Improved U-Net Remote Sensing Classification Algorithm Based on Multi-Feature Fusion Perception
The selection and representation of remote sensing image classification features play crucial roles in image classification accuracy. To effectively improve the classification accuracy of features, an improved U-Net network framework based on multi-feature fusion perception is proposed in this paper...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-02-01
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| Series: | Remote Sensing |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2072-4292/14/5/1118 |
| _version_ | 1827650744658952192 |
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| author | Chuan Yan Xiangsuo Fan Jinlong Fan Nayi Wang |
| author_facet | Chuan Yan Xiangsuo Fan Jinlong Fan Nayi Wang |
| author_sort | Chuan Yan |
| collection | DOAJ |
| description | The selection and representation of remote sensing image classification features play crucial roles in image classification accuracy. To effectively improve the classification accuracy of features, an improved U-Net network framework based on multi-feature fusion perception is proposed in this paper. This framework adds the channel attention module (CAM-UNet) to the original U-Net framework and cascades the shallow features with the deep semantic features, replaces the classification layer in the original U-Net network with a support vector machine, and finally uses the majority voting game theory algorithm to fuse the multifeature classification results and obtain the final classification results. This study used the forest distribution in Xingbin District, Laibin City, Guangxi Zhuang Autonomous Region as the research object, which is based on Landsat 8 multispectral remote sensing images, and, by combining spectral features, spatial features, and advanced semantic features, overcame the influence of the reduction in spatial resolution that occurs with the deepening of the network on the classification results. The experimental results showed that the improved algorithm can improve classification accuracy. Before the improvement, the overall segmentation accuracy and segmentation accuracy of the forestland increased from 90.50% to 92.82% and from 95.66% to 97.16%, respectively. The forest cover results obtained by the algorithm proposed in this paper can be used as input data for regional ecological models, which is conducive to the development of accurate and real-time vegetation growth change models. |
| first_indexed | 2024-03-09T20:23:20Z |
| format | Article |
| id | doaj.art-7202f8f3d4d049e19a61b7ce146982ba |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-09T20:23:20Z |
| publishDate | 2022-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-7202f8f3d4d049e19a61b7ce146982ba2023-11-23T23:41:41ZengMDPI AGRemote Sensing2072-42922022-02-01145111810.3390/rs14051118Improved U-Net Remote Sensing Classification Algorithm Based on Multi-Feature Fusion PerceptionChuan Yan0Xiangsuo Fan1Jinlong Fan2Nayi Wang3School of Electrical, Electronic and Computer Science, Guangxi University of Science and Technology, Liuzhou 545006, ChinaSchool of Electrical, Electronic and Computer Science, Guangxi University of Science and Technology, Liuzhou 545006, ChinaNational Satellite Meteorological Center of China Meteorological Administratio, Beijing 100089, ChinaSchool of Electrical, Electronic and Computer Science, Guangxi University of Science and Technology, Liuzhou 545006, ChinaThe selection and representation of remote sensing image classification features play crucial roles in image classification accuracy. To effectively improve the classification accuracy of features, an improved U-Net network framework based on multi-feature fusion perception is proposed in this paper. This framework adds the channel attention module (CAM-UNet) to the original U-Net framework and cascades the shallow features with the deep semantic features, replaces the classification layer in the original U-Net network with a support vector machine, and finally uses the majority voting game theory algorithm to fuse the multifeature classification results and obtain the final classification results. This study used the forest distribution in Xingbin District, Laibin City, Guangxi Zhuang Autonomous Region as the research object, which is based on Landsat 8 multispectral remote sensing images, and, by combining spectral features, spatial features, and advanced semantic features, overcame the influence of the reduction in spatial resolution that occurs with the deepening of the network on the classification results. The experimental results showed that the improved algorithm can improve classification accuracy. Before the improvement, the overall segmentation accuracy and segmentation accuracy of the forestland increased from 90.50% to 92.82% and from 95.66% to 97.16%, respectively. The forest cover results obtained by the algorithm proposed in this paper can be used as input data for regional ecological models, which is conducive to the development of accurate and real-time vegetation growth change models.https://www.mdpi.com/2072-4292/14/5/1118multifeature fusionU-Netchannel attentionremote sensing image classificationmajority voting game |
| spellingShingle | Chuan Yan Xiangsuo Fan Jinlong Fan Nayi Wang Improved U-Net Remote Sensing Classification Algorithm Based on Multi-Feature Fusion Perception Remote Sensing multifeature fusion U-Net channel attention remote sensing image classification majority voting game |
| title | Improved U-Net Remote Sensing Classification Algorithm Based on Multi-Feature Fusion Perception |
| title_full | Improved U-Net Remote Sensing Classification Algorithm Based on Multi-Feature Fusion Perception |
| title_fullStr | Improved U-Net Remote Sensing Classification Algorithm Based on Multi-Feature Fusion Perception |
| title_full_unstemmed | Improved U-Net Remote Sensing Classification Algorithm Based on Multi-Feature Fusion Perception |
| title_short | Improved U-Net Remote Sensing Classification Algorithm Based on Multi-Feature Fusion Perception |
| title_sort | improved u net remote sensing classification algorithm based on multi feature fusion perception |
| topic | multifeature fusion U-Net channel attention remote sensing image classification majority voting game |
| url | https://www.mdpi.com/2072-4292/14/5/1118 |
| work_keys_str_mv | AT chuanyan improvedunetremotesensingclassificationalgorithmbasedonmultifeaturefusionperception AT xiangsuofan improvedunetremotesensingclassificationalgorithmbasedonmultifeaturefusionperception AT jinlongfan improvedunetremotesensingclassificationalgorithmbasedonmultifeaturefusionperception AT nayiwang improvedunetremotesensingclassificationalgorithmbasedonmultifeaturefusionperception |