DDAM-Net: A Difference-Directed Multi-Scale Attention Mechanism Network for Cultivated Land Change Detection
Declining cultivated land poses a serious threat to food security. However, existing Change Detection (CD) methods are insufficient for overcoming intra-class differences in cropland, and the accumulation of irrelevant features and loss of key features leads to poor detection results. To effectively...
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MDPI AG
2024-10-01
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| Online Access: | https://www.mdpi.com/1424-8220/24/21/7040 |
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| author | Junbiao Feng Haikun Yu Xiaoping Lu Xiaoran Lv Junli Zhou |
| author_facet | Junbiao Feng Haikun Yu Xiaoping Lu Xiaoran Lv Junli Zhou |
| author_sort | Junbiao Feng |
| collection | DOAJ |
| description | Declining cultivated land poses a serious threat to food security. However, existing Change Detection (CD) methods are insufficient for overcoming intra-class differences in cropland, and the accumulation of irrelevant features and loss of key features leads to poor detection results. To effectively identify changes in agricultural land, we propose a Difference-Directed Multi-scale Attention Mechanism Network (DDAM-Net). Specifically, we use a feature extraction module to effectively extract the cropland’s multi-scale features from dual-temporal images, and we introduce a Difference Enhancement Fusion Module (DEFM) and a Cross-scale Aggregation Module (CAM) to pass and fuse the multi-scale and difference features layer by layer. In addition, we introduce the Attention Refinement Module (ARM) to optimize the edge and detail features of changing objects. In the experiments, we evaluated the applicability of DDAM-Net on the HN-CLCD dataset for cropland CD and non-agricultural identification, with F1 and precision of 79.27% and 80.70%, respectively. In addition, generalization experiments using the publicly accessible PX-CLCD and SET-CLCD datasets revealed F1 and precision values of 95.12% and 95.47%, and 72.40% and 77.59%, respectively. The relevant comparative and ablation experiments suggested that DDAM-Net has greater performance and reliability in detecting cropland changes. |
| format | Article |
| id | doaj-art-61bb11203afb40efb46fb2baac81e1c5 |
| institution | OA Journals |
| issn | 1424-8220 |
| language | English |
| publishDate | 2024-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj-art-61bb11203afb40efb46fb2baac81e1c52025-08-20T02:14:16ZengMDPI AGSensors1424-82202024-10-012421704010.3390/s24217040DDAM-Net: A Difference-Directed Multi-Scale Attention Mechanism Network for Cultivated Land Change DetectionJunbiao Feng0Haikun Yu1Xiaoping Lu2Xiaoran Lv3Junli Zhou4Key Laboratory of Spatio-Temporal Information and Ecological Restoration of Mines of Natural Resources of the People’s Republic of China, Henan Polytechnic University, Jiaozuo 454003, ChinaHenan Remote Sensing and Mapping Institute, Zhengzhou 450003, ChinaKey Laboratory of Spatio-Temporal Information and Ecological Restoration of Mines of Natural Resources of the People’s Republic of China, Henan Polytechnic University, Jiaozuo 454003, ChinaKey Laboratory of Spatio-Temporal Information and Ecological Restoration of Mines of Natural Resources of the People’s Republic of China, Henan Polytechnic University, Jiaozuo 454003, ChinaHenan Remote Sensing and Mapping Institute, Zhengzhou 450003, ChinaDeclining cultivated land poses a serious threat to food security. However, existing Change Detection (CD) methods are insufficient for overcoming intra-class differences in cropland, and the accumulation of irrelevant features and loss of key features leads to poor detection results. To effectively identify changes in agricultural land, we propose a Difference-Directed Multi-scale Attention Mechanism Network (DDAM-Net). Specifically, we use a feature extraction module to effectively extract the cropland’s multi-scale features from dual-temporal images, and we introduce a Difference Enhancement Fusion Module (DEFM) and a Cross-scale Aggregation Module (CAM) to pass and fuse the multi-scale and difference features layer by layer. In addition, we introduce the Attention Refinement Module (ARM) to optimize the edge and detail features of changing objects. In the experiments, we evaluated the applicability of DDAM-Net on the HN-CLCD dataset for cropland CD and non-agricultural identification, with F1 and precision of 79.27% and 80.70%, respectively. In addition, generalization experiments using the publicly accessible PX-CLCD and SET-CLCD datasets revealed F1 and precision values of 95.12% and 95.47%, and 72.40% and 77.59%, respectively. The relevant comparative and ablation experiments suggested that DDAM-Net has greater performance and reliability in detecting cropland changes.https://www.mdpi.com/1424-8220/24/21/7040non-agricultural changehigh-resolution remote-sensing imagesattention mechanismchange detectionself-built dataset |
| spellingShingle | Junbiao Feng Haikun Yu Xiaoping Lu Xiaoran Lv Junli Zhou DDAM-Net: A Difference-Directed Multi-Scale Attention Mechanism Network for Cultivated Land Change Detection Sensors non-agricultural change high-resolution remote-sensing images attention mechanism change detection self-built dataset |
| title | DDAM-Net: A Difference-Directed Multi-Scale Attention Mechanism Network for Cultivated Land Change Detection |
| title_full | DDAM-Net: A Difference-Directed Multi-Scale Attention Mechanism Network for Cultivated Land Change Detection |
| title_fullStr | DDAM-Net: A Difference-Directed Multi-Scale Attention Mechanism Network for Cultivated Land Change Detection |
| title_full_unstemmed | DDAM-Net: A Difference-Directed Multi-Scale Attention Mechanism Network for Cultivated Land Change Detection |
| title_short | DDAM-Net: A Difference-Directed Multi-Scale Attention Mechanism Network for Cultivated Land Change Detection |
| title_sort | ddam net a difference directed multi scale attention mechanism network for cultivated land change detection |
| topic | non-agricultural change high-resolution remote-sensing images attention mechanism change detection self-built dataset |
| url | https://www.mdpi.com/1424-8220/24/21/7040 |
| work_keys_str_mv | AT junbiaofeng ddamnetadifferencedirectedmultiscaleattentionmechanismnetworkforcultivatedlandchangedetection AT haikunyu ddamnetadifferencedirectedmultiscaleattentionmechanismnetworkforcultivatedlandchangedetection AT xiaopinglu ddamnetadifferencedirectedmultiscaleattentionmechanismnetworkforcultivatedlandchangedetection AT xiaoranlv ddamnetadifferencedirectedmultiscaleattentionmechanismnetworkforcultivatedlandchangedetection AT junlizhou ddamnetadifferencedirectedmultiscaleattentionmechanismnetworkforcultivatedlandchangedetection |