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object-based image analysis for extracting regional slope units and its application

EARTH SCIENCE INFORMATICS 2024年第5期17卷 4575-4595页

作者： Zeng, Ying Zhang, Yingbin Liu, Jing Zhu, Hui Feng, Zhenhai Sun, Yu Southwest Jiaotong Univ State Key Lab Intelligent Geotech & Tunnelling Chengdu 610031 Peoples R China

The selection and quality of the evaluation unit directly affect the rationality of the evaluation results of regional landslides, in which the slope unit as the evaluation unit can better describe the landform characteristics and topographic changes, and can better reflect the natural environmental conditions of landslides. However, there is currently no optimization method for slope unit partitioning, which restricts the application of slope units in regional landslide disaster evaluation. Therefore, this paper proposes a R-MSS (Refined Multi-Scale segmentation) method based on object-based image analysis (OBIA). Typical mountainous areas in southwest China and hilly areas in southeast China were selected and compared using hydrological analysis, curvature watershed, and MSS methods. The results show that the R-MSS method can effectively extract large-area slope units without a lot of manual modification. The divided slope units have uniform size, small spatial difference and good morphological effect. The overall effect is significantly better than other methods, especially in the high and steep mountainous areas with complex and changeable terrain, its segmentation performance is especially outstanding. The research results can serve for regional landslide hazard evaluation and improve the reliability of regional landslide hazard analysis results to a certain extent.

关键词： Slope units object-based image analysis Multi-scale segmentation Digital elevation model

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Assessing Lightweight Folding UAV Reliability Through a Photogrammetric Case Study: Extracting Urban Village's Buildings Using object-based image analysis (OBIA) Method

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DRONES 2025年第2期9卷 101-101页

作者： Kuang, Junyu Chen, Yingbiao Ling, Zhenxiang Meng, Xianxin Chen, Wentao Zheng, Zihao Guangzhou Univ Sch Geog Sci & Remote Sensing Guangzhou 510006 Peoples R China Guangzhou Univ Inst Aerosp Remote Sensing Innovat ARSI Guangzhou 510006 Peoples R China Guangzhou Univ Huangpu Res Sch Guangzhou 511363 Peoples R China

With the rapid advancement of drone technology, modern drones have achieved high levels of functional integration, alongside structural improvements that include lightweight, compact designs with foldable features, greatly enhancing their flexibility and applicability in photogrammetric applications. Nevertheless, limited research currently explores data collected by such compact UAVs, and whether they can balance a small form factor with high data quality remains uncertain. To address this challenge, this study acquired the remote sensing data of a peri-urban area using the DJI Mavic 3 Enterprise and applied object-based image analysis (OBIA) to extract high-density buildings. It was found that this drone offers high portability, a low operational threshold, and minimal regulatory constraints in practical applications, while its captured imagery provides rich textural details that clearly depict the complex surface features in urban villages. To assess the accuracy of the extraction results, the visual comparison between the segmentation outputs and airborne LiDAR point clouds captured by the DJI M300 RTK was performed, and classification performance was evaluated based on confusion matrix metrics. The results indicate that the boundaries of the segmented objects align well with the building edges in the LiDAR point cloud. The classification accuracy of the three selected algorithms exceeded 80%, with the KNN classifier achieving an accuracy of 91% and a Kappa coefficient of 0.87, which robustly demonstrate the reliability of the UAV data and validate the feasibility of the proposed approach in complex cases. As a practical case reference, this study is expected to promote the wider application of lightweight UAVs across various fields.

关键词： lightweight UAV DJI Mavic 3 Enterprise object-based image analysis LiDAR urban village buildings extraction

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Utilization of Improved Annotations From object-based image analysis as Training Data for DeepLab V3+Model: A Focus on Road Extraction in Very High-Resolution Orthophotos

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IEEE ACCESS 2024年 12卷 67910-67923页

作者： Sussi Husni, Emir Yusuf, Rahadian Budi Harto, Agung Suwardhi, Deni Siburian, Arthur Inst Teknol Bandung Sch Elect Engn & Informat Bandung 40132 Indonesia Telkom Univ Fac Elect Engn Dept Telecommun Bandung 40257 Indonesia Inst Teknol Bandung Fac Earth Sci & Technol Bandung 40132 Indonesia

Road extraction plays a crucial role in various sectors, including transportation systems, disaster relief distribution, and urban planning, necessitating a more efficient method than the current labor-intensive approach. The conventional process, reliant on operator efforts, proves costly, time-consuming, and energy intensive. To address this, the employment of deep learning models such as DeepLab V3+ are done, leveraging encoders like ResNet 50, ResNet 101, and MobileNet V2, known for their effectiveness in deep learning applications. Although Deep Learning has demonstrated faster and more automated road extraction, the bottleneck persists in the manual creation of training data through road annotations by operators. Our research focuses on accelerating road extraction by incorporating enhanced annotations from object-based image analysis (OBIA) along with organic annotations into the training data to expedite dataset creation while ensuring extraction model accuracy. Specifically, we investigate the optimal ratio of synthetic to organic annotations that yields the highest road extraction accuracy. Moreover, we enhance OBIA-derived road annotations and regulate their integration into the training data. Our findings reveal an optimal composition of 25% for OBIA annotations and 50% for improved OBIA annotations, as exceeding these numbers results in diminished model performance. Significantly, the further improvement of OBIA annotations substantially boosts model performance metrics exemplified in the use of 100% composition in training data. On average, each model produces Pixel Accuracy of 0.942, IoUr of 0.012, mean IoU of 0.477, and Dice Score of 0.495 for every use of 100% OBIA annotations in the training data. Improvement in model performance evaluation metrics occurs when using 100% improved OBIA annotations in the training data where on average each model produces Pixel Accuracy values of 0.954, IoUr of 0.433, mean IoU of 0.692, and Dice Score of 0.771. The e

关键词： Annotations Roads Training data Data models image analysis Training Deep learning Pixel object-based image analysis deeplab v3+ pixel accuracy mean IoU

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Development of an object-based image analysis tool for mass spectrometry imaging ion classification

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ANALYTICAL AND BIOANALYTICAL CHEMISTRY 2023年第19期415卷 4725-4730页

作者： Eisenberg, Seth M. Knizner, Kevan T. Muddiman, David C. North Carolina State Univ Dept Chem FTMS Lab Human Hlth Res Raleigh NC 27695 USA

Mass spectrometry imaging (MSI) is an analytical technique that can detect and visualize thousands of m/z values resolved in two- and three-dimensional space. These m/z values lead to hundreds of molecular annotations, including on-tissue and background ions. Discrimination of sample-related analytes from ambient ions conventionally involves manual investigation of each ion heatmap, which requires significant researcher time and effort (for a single tissue image, it can take an hour to determine on-tissue and off-tissue species). Moreover, manual investigation lends itself to subjectivity. Herein, we present the utility of an ion classification tool (ICT) developed using object-based image analysis in MATLAB. The ICT functions by segmenting ion heatmap images into on-tissue and off-tissue objects through binary conversion. The binary images are analyzed and within seconds used to classify the ions as on-tissue or background using a binning approach based on the number of detected objects. In a representative dataset with 50 randomly selected annotations, the ICT was able to accurately classify 45/50 ions as on-tissue or background.

关键词： IR-MALDESI object-based image analysis Mass spectrometry imaging image segmentation image classification MATLAB

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Assessment of Sentinel-2A images for estimating rosemary land cover through an object-based image analysis approach

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AFRICAN JOURNAL OF ECOLOGY 2022年第3期60卷 682-690页

作者： Sabbahi, Monsif Nemmaoui, Abderrahim Tahani, Abdessalam El Bachiri, Ali Univ Mohammed Premier Lab Phys Chem Nat Resources & Environm BP 717 Oujda 60000 Morocco Univ Almeria Dept Engn Almeria Spain

Rosemary is a medicinal plant widely used in the food and pharmaceutical industry. In Morocco, rosemary harvesting generates significant benefits for the local economy. Even though, decision makers are short of accurate information about its geographical distribution area. The aim of this study is to delineate the rosemary shrubs in the Oriental region in Morocco in order to provide guidance for the sustainable use of these natural resources. We therefore used the freely available images of Sentinel-2A for binary classification (rosemary shrubs and the other lands) through an object-based image-analysis approach. The random forest classifier was deployed and trained with different object features likewise spectral values and vegetation indices. As a result, the distribution area of rosemary is 240,747 ha, which represents 18% of the studied area. Besides, an encouraging overall accuracy of 94.24% was attained;the user's accuracy for the class of "rosemary" and "Others" is 93.3% and 95.0%, respectively;meanwhile, the producer's accuracy for "rosemary" and "Others" is 93.72% and 94.65%.

关键词： land cover object-based image analysis oriental rosemary Sentinel-2A shrub

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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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Ultra-resolution unmanned aerial vehicle (UAV) and digital surface model (DSM) data-based automatic extraction of urban features using object-based image analysis approach in Gurugram, Haryana

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APPLIED GEOMATICS 2022年第4期14卷 751-764页

作者： Chauhan, Nitin Kumar, Ritesh Mukherjee, Semonti Hazra, Anupam Giri, Kushal Haryana Space Applicat Ctr Hisar 125004 Haryana India

Unmanned aerial vehicles (UAV) have emerged as flexible, swift, and economical imaging systems that have proven their feasibility in urban infrastructure mapping. However, data derived from such systems are not utilized thoroughly. In this study, an object-oriented, multiresolution segmentation-based workflow is explored to automatically extract the urban features such as buildings and roads that can revolutionize the pace of existing mapping methods. This paper contemplates the automatic object-oriented-based feature extraction process on 5-cm true color ortho-rectified images and digital surface model (DSM). The data was generated using the JOUAV/CW-10 model and a Sony Camera with a 40-megapixel resolution. The segmentation procedure was implemented, defining various parameters such as scale, shape, and compactness. Here, the optimum scale, shape, and compactness parameters chosen for buildings and road segmentation are 100, 0.6, and 0.8, and 50, 0.5, and 0.9, respectively. The object-based image analysis (OBIA) results were compared to manually digitized features to assess the accuracy of the automated process. The fractal border error accuracy is calculated for urban features such as roads and buildings. The OBIA results indicated that completeness, correctness, and quality of building features were 98.2%, 97.6%, and 95.9%, respectively. Similarly, the road features' average completeness, correctness, and quality were 85.8%, 73.8%, and 68.2%, respectively, which is on the lower side due to obscuring of roads by the avenue trees. The methodology yielded promising results for urban feature extraction with substantial accuracy and can be implemented in other areas with little fine-tuning of feature extraction parameters.

关键词： Unmanned aerial vehicle (UAV) Digital surface model (DSM) Feature extraction image segmentation object-based image analysis

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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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An object-based image analysis of WorldView-3 image for Urban Flood Vulnerability Assessment and Dissemination Through ESRI Story Maps

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JOURNAL OF THE INDIAN SOCIETY OF REMOTE SENSING 2021年第11期49卷 2639-2654页

作者： Aahlaad, Musunuru Mozumder, Chitrini Tripathi, Nitin Pal, Indrajit Asian Inst Technol AIT Sch Engn & Technol SET Dept Informat & Commun Technol Remote Sensing & Geog Informat Syst Khlong Luang Thailand Asian Inst Technol AIT Disaster Preparedness Mitigat & Management DPMM Khlong Luang Thailand

The utilization of remote sensing and GIS is increasing in importance across the world for disaster preparedness, assessments, mitigation, and governance. A major focus is on extracting and disseminating useful information from satellite and airborne data sources for effective planning and decision making during and after disasters. This study presents a case study from Ko-Rian, a sub-district in Ayutthaya, Thailand which is frequently hit by monsoon flooding. First, an approach is demonstrated to extract buildings and roads from very-high-resolution satellite images. The results showed that use of selected multiple indices with object-based image analysis produced excellent results in mixed urban environment. Secondly, a procedure and framework are proposed for geodatabase modeling of urban flood vulnerability and disseminating information to a wider community through ESRI story maps. The proposed approach using virtual reality technologies such as Google Street View for assessing physical vulnerability and ESRI story maps to share the information can significantly reduce the time and costs of the traditional survey. The techniques and approach presented in this study can be useful to develop detailed vulnerability profile for effective decision making and dynamic planning for a flood resilient city.

关键词： ESRI story map GIS object-based image analysis Urban flood Vulnerability WorldView-3 image

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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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