Single-Object-Based Region Growth: Key Area Localization Model for Remote Sensing Image Scene Classification
Remote sensing image scene classification is a challenging task due to the large differences within the same classes and a large number of similar scenes among different classes. To tackle this problem, this paper proposes a single-object-based region growth algorithm to effectively localize the mos...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2022-01-01
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| Series: | Advances in Multimedia |
| Online Access: | http://dx.doi.org/10.1155/2022/5816565 |
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| _version_ | 1832563269475237888 |
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| author | Feiyang Li Jiangtao Wang Mingyang Wang Ziyang Wang |
| author_facet | Feiyang Li Jiangtao Wang Mingyang Wang Ziyang Wang |
| author_sort | Feiyang Li |
| collection | DOAJ |
| description | Remote sensing image scene classification is a challenging task due to the large differences within the same classes and a large number of similar scenes among different classes. To tackle this problem, this paper proposes a single-object-based region growth algorithm to effectively localize the most key area in the whole image, so as to generate more discriminative local fine-grained features for the image scene. Concurrently, a local-global two-branch network is designed to utilize the features of the images from multiple perspectives, respectively. Specially, the global branch extracts global features (such as contour, texture) from the whole image, and local branch extracts more local features from the local key area. Finally, the global and local classification scores are integrated to make the final decision. Experiments are performed on three publicly available data sets, and the results show that this method can achieve higher accuracy compared to most existing state-of-the-art methods. |
| format | Article |
| id | doaj-art-14319f59ea4e4aceb267eeeef5a266af |
| institution | Kabale University |
| issn | 1687-5699 |
| language | English |
| publishDate | 2022-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Advances in Multimedia |
| spelling | doaj-art-14319f59ea4e4aceb267eeeef5a266af2025-02-03T01:20:35ZengWileyAdvances in Multimedia1687-56992022-01-01202210.1155/2022/5816565Single-Object-Based Region Growth: Key Area Localization Model for Remote Sensing Image Scene ClassificationFeiyang Li0Jiangtao Wang1Mingyang Wang2Ziyang Wang3School of Physics and Electronic InformationSchool of Physics and Electronic InformationSchool of Physics and Electronic InformationSchool of Physics and Electronic InformationRemote sensing image scene classification is a challenging task due to the large differences within the same classes and a large number of similar scenes among different classes. To tackle this problem, this paper proposes a single-object-based region growth algorithm to effectively localize the most key area in the whole image, so as to generate more discriminative local fine-grained features for the image scene. Concurrently, a local-global two-branch network is designed to utilize the features of the images from multiple perspectives, respectively. Specially, the global branch extracts global features (such as contour, texture) from the whole image, and local branch extracts more local features from the local key area. Finally, the global and local classification scores are integrated to make the final decision. Experiments are performed on three publicly available data sets, and the results show that this method can achieve higher accuracy compared to most existing state-of-the-art methods.http://dx.doi.org/10.1155/2022/5816565 |
| spellingShingle | Feiyang Li Jiangtao Wang Mingyang Wang Ziyang Wang Single-Object-Based Region Growth: Key Area Localization Model for Remote Sensing Image Scene Classification Advances in Multimedia |
| title | Single-Object-Based Region Growth: Key Area Localization Model for Remote Sensing Image Scene Classification |
| title_full | Single-Object-Based Region Growth: Key Area Localization Model for Remote Sensing Image Scene Classification |
| title_fullStr | Single-Object-Based Region Growth: Key Area Localization Model for Remote Sensing Image Scene Classification |
| title_full_unstemmed | Single-Object-Based Region Growth: Key Area Localization Model for Remote Sensing Image Scene Classification |
| title_short | Single-Object-Based Region Growth: Key Area Localization Model for Remote Sensing Image Scene Classification |
| title_sort | single object based region growth key area localization model for remote sensing image scene classification |
| url | http://dx.doi.org/10.1155/2022/5816565 |
| work_keys_str_mv | AT feiyangli singleobjectbasedregiongrowthkeyarealocalizationmodelforremotesensingimagesceneclassification AT jiangtaowang singleobjectbasedregiongrowthkeyarealocalizationmodelforremotesensingimagesceneclassification AT mingyangwang singleobjectbasedregiongrowthkeyarealocalizationmodelforremotesensingimagesceneclassification AT ziyangwang singleobjectbasedregiongrowthkeyarealocalizationmodelforremotesensingimagesceneclassification |