Information Extraction of High Resolution Remote Sensing Images Based on the Calculation of Optimal Segmentation Parameters

Zhu, Hao; Cai, Lijie; Liu, Haiying; Huang, Wei

doi:10.1371/journal.pone.0158585

Cited by 11 publications

(10 citation statements)

References 5 publications

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“…Although various combination of values for each segmentation parameter affects an image scene to different extents, Su et al [121] suggests that scale is usually the most influential factor when doing multi-resolution segmentation. A key issue with segmentation parameters, however, is that they are mainly chosen subjectively [16,122]. Few studies have developed automated methods for the selection of segmentation parameters for slums, notably Novack and Kux [71].…”

Section: Object-based Image Analysismentioning

confidence: 99%

A Critical Review of High and Very High-Resolution Remote Sensing Approaches for Detecting and Mapping Slums: Trends, Challenges and Emerging Opportunities

et al. 2018

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Slums are a global urban challenge, with less developed countries being particularly impacted. To adequately detect and map them, data is needed on their location, spatial extent and evolution. High-and very high-resolution remote sensing imagery has emerged as an important source of data in this regard. The purpose of this paper is to critically review studies that have used such data to detect and map slums. Our analysis shows that while such studies have been increasing over time, they tend to be concentrated to a few geographical areas and often focus on the use of a single approach (e.g., image texture and object-based image analysis), thus limiting generalizability to understand slums, their population, and evolution within the global context. We argue that to develop a more comprehensive framework that can be used to detect and map slums, other emerging sourcing of geospatial data should be considered (e.g., volunteer geographic information) in conjunction with growing trends and advancements in technology (e.g., geosensor networks). Through such data integration and analysis we can then create a benchmark for determining the most suitable methods for mapping slums in a given locality, thus fostering the creation of new approaches to address this challenge.

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Section: Object-based Image Analysismentioning

confidence: 99%

A Critical Review of High and Very High-Resolution Remote Sensing Approaches for Detecting and Mapping Slums: Trends, Challenges and Emerging Opportunities

et al. 2018

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“…The multiresolution segmentation technique is based on the minimum criterion of heterogeneity [41]. After setting the appropriate segmentation parameters, starting from a random pixel in the target image, the heterogeneity after merging with adjacent pixels is calculated and compared with the segmentation scale.…”

Section: Object-oriented Multiresolution Segmentation Methods 221 Determination Of Segmentation Parametersmentioning

confidence: 99%

Generation of Homogeneous Slope Units Using a Novel Object-Oriented Multi-Resolution Segmentation Method

Chen

et al. 2021

Water

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The generation of map units is a fundamental step for an appropriate assessment of landslide susceptibility. Recent studies have indicated that the terrain relief-based slope units perform better in homogeneity compared with the grid units. However, it is difficult at present to generate high-precision and high-matching slope units by traditional methods. The problem commonly concentrates in the plain areas without obvious terrain reliefs and the junction of sudden changes in terrain. In this paper, we propose a novel object-oriented segmentation method for generating homogeneous slope units. Herein, the multi-resolution segmentation algorithm in the image processing field is introduced, enabling the integration of terrain boundary conditions and image segmentation conditions in slope units. In order to illustrate the performances of the proposed method, Kitakyushu region in Japan is selected as a case study. The results show that the proposed method generates satisfactory slope units that satisfactorily reproduce the actual terrain relief, with the best within-unit and between-unit homogeneities compared with the previous methods, in particular at the plain areas. We also verify the effectiveness of the presented method through the sensitivity analysis using different resolutions of digital elevation models (DEMs) data of the region. It is reported that the presented approach is notably advanced in the requirements of the quality of DEM data, as the presented approach is less sensitive to DEM spatial resolution compared with other available methods.

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“…Machine learning approaches are successfully employed to extract building footprints from satellite and aerial images 38 , 39 and classify buildings from remote sensing imagery 40 – 43 , Google Earth images 44 , and light detection and ranging (LiDAR) data 45 , 46 . Semantic analysis is also coupled with the random forest method to classify urban buildings from images into finer categories 47 , 48 .…”

Section: Related Workmentioning

confidence: 99%

Predicting building types using OpenStreetMap

Atwal

Anderson

Pfoser

et al. 2022

Sci Rep

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Having accurate building information is paramount for a plethora of applications, including humanitarian efforts, city planning, scientific studies, and navigation systems. While volunteered geographic information from sources such as OpenStreetMap (OSM) has good building geometry coverage, descriptive attributes such as the type of a building are sparse. To fill this gap, this study proposes a supervised learning-based approach to provide meaningful, semantic information for OSM data without manual intervention. We present a basic demonstration of our approach that classifies buildings into either residential or non-residential types for three study areas: Fairfax County in Virginia (VA), Mecklenburg County in North Carolina (NC), and the City of Boulder in Colorado (CO). The model leverages (i) available OSM tags capturing non-spatial attributes, (ii) geometric and topological properties of the building footprints including adjacent types of roads, proximity to parking lots, and building size. The model is trained and tested using ground truth data available for the three study areas. The results show that our approach achieves high accuracy in predicting building types for the selected areas. Additionally, a trained model is transferable with high accuracy to other regions where ground truth data is unavailable. The OSM and data science community are invited to build upon our approach to further enrich the volunteered geographic information in an automated manner.

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Information Extraction of High Resolution Remote Sensing Images Based on the Calculation of Optimal Segmentation Parameters

Cited by 11 publications

References 5 publications

A Critical Review of High and Very High-Resolution Remote Sensing Approaches for Detecting and Mapping Slums: Trends, Challenges and Emerging Opportunities

A Critical Review of High and Very High-Resolution Remote Sensing Approaches for Detecting and Mapping Slums: Trends, Challenges and Emerging Opportunities

Generation of Homogeneous Slope Units Using a Novel Object-Oriented Multi-Resolution Segmentation Method

Predicting building types using OpenStreetMap

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