A Fractal Curve-Inspired Framework for Enhanced Semantic Segmentation of Remote Sensing Images
The classification and recognition of features play a vital role in production and daily life; however, the current semantic segmentation of remote sensing images is hampered by background interference and other factors, leading to issues such as fuzzy boundary segmentation. To address these challen...
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MDPI AG
2024-11-01
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| Online Access: | https://www.mdpi.com/1424-8220/24/22/7159 |
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| author | Xinhua Wang Botao Yuan Zhuang Li Heqi Wang |
| author_facet | Xinhua Wang Botao Yuan Zhuang Li Heqi Wang |
| author_sort | Xinhua Wang |
| collection | DOAJ |
| description | The classification and recognition of features play a vital role in production and daily life; however, the current semantic segmentation of remote sensing images is hampered by background interference and other factors, leading to issues such as fuzzy boundary segmentation. To address these challenges, we propose a novel module for encoding and reconstructing multi-dimensional feature layers. Our approach first utilizes a bilinear interpolation method to downsample the multi-dimensional feature layer in the coding stage of the U-shaped framework. Subsequently, we incorporate a fractal curve module into the encoder, which aggregates points on feature maps from different layers, effectively grouping points from diverse regions. Finally, we introduce an aggregation layer that combines the upsampling method from the UNet series, employing the multi-scale censoring of multi-dimensional feature map outputs from various layers to efficiently capture both spatial and feature information. The experimental results across diverse scenarios demonstrate that our model achieves excellent performance in aggregating point information from feature maps, significantly enhancing semantic segmentation tasks. |
| format | Article |
| id | doaj-art-ddefe724685e45f1858d6295d25cd037 |
| institution | OA Journals |
| issn | 1424-8220 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj-art-ddefe724685e45f1858d6295d25cd0372025-08-20T02:27:39ZengMDPI AGSensors1424-82202024-11-012422715910.3390/s24227159A Fractal Curve-Inspired Framework for Enhanced Semantic Segmentation of Remote Sensing ImagesXinhua Wang0Botao Yuan1Zhuang Li2Heqi Wang3School of Computer Science, Northeast Electric Power University, Jilin 132012, ChinaSchool of Computer Science, Northeast Electric Power University, Jilin 132012, ChinaSchool of Computer Science, Northeast Electric Power University, Jilin 132012, ChinaChangchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, ChinaThe classification and recognition of features play a vital role in production and daily life; however, the current semantic segmentation of remote sensing images is hampered by background interference and other factors, leading to issues such as fuzzy boundary segmentation. To address these challenges, we propose a novel module for encoding and reconstructing multi-dimensional feature layers. Our approach first utilizes a bilinear interpolation method to downsample the multi-dimensional feature layer in the coding stage of the U-shaped framework. Subsequently, we incorporate a fractal curve module into the encoder, which aggregates points on feature maps from different layers, effectively grouping points from diverse regions. Finally, we introduce an aggregation layer that combines the upsampling method from the UNet series, employing the multi-scale censoring of multi-dimensional feature map outputs from various layers to efficiently capture both spatial and feature information. The experimental results across diverse scenarios demonstrate that our model achieves excellent performance in aggregating point information from feature maps, significantly enhancing semantic segmentation tasks.https://www.mdpi.com/1424-8220/24/22/7159remote sensing imagesbilinear interpolationfractal curvegather layersencoder–decodersemantic segmentation |
| spellingShingle | Xinhua Wang Botao Yuan Zhuang Li Heqi Wang A Fractal Curve-Inspired Framework for Enhanced Semantic Segmentation of Remote Sensing Images Sensors remote sensing images bilinear interpolation fractal curve gather layers encoder–decoder semantic segmentation |
| title | A Fractal Curve-Inspired Framework for Enhanced Semantic Segmentation of Remote Sensing Images |
| title_full | A Fractal Curve-Inspired Framework for Enhanced Semantic Segmentation of Remote Sensing Images |
| title_fullStr | A Fractal Curve-Inspired Framework for Enhanced Semantic Segmentation of Remote Sensing Images |
| title_full_unstemmed | A Fractal Curve-Inspired Framework for Enhanced Semantic Segmentation of Remote Sensing Images |
| title_short | A Fractal Curve-Inspired Framework for Enhanced Semantic Segmentation of Remote Sensing Images |
| title_sort | fractal curve inspired framework for enhanced semantic segmentation of remote sensing images |
| topic | remote sensing images bilinear interpolation fractal curve gather layers encoder–decoder semantic segmentation |
| url | https://www.mdpi.com/1424-8220/24/22/7159 |
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