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Object-based multiscale segmentation incorporating texture and edge features of high-resolution remote sensing images
Multiscale segmentation (MSS) is crucial in object-based image analysis methods (OBIA). How to describe the underlying features of remote sensing images and combine multiple features for object-based multiscale image segmentation is a hotspot in the field of OBIA. Traditional object-based segmentati...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
PeerJ Inc.
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10280506/ https://www.ncbi.nlm.nih.gov/pubmed/37346590 http://dx.doi.org/10.7717/peerj-cs.1290 |
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author | Shen, Xiaole Guo, Yiquan Cao, Jinzhou |
author_facet | Shen, Xiaole Guo, Yiquan Cao, Jinzhou |
author_sort | Shen, Xiaole |
collection | PubMed |
description | Multiscale segmentation (MSS) is crucial in object-based image analysis methods (OBIA). How to describe the underlying features of remote sensing images and combine multiple features for object-based multiscale image segmentation is a hotspot in the field of OBIA. Traditional object-based segmentation methods mostly use spectral and shape features of remote sensing images and pay less attention to texture and edge features. We analyze traditional image segmentation methods and object-based MSS methods. Then, on the basis of comparing image texture feature description methods, a method for remote sensing image texture feature description based on time-frequency analysis is proposed. In addition, a method for measuring the texture heterogeneity of image objects is constructed on this basis. Using bottom-up region merging as an MSS strategy, an object-based MSS algorithm for remote sensing images combined with texture feature is proposed. Finally, based on the edge feature of remote sensing images, a description method of remote sensing image edge intensity and an edge fusion cost criterion are proposed. Combined with the heterogeneity criterion, an object-based MSS algorithm combining spectral, shape, texture, and edge features is proposed. Experiment results show that the comprehensive features object-based MSS algorithm proposed in this article can obtain more complete segmentation objects when segmenting ground objects with rich texture information and slender shapes and is not prone to over-segmentation. Compare with the traditional object-based segmentation algorithm, the average accuracy of the algorithm is increased by 4.54%, and the region ratio is close to 1, which will be more conducive to the subsequent processing and analysis of remote sensing images. In addition, the object-based MSS algorithm proposed in this article can effectively obtain more complete ground objects and can be widely used in scenes such as building extraction. |
format | Online Article Text |
id | pubmed-10280506 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | PeerJ Inc. |
record_format | MEDLINE/PubMed |
spelling | pubmed-102805062023-06-21 Object-based multiscale segmentation incorporating texture and edge features of high-resolution remote sensing images Shen, Xiaole Guo, Yiquan Cao, Jinzhou PeerJ Comput Sci Algorithms and Analysis of Algorithms Multiscale segmentation (MSS) is crucial in object-based image analysis methods (OBIA). How to describe the underlying features of remote sensing images and combine multiple features for object-based multiscale image segmentation is a hotspot in the field of OBIA. Traditional object-based segmentation methods mostly use spectral and shape features of remote sensing images and pay less attention to texture and edge features. We analyze traditional image segmentation methods and object-based MSS methods. Then, on the basis of comparing image texture feature description methods, a method for remote sensing image texture feature description based on time-frequency analysis is proposed. In addition, a method for measuring the texture heterogeneity of image objects is constructed on this basis. Using bottom-up region merging as an MSS strategy, an object-based MSS algorithm for remote sensing images combined with texture feature is proposed. Finally, based on the edge feature of remote sensing images, a description method of remote sensing image edge intensity and an edge fusion cost criterion are proposed. Combined with the heterogeneity criterion, an object-based MSS algorithm combining spectral, shape, texture, and edge features is proposed. Experiment results show that the comprehensive features object-based MSS algorithm proposed in this article can obtain more complete segmentation objects when segmenting ground objects with rich texture information and slender shapes and is not prone to over-segmentation. Compare with the traditional object-based segmentation algorithm, the average accuracy of the algorithm is increased by 4.54%, and the region ratio is close to 1, which will be more conducive to the subsequent processing and analysis of remote sensing images. In addition, the object-based MSS algorithm proposed in this article can effectively obtain more complete ground objects and can be widely used in scenes such as building extraction. PeerJ Inc. 2023-03-15 /pmc/articles/PMC10280506/ /pubmed/37346590 http://dx.doi.org/10.7717/peerj-cs.1290 Text en ©2023 Shen et al. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ Computer Science) and either DOI or URL of the article must be cited. |
spellingShingle | Algorithms and Analysis of Algorithms Shen, Xiaole Guo, Yiquan Cao, Jinzhou Object-based multiscale segmentation incorporating texture and edge features of high-resolution remote sensing images |
title | Object-based multiscale segmentation incorporating texture and edge features of high-resolution remote sensing images |
title_full | Object-based multiscale segmentation incorporating texture and edge features of high-resolution remote sensing images |
title_fullStr | Object-based multiscale segmentation incorporating texture and edge features of high-resolution remote sensing images |
title_full_unstemmed | Object-based multiscale segmentation incorporating texture and edge features of high-resolution remote sensing images |
title_short | Object-based multiscale segmentation incorporating texture and edge features of high-resolution remote sensing images |
title_sort | object-based multiscale segmentation incorporating texture and edge features of high-resolution remote sensing images |
topic | Algorithms and Analysis of Algorithms |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10280506/ https://www.ncbi.nlm.nih.gov/pubmed/37346590 http://dx.doi.org/10.7717/peerj-cs.1290 |
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