Multilevel Feature Interaction Network for Remote Sensing Images Semantic Segmentation
High-spatial resolution (HSR) remote sensing images present significant challenges due to their highly complex backgrounds, a large number of densely distributed small targets, and the potential for confusion with land targets. These characteristics render existing methods ineffective in accurately...
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IEEE
2024-01-01
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| Series: | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
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| Online Access: | https://ieeexplore.ieee.org/document/10736554/ |
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| author | Hongkun Chen Huilan Luo |
| author_facet | Hongkun Chen Huilan Luo |
| author_sort | Hongkun Chen |
| collection | DOAJ |
| description | High-spatial resolution (HSR) remote sensing images present significant challenges due to their highly complex backgrounds, a large number of densely distributed small targets, and the potential for confusion with land targets. These characteristics render existing methods ineffective in accurately segmenting small targets and prone to boundary blurring. In response to these challenges, we introduce a novel multilevel feature interaction network (MFIN). The MFIN model was designed as a dual-branch U-shaped interactive decoding structure that effectively achieves semantic segmentation and edge detection. Notably, this study is the first to address ways to enhance the performance for HSR remote sensing image analysis by iteratively refining features at multilevels for different tasks. We designed the feature interaction module (FIM), which refines semantic features through multiscale attention and interacts with edge features of the same scale for optimization, then serving as input for iterative optimization in the next scale's FIM. In addition, a lightweight global feature module is designed to adaptively extract global contextual information from different scales features, thereby enhancing the semantic accuracy of the features. Furthermore, to mitigate the semantic dilution issues caused by upsampling, a semantic-guided fusion module is introduced to enhance the propagation of rich semantic information among features. The proposed methods achieve state-of-the-art segmentation performance across four publicly available remote sensing datasets: Potsdam, Vaihingen, LoveDA, and UAVid. Notably, our MFIN has only 15.4 MB parameters and 34.2 GB GFLOPs, achieving an optimal balance between accuracy and efficiency. |
| format | Article |
| id | doaj-art-436a4d29ef0241b7afd4f7439d4ded39 |
| institution | OA Journals |
| issn | 1939-1404 2151-1535 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
| spelling | doaj-art-436a4d29ef0241b7afd4f7439d4ded392025-08-20T02:14:55ZengIEEEIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing1939-14042151-15352024-01-0117198311985210.1109/JSTARS.2024.348672410736554Multilevel Feature Interaction Network for Remote Sensing Images Semantic SegmentationHongkun Chen0https://orcid.org/0009-0001-2952-0726Huilan Luo1https://orcid.org/0000-0002-5912-2331Jiangxi Provincial Key Laboratory of Multidimensional Intelligent, Jiangxi University of Science and Technology, Ganzhou, ChinaJiangxi Provincial Key Laboratory of Multidimensional Intelligent, Jiangxi University of Science and Technology, Ganzhou, ChinaHigh-spatial resolution (HSR) remote sensing images present significant challenges due to their highly complex backgrounds, a large number of densely distributed small targets, and the potential for confusion with land targets. These characteristics render existing methods ineffective in accurately segmenting small targets and prone to boundary blurring. In response to these challenges, we introduce a novel multilevel feature interaction network (MFIN). The MFIN model was designed as a dual-branch U-shaped interactive decoding structure that effectively achieves semantic segmentation and edge detection. Notably, this study is the first to address ways to enhance the performance for HSR remote sensing image analysis by iteratively refining features at multilevels for different tasks. We designed the feature interaction module (FIM), which refines semantic features through multiscale attention and interacts with edge features of the same scale for optimization, then serving as input for iterative optimization in the next scale's FIM. In addition, a lightweight global feature module is designed to adaptively extract global contextual information from different scales features, thereby enhancing the semantic accuracy of the features. Furthermore, to mitigate the semantic dilution issues caused by upsampling, a semantic-guided fusion module is introduced to enhance the propagation of rich semantic information among features. The proposed methods achieve state-of-the-art segmentation performance across four publicly available remote sensing datasets: Potsdam, Vaihingen, LoveDA, and UAVid. Notably, our MFIN has only 15.4 MB parameters and 34.2 GB GFLOPs, achieving an optimal balance between accuracy and efficiency.https://ieeexplore.ieee.org/document/10736554/Feature interactionmultilevel featuresremote sensing images analysissemantic segmentation |
| spellingShingle | Hongkun Chen Huilan Luo Multilevel Feature Interaction Network for Remote Sensing Images Semantic Segmentation IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Feature interaction multilevel features remote sensing images analysis semantic segmentation |
| title | Multilevel Feature Interaction Network for Remote Sensing Images Semantic Segmentation |
| title_full | Multilevel Feature Interaction Network for Remote Sensing Images Semantic Segmentation |
| title_fullStr | Multilevel Feature Interaction Network for Remote Sensing Images Semantic Segmentation |
| title_full_unstemmed | Multilevel Feature Interaction Network for Remote Sensing Images Semantic Segmentation |
| title_short | Multilevel Feature Interaction Network for Remote Sensing Images Semantic Segmentation |
| title_sort | multilevel feature interaction network for remote sensing images semantic segmentation |
| topic | Feature interaction multilevel features remote sensing images analysis semantic segmentation |
| url | https://ieeexplore.ieee.org/document/10736554/ |
| work_keys_str_mv | AT hongkunchen multilevelfeatureinteractionnetworkforremotesensingimagessemanticsegmentation AT huilanluo multilevelfeatureinteractionnetworkforremotesensingimagessemanticsegmentation |