CroplandCDNet: Cropland Change Detection Network for Multitemporal Remote Sensing Images Based on Multilayer Feature Transmission Fusion of an Adaptive Receptive Field
Dynamic monitoring of cropland using high spatial resolution remote sensing images is a powerful means to protect cropland resources. However, when a change detection method based on a convolutional neural network employs a large number of convolution and pooling operations to mine the deep features...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-03-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/16/6/1061 |
| _version_ | 1827305097202237440 |
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| author | Qiang Wu Liang Huang Bo-Hui Tang Jiapei Cheng Meiqi Wang Zixuan Zhang |
| author_facet | Qiang Wu Liang Huang Bo-Hui Tang Jiapei Cheng Meiqi Wang Zixuan Zhang |
| author_sort | Qiang Wu |
| collection | DOAJ |
| description | Dynamic monitoring of cropland using high spatial resolution remote sensing images is a powerful means to protect cropland resources. However, when a change detection method based on a convolutional neural network employs a large number of convolution and pooling operations to mine the deep features of cropland, the accumulation of irrelevant features and the loss of key features will lead to poor detection results. To effectively solve this problem, a novel cropland change detection network (CroplandCDNet) is proposed in this paper; this network combines an adaptive receptive field and multiscale feature transmission fusion to achieve accurate detection of cropland change information. CroplandCDNet first effectively extracts the multiscale features of cropland from bitemporal remote sensing images through the feature extraction module and subsequently embeds the receptive field adaptive SK attention (SKA) module to emphasize cropland change. Moreover, the SKA module effectively uses spatial context information for the dynamic adjustment of the convolution kernel size of cropland features at different scales. Finally, multiscale features and difference features are transmitted and fused layer by layer to obtain the content of cropland change. In the experiments, the proposed method is compared with six advanced change detection methods using the cropland change detection dataset (CLCD). The experimental results show that CroplandCDNet achieves the best F1 and OA at 76.04% and 94.47%, respectively. Its precision and recall are second best of all models at 76.46% and 75.63%, respectively. Moreover, a generalization experiment was carried out using the Jilin-1 dataset, which effectively verified the reliability of CroplandCDNet in cropland change detection. |
| first_indexed | 2024-04-24T17:51:23Z |
| format | Article |
| id | doaj.art-21cb41214cf44c98ab01b0b178346930 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-04-24T17:51:23Z |
| publishDate | 2024-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-21cb41214cf44c98ab01b0b1783469302024-03-27T14:02:45ZengMDPI AGRemote Sensing2072-42922024-03-01166106110.3390/rs16061061CroplandCDNet: Cropland Change Detection Network for Multitemporal Remote Sensing Images Based on Multilayer Feature Transmission Fusion of an Adaptive Receptive FieldQiang Wu0Liang Huang1Bo-Hui Tang2Jiapei Cheng3Meiqi Wang4Zixuan Zhang5Faculty of Land Resources Engineering, Kunming University of Science and Technology, Kunming 650093, ChinaFaculty of Land Resources Engineering, Kunming University of Science and Technology, Kunming 650093, ChinaFaculty of Land Resources Engineering, Kunming University of Science and Technology, Kunming 650093, ChinaFaculty of Land Resources Engineering, Kunming University of Science and Technology, Kunming 650093, ChinaFaculty of Land Resources Engineering, Kunming University of Science and Technology, Kunming 650093, ChinaFaculty of Land Resources Engineering, Kunming University of Science and Technology, Kunming 650093, ChinaDynamic monitoring of cropland using high spatial resolution remote sensing images is a powerful means to protect cropland resources. However, when a change detection method based on a convolutional neural network employs a large number of convolution and pooling operations to mine the deep features of cropland, the accumulation of irrelevant features and the loss of key features will lead to poor detection results. To effectively solve this problem, a novel cropland change detection network (CroplandCDNet) is proposed in this paper; this network combines an adaptive receptive field and multiscale feature transmission fusion to achieve accurate detection of cropland change information. CroplandCDNet first effectively extracts the multiscale features of cropland from bitemporal remote sensing images through the feature extraction module and subsequently embeds the receptive field adaptive SK attention (SKA) module to emphasize cropland change. Moreover, the SKA module effectively uses spatial context information for the dynamic adjustment of the convolution kernel size of cropland features at different scales. Finally, multiscale features and difference features are transmitted and fused layer by layer to obtain the content of cropland change. In the experiments, the proposed method is compared with six advanced change detection methods using the cropland change detection dataset (CLCD). The experimental results show that CroplandCDNet achieves the best F1 and OA at 76.04% and 94.47%, respectively. Its precision and recall are second best of all models at 76.46% and 75.63%, respectively. Moreover, a generalization experiment was carried out using the Jilin-1 dataset, which effectively verified the reliability of CroplandCDNet in cropland change detection.https://www.mdpi.com/2072-4292/16/6/1061cropland change detectionhigh spatial resolution remote sensing imagesadaptive receptive fielddeep learningattention mechanism |
| spellingShingle | Qiang Wu Liang Huang Bo-Hui Tang Jiapei Cheng Meiqi Wang Zixuan Zhang CroplandCDNet: Cropland Change Detection Network for Multitemporal Remote Sensing Images Based on Multilayer Feature Transmission Fusion of an Adaptive Receptive Field Remote Sensing cropland change detection high spatial resolution remote sensing images adaptive receptive field deep learning attention mechanism |
| title | CroplandCDNet: Cropland Change Detection Network for Multitemporal Remote Sensing Images Based on Multilayer Feature Transmission Fusion of an Adaptive Receptive Field |
| title_full | CroplandCDNet: Cropland Change Detection Network for Multitemporal Remote Sensing Images Based on Multilayer Feature Transmission Fusion of an Adaptive Receptive Field |
| title_fullStr | CroplandCDNet: Cropland Change Detection Network for Multitemporal Remote Sensing Images Based on Multilayer Feature Transmission Fusion of an Adaptive Receptive Field |
| title_full_unstemmed | CroplandCDNet: Cropland Change Detection Network for Multitemporal Remote Sensing Images Based on Multilayer Feature Transmission Fusion of an Adaptive Receptive Field |
| title_short | CroplandCDNet: Cropland Change Detection Network for Multitemporal Remote Sensing Images Based on Multilayer Feature Transmission Fusion of an Adaptive Receptive Field |
| title_sort | croplandcdnet cropland change detection network for multitemporal remote sensing images based on multilayer feature transmission fusion of an adaptive receptive field |
| topic | cropland change detection high spatial resolution remote sensing images adaptive receptive field deep learning attention mechanism |
| url | https://www.mdpi.com/2072-4292/16/6/1061 |
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