ Space Res Assoc Columbia MD 21046 USA Goddard Earth Sci Technol & Res Columbia MD USA NASA Goddard Space Flight Ctr Hydrol Sci Lab Greenbelt MD USA Univ Twente Fac Geoinformat Sci & Earth Observat ITC Enschede Netherlands

Availability of high-resolution optical imagery and advances in image processing technologies have significantly improved our ability to map landslides. In recent years object-based image analysis (OBIA) has been gaining in popularity for landslide mapping due to its ability to incorporate spectral, textural, morphological and topographical properties. Many studies have been conducted based on commercial software. In this study, we create an open source Semi-Automatic Landslide Detection (SALaD) system utilizing OBIA and machine learning. Configured to run in Linux environment, it uses various open source Python packages and modules. This system was tested in 575 km(2) area along the Pasang Lhamu Highway, Nepal where large numbers of landslides were triggered by the 2015 Gorkha earthquake. Comparison with a manual inventory highlighted that this system was able to detect 70% of the landslide area. The speed and efficiency with which this system was able to detect landslides makes it a viable alternative to manual techniques for landslide mapping over large areas, when establishing approximate landslide locations is of prime importance.

关键词： Open source object-based image analysis Landslides RapidEye Python Nepal

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Using Unmanned Aerial Systems (UAS) and object-based image analysis (OBIA) for Measuring Plant-Soil Feedback Effects on Crop Productivity

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DRONES 2019年第3期3卷 54-54页

作者： Nuijten, Rik J. G. Kooistra, Lammert De Deyn, Gerlinde B. Univ Utrecht Fac Geosci POB 80-115 NL-3508 TC Utrecht Netherlands Univ British Columbia Fac Forestry 2424 Main Mall Vancouver BC V6T IZ4 Canada Wageningen Univ & Res Lab Geoinformat Sci & Remote Sensing POB 47 NL-6700 AA Wageningen Netherlands Wageningen Univ & Res Soil Biol Grp Environm Sci POB 47 NL-6700 AA Wageningen Netherlands

Unmanned aerial system (UAS) acquired high-resolution optical imagery and object-based image analysis (OBIA) techniques have the potential to provide spatial crop productivity information. In general, plant-soil feedback (PSF) field studies are time-consuming and laborious which constrain the scale at which these studies can be performed. Development of non-destructive methodologies is needed to enable research under actual field conditions and at realistic spatial and temporal scales. In this study, the influence of six winter cover crop (WCC) treatments (monocultures Raphanus sativus, Lolium perenne, Trifolium repens, Vicia sativa and two species mixtures) on the productivity of succeeding endive (Cichorium endivia) summer crop was investigated by estimating crop volume. A three-dimensional surface and terrain model were photogrammetrically reconstructed from UAS imagery, acquired on 1 July 2015 in Wageningen, the Netherlands. Multi-resolution image segmentation (MIRS) and template matching algorithms were used in an integrated workflow to detect individual crops (accuracy = 99.8%) and delineate C. endivia crop covered area (accuracy = 85.4%). Mean crop area (R = 0.61) and crop volume (R = 0.71) estimates had strong positive correlations with in situ measured dry biomass. Productivity differences resulting from the WCC treatments were greater for estimated crop volume in comparison to in situ biomass, the legacy of Raphanus was most beneficial for estimated crop volume. The perennial ryegrass L. perenne treatment resulted in a significantly lower production of C. endivia. The developed workflow has potential for PSF studies as well as precision farming due to its flexibility and scalability. Our findings provide insight into the potential of UAS for determining crop productivity on a large scale.

关键词： remote sensing unmanned aerial systems object-based image analysis plant-soil feedback plant productivity template matching segmentation precision agriculture

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The possibilities to support ZB GIS®database update using object-based image analysis in ecognition developer software

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Geodezija ir Kartografija 2014年第3期40卷 122-132页

The process of updating a spatial database is a necessary part of database management in order to keep the stored information of an acceptable quality. The first step of database update requires change detection. Many methods have been suggested to detect changes, mostly pixel-based. Recently, with the spread of very high resolution images and object-based image analysis, object-based methods were developed, too. This article presents object-based change detection method for update of the vector database ZB GIS®, that is a geometric base of the Slovak National infrastructure of spatial information, using orthophoto of the area of interest. The method stages include the following: segmentation of orthophoto using the geometry of database objects and further according to spectral and spatial information, classification according to ZB GIS®, defining reclassification rules between two classes. The proposed method was tested in two localities – Malženice (agricultural landscape) and Chopok-Jasná (mountainous landscape) reaching the overall accuracy of classification 87.12% and 84.55%, respectively. The main limitation of the method is that it can be applied only for polygonal objects.

关键词： remote sensing change detection object-based image analysis object-based classification spatial database database update

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