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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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object-based image analysis approach for vessel detection on optical and radar images

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JOURNAL OF APPLIED REMOTE SENSING 2019年第1期13卷

作者： Aiello, Martina Vezzoli, Renata Gianinetto, Marco Politecn Milan Dept Architecture Built Environm & Construct Engn Milan Italy

Commercial satellites for Earth observation can integrate conventional positioning and tracking systems for monitoring legal and illegal activities by sea, in order to effectively detect and prevent events threatening human life and environment. This study describes an object-oriented approach to detect vessels combining high- and medium-resolution optical and radar images. Once detected, the algorithm estimates their position, length, and heading and assigns a speed range. Tests are done using WorldView-2, QuickBird, GeoEye-1, Sentinel-2A, COSMO-SkyMed, and Sentinel-1 data imaged in several test sites including China, Australia, Italy, Hong Kong, and the western Mediterranean Sea. Validation of results with data from the automatic identification system shows that the estimates for length and heading have R-2 = 0.85 and R-2 = 0.92, respectively. Tests for evaluating speed from Sentinel-2 time-lag image displacement show encouraging results, with 70% of estimates' residuals within +/- 2 m/s. Finally, our method is compared to the state-of-the-art search for unidentified maritime object (SUMO), provided by the European Commission's Joint Research Centre. Finally, our method is compared to the state-of-the-art SUMO. Tests with Sentinel-1 data show similar results in terms of correct detections. Nevertheless, our method returns a smaller number of false alarms compared to SUMO. (C) 2019 Society of Photo-Optical Instrumentation Engineers (SPIE)

关键词： optical images synthetic aperture radar images object-based image analysis ship detection

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object-based image analysis using multiscale connectivity

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IEEE TRANSACTIONS ON PATTERN analysis AND MACHINE INTELLIGENCE 2005年第6期27卷 892-907页

作者： Braga-Neto, U Goutsias, J CPqAM FIOCRUZ Aggeu MAgalhaes Res Ctr Virol & Expt Therapy Lab Recife PE Brazil Johns Hopkins Univ Ctr Imaging Sci Baltimore MD 21218 USA Johns Hopkins Univ Dept Elect & Comp Engn Baltimore MD 21218 USA

This paper introduces a novel approach for image analysis based on the notion of multiscale connectivity. We use the proposed approach to design several novel tools for object-based image representation and analysis which exploit the connectivity structure of images in a multiscale fashion. More specifically, we propose a nonlinear pyramidal image representation scheme, which decomposes an image at different scales by means of multiscale grain filters. These filters gradually remove connected components from an image that fail to satisfy a given criterion. We also use the concept of multiscale connectivity to design a hierarchical data partitioning tool. We employ this tool to construct another image representation scheme, based on the concept of component trees, which organizes partitions of an image in a hierarchical multiscale fashion. In addition, we propose a geometrically-oriented hierarchical clustering algorithm which generalizes the classical single-linkage algorithm. Finally, we propose two object-based multiscale image summaries, reminiscent of the well-known (morphological) pattern spectrum, which can be useful in image analysis and image understanding applications.

关键词： connectivity connection hierarchical clustering hierarchical segmentation mathematical morphology multiscale image analysis multiscale image decomposition object-based image analysis reconstruction opening

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object-based image analysis: a review of developments and future directions of automated feature detection in landscape archaeology

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ARCHAEOLOGICAL PROSPECTION 2019年第2期26卷 155-163页

作者： Davis, Dylan S. Penn State Univ Dept Anthropol University Pk PA 16802 USA

object-based image analysis (OBIA) is a method of assessing remote sensing data that uses morphometric and spectral parameters simultaneously to identify features in remote sensing imagery. Over the past 10-15 years, OBIA methods have been introduced to detect archaeological features. Improvements in accuracy have been attained by using a greater number of morphometric variables and multiple scales of analysis. This article highlights the developments that have occurred in the application of OBIA within archaeology and argues that OBIA is both a useful and necessary tool for archaeological research. Additionally, I discuss future research paths using this method. Some of the suggestions put forth here include: pushing for multifaceted research designs utilizing OBIA and manual interpretation, using OBIA methods for directly studying landscape settlement patterns, and increasing data sharing of methods between researchers.

关键词： automated feature extraction landscape analysis machine learning object-based image analysis pattern recognition remote sensing

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object-based image analysis for the impact of sewage pollution in Malad Creek, Mumbai, India

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ENVIRONMENTAL MONITORING AND ASSESSMENT 2016年第2期188卷 95页

作者： Shirke, Shivani Pinto, Shannon M. Kushwaha, Vikash K. Mardikar, Trupti Vijay, Ritesh CSIR Natl Environm Engn Res Inst Environm Syst Design & Modelling Div Nehru Marg Nagpur 440020 Maharashtra India

Today, object-based image analysis provides an option for integrating spatial information beyond conventional pixel-based classifications for high-resolution imagery. Due to its rare applicability in pollution assessment, an attempt has been made to assess the spatial extent of sewage pollution in Malad Creek, Mumbai, India. based on multiresolution segmentation of an IRS P6 (LISS IV) image and the Normalized Difference Turbidity Index (NDTI), the various water quality regions in the creek were classified. The existing literature implies that the reflectance of turbid water is similar to that of bare soil which gives positive NDTI values. In contrast to this, negative values of NDTI are observed in the present study due to the presence of organic matter which absorbs light and imparts turbidity, which is supported by the significant correlation between NDTI and turbidity. A strong relationship is observed between turbidity and water quality parameters, implying the impact of organic matter through discharges of sewage in the creek. based on the classified regions and the water quality parameters, the extent of pollution was ranked as high, moderate, low and least. The methodology developed in the present study was successfully applied on an IKONOS image for the same study area but a different time frame. The approach will help in impact assessment of sewage pollution and its spatial extent in other water bodies.

关键词： Creek object-based image analysis Segmentation Sewage pollution

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

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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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object-based image analysis OF POST-TSUNAMI HIGH-RESOLUTION SATELLITE imageS FOR MAPPING THE IMPACT OF TSUNAMI DISASTER

OBJECT-BASED IMAGE ANALYSIS OF POST-TSUNAMI HIGH-RESOLUTION ...

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IEEE International Geoscience and Remote Sensing Symposium (IGARSS)

作者： Koshimura, Shunichi Kayaba, Shintaro Gokon, Hideomi Tohoku Univ Grad Sch Engn Sendai Miyagi 980 Japan Miyagi TV Broadcast Corp Miyazaki Japan

ISBN: (纸本)9781457710056

The authors developed a method of object-based satellite image analysis using high-resolution post-tsunami satellite image to detect and map tsunami impact. The method is applied to QuickBird 4 band pan-sharpened composite image acquired in Banda Aceh, Indonesia, and the ground objects are classified into six;vegetation, water, soil, building, road and debris, for mapping the impact of the 2004 Sumatra-Andaman earthquake tsunami.

关键词： tsunami damage detection object-based image analysis optical satellite image

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object-based image analysis AND DATA MINING FOR MAPPING SUGARCANE WITH LANDSAT imageRY IN BRAZIL

OBJECT-BASED IMAGE ANALYSIS AND DATA MINING FOR MAPPING SUGA...

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Conference on Geographic object-based image analysis (GEOBIA)

作者： Formaggio, A. R. Vieira, M. A. Renno, C. D. Aguiar, D. A. Mello, M. P. INPE BR-12227010 Sao Jose Dos Campos SP Brazil

Mapping of sugarcane planted area is an important information for decision making, mainly when the search for alternatives to mitigate greenhouse gas emissions has indicated the use of biofuels as a viable option. Thus, the aim of this research was to develop a methodology in order to automate the sugarcane mapping task when remote sensing data are used. Thus the integration of two major approaches of Artificial Intelligence, object-based image analysis (OBIA) and Data Mining (DM), were tested in a study area located in Sao Paulo state, which is well representative of the agriculture of large regions of Brazil and other countries. OBIA was used to emulate the interpreter knowledge in the process of sugarcane mapping, and DM techniques were employed for automatic generation of knowledge model. A time series of four Landsat images was acquired in order to represent the wide variability of the patterns during sugarcane crop season. Definiens Developer (R) multiresolution segmentation algorithm produced the objects and properly trained decision tree applied to the Landsat data for the generation of the thematic map with sugarcane as the main class of interest. An overall accuracy of 94% (Kappa = 0,87) was obtained, showing that OBIA and DM are very efficient and promising in the direction of automating the sugarcane classification process with Landsat multitemporal time series.

关键词： Sugarcane mapping Artificial Intelligence object-based image analysis Data Mining Landsat images

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object-based image analysis and Data mining for Building Ontology of Informal Settlements

Object-based Image Analysis and Data mining for Building Ont...

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Conference on image and Signal Processing for Remote Sensing XVIII

作者： Khelifa, Djerriri Mimoun, Malki Ctr Spatial Tech Div Earth Observat Arzew 31200 Oran Algeria Univ Djillali Liabes Sidi Bel Abbes BEEDIS Lab Abbes 22000 Algeria

ISBN: (纸本)9780819492777

During recent decades, unplanned settlements have been appeared around the big cities in most developing countries and as consequence, numerous problems have emerged. Thus the identification of different kinds of settlements is a major concern and challenge for authorities of many countries. Very High Resolution (VHR) Remotely Sensed imagery has proved to be a very promising way to detect different kinds of settlements, especially through the using of new object-based image analysis (OBIA). The most important key is in understanding what characteristics make unplanned settlements differ from planned ones, where most experts characterize unplanned urban areas by small building sizes at high densities, no orderly road arrangement and Lack of green spaces. Knowledge about different kinds of settlements can be captured as a domain ontology that has the potential to organize knowledge in a formal, understandable and sharable way. In this work we focus on extracting knowledge from VHR images and expert's knowledge. We used an object based strategy by segmenting a VHR image taken over urban area into regions of homogenous pixels at adequate scale level and then computing spectral, spatial and textural attributes for each region to create objects. A genetic-based data mining was applied to generate high predictive and comprehensible classification rules based on selected samples from the OBIA result. Optimized intervals of relevant attributes are found, linked with land use types for forming classification rules. The unplanned areas were separated from the planned ones, through analyzing of the line segments detected from the input image. Finally a simple ontology was built based on the previous processing steps. The approach has been tested to VHR images of one of the biggest Algerian cities, that has grown considerably in recent decades.

关键词： Unplanned settlements VHR imagery object-based image analysis Data mining Ontology

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object-based image analysis for the assessment of mineral extraction in conflict regions - a case study in the Democratic Republic of the Congo 23

Object-based image analysis for the assessment of mineral ex...

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

作者： Kranz, Olaf Schoepfer, Elisabeth Sproehnle, Kristin Lang, Stefan Salzburg Univ Dept Geoinformat Z GIS Salzburg Austria Helmholtz Assoc German Res Ctr Res Sect Head Berlin Germany German Aerosp Ctr DLR German Remote Sensing Data Ctr DFD Oberpfaffenhofen Germany

In this study object-based image analysis (OBIA) techniques were applied to assess land cover changes related to mineral extraction in a conflict-affected area of the eastern Democratic Republic of the Congo (DRC) over a period of five years based on very high resolution (VHR) satellite data of different sensors. object-based approaches explicitly consider spatio-temporal aspects which allow extracting important information to document mining activities. The use of remote sensing data as an independent, up-to-date and reliable data source provided hints on the general development of the mining sector in relation to socio-economic and political decisions. While in early 2010, the situation was still characterised by an intensification of mineral extraction, a mining ban between autumn 2010 and spring 2011 marked the starting point for a continuous decrease of mining activities. The latter can be substantiated through a decrease in the extend of the mining area as well as of the number of dwellings in the nearby settlement. A following demilitarisation and the mentioned need for accountability with respect to the origin of certain minerals led to organised, more industrialized exploitation. This development is likewise visible on satellite imagery as typical clearings within forested areas. The results of the continuous monitoring in turn facilitate non-governmental organisations (NGOs) to further foster the mentioned establishment of responsible supply chains by the mining industry throughout the entire period of investigation.

关键词： land cover change natural resources mining object-based image analysis remote sensing

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