FMCNet: A Fuzzy Multiscale Convolution Network for Remote Sensing Image Segmentation
Due to being affected by factors such as imaging distance, lighting, ground features, and environment, objects in the same class may have certain differences, and different classes of objects often produce similar visual features in remote sensing images. This phenomenon leads to an uncertainty prob...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2024-12-01
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| Series: | Canadian Journal of Remote Sensing |
| Online Access: | http://dx.doi.org/10.1080/07038992.2024.2418091 |
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| _version_ | 1846095175927988224 |
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| author | Ziyi Li Tingting Qu Qianpeng Chong Jindong Xu |
| author_facet | Ziyi Li Tingting Qu Qianpeng Chong Jindong Xu |
| author_sort | Ziyi Li |
| collection | DOAJ |
| description | Due to being affected by factors such as imaging distance, lighting, ground features, and environment, objects in the same class may have certain differences, and different classes of objects often produce similar visual features in remote sensing images. This phenomenon leads to an uncertainty problem in segmentation of remote sensing images, i.e., intra-class heterogeneity and inter-class blurring. To alleviate this problem, a fuzzy multiscale convolution neural network (FMCNet) is proposed in this paper. By extracting receptive fields of different scales, sizes and aspect ratios, the detailed information in remote sensing objects is fully represented. The relationship between their adjacent pixels is effectively expressed by fuzzy logic learning to alleviate the uncertain segmentation. The proposed method achieves overall accuracies of 85.33%, 86.31%, and 85.39% on the Vaihingen, Potsdam, and Gaofen Image datasets respectively. It demonstrates superior performance compared to existing popular methods. |
| format | Article |
| id | doaj-art-72a655a96f9543c7b60c76cb431dd718 |
| institution | Kabale University |
| issn | 1712-7971 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Canadian Journal of Remote Sensing |
| spelling | doaj-art-72a655a96f9543c7b60c76cb431dd7182025-01-02T11:34:20ZengTaylor & Francis GroupCanadian Journal of Remote Sensing1712-79712024-12-0150110.1080/07038992.2024.24180912418091FMCNet: A Fuzzy Multiscale Convolution Network for Remote Sensing Image SegmentationZiyi Li0Tingting Qu1Qianpeng Chong2Jindong Xu3School of Computer and Control Engineering, YanTai UniversitySchool of Computer and Control Engineering, YanTai UniversitySchool of Computer and Control Engineering, YanTai UniversitySchool of Computer and Control Engineering, YanTai UniversityDue to being affected by factors such as imaging distance, lighting, ground features, and environment, objects in the same class may have certain differences, and different classes of objects often produce similar visual features in remote sensing images. This phenomenon leads to an uncertainty problem in segmentation of remote sensing images, i.e., intra-class heterogeneity and inter-class blurring. To alleviate this problem, a fuzzy multiscale convolution neural network (FMCNet) is proposed in this paper. By extracting receptive fields of different scales, sizes and aspect ratios, the detailed information in remote sensing objects is fully represented. The relationship between their adjacent pixels is effectively expressed by fuzzy logic learning to alleviate the uncertain segmentation. The proposed method achieves overall accuracies of 85.33%, 86.31%, and 85.39% on the Vaihingen, Potsdam, and Gaofen Image datasets respectively. It demonstrates superior performance compared to existing popular methods.http://dx.doi.org/10.1080/07038992.2024.2418091 |
| spellingShingle | Ziyi Li Tingting Qu Qianpeng Chong Jindong Xu FMCNet: A Fuzzy Multiscale Convolution Network for Remote Sensing Image Segmentation Canadian Journal of Remote Sensing |
| title | FMCNet: A Fuzzy Multiscale Convolution Network for Remote Sensing Image Segmentation |
| title_full | FMCNet: A Fuzzy Multiscale Convolution Network for Remote Sensing Image Segmentation |
| title_fullStr | FMCNet: A Fuzzy Multiscale Convolution Network for Remote Sensing Image Segmentation |
| title_full_unstemmed | FMCNet: A Fuzzy Multiscale Convolution Network for Remote Sensing Image Segmentation |
| title_short | FMCNet: A Fuzzy Multiscale Convolution Network for Remote Sensing Image Segmentation |
| title_sort | fmcnet a fuzzy multiscale convolution network for remote sensing image segmentation |
| url | http://dx.doi.org/10.1080/07038992.2024.2418091 |
| work_keys_str_mv | AT ziyili fmcnetafuzzymultiscaleconvolutionnetworkforremotesensingimagesegmentation AT tingtingqu fmcnetafuzzymultiscaleconvolutionnetworkforremotesensingimagesegmentation AT qianpengchong fmcnetafuzzymultiscaleconvolutionnetworkforremotesensingimagesegmentation AT jindongxu fmcnetafuzzymultiscaleconvolutionnetworkforremotesensingimagesegmentation |