Fine-Grained Abandoned Cropland Mapping in Southern China Using Pixel Attention Contrastive Learning
Cropland abandonment has multifaceted and controversial impacts on the natural environment and socioeconomic development. Utilizing remote sensing data offers the potential for comprehensive coverage and large-scale insights into automated abandoned cropland identification. However, accurately captu...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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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 |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10336895/ |
| _version_ | 1797365910103654400 |
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| author | Haoyang Li Haomei Lin Junshen Luo Teng Wang Hao Chen Qiuting Xu Xinchang Zhang |
| author_facet | Haoyang Li Haomei Lin Junshen Luo Teng Wang Hao Chen Qiuting Xu Xinchang Zhang |
| author_sort | Haoyang Li |
| collection | DOAJ |
| description | Cropland abandonment has multifaceted and controversial impacts on the natural environment and socioeconomic development. Utilizing remote sensing data offers the potential for comprehensive coverage and large-scale insights into automated abandoned cropland identification. However, accurately capturing small abandoned cropland, particularly in regions, such as southern China, with fragmentized farmland, poses a significant challenge using the traditional optical image-based mapping methods due to their coarse spatial resolution. In addition, irregular and chaotic textures of abandoned cropland further complicate the accurate prediction using very high resolution (VHR) data. In this article, we propose a novel deep learning network termed pixel attention contrastive network (PACnet) to map fine-grained abandoned cropland based on VHR data. Cross-image pixel contrast learning is introduced to discern distinctive features distinguishing abandoned cropland from other land types across various interimages. Moreover, a criss-cross attention module is embedded to enhance the contrasting characteristics within individual intraimages. Experimental outcomes validate the efficacy of PACnet, showcasing the highest accuracy (OA = 93.8% and mIOU = 71.7%) when compared with classical semantic segmentation networks. Our proposal not only underscores the potency of VHR remote sensing data in finely delineating abandoned cropland but also carries significant implications for cropland abandonment impact analysis and informed policy formulation. |
| first_indexed | 2024-03-08T16:56:39Z |
| format | Article |
| id | doaj.art-31a78f74c3014c0299d708e49580fe5c |
| institution | Directory Open Access Journal |
| issn | 2151-1535 |
| language | English |
| last_indexed | 2024-03-08T16:56:39Z |
| 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-31a78f74c3014c0299d708e49580fe5c2024-01-05T00:00:58ZengIEEEIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing2151-15352024-01-01172283229510.1109/JSTARS.2023.333845410336895Fine-Grained Abandoned Cropland Mapping in Southern China Using Pixel Attention Contrastive LearningHaoyang Li0https://orcid.org/0000-0001-8725-342XHaomei Lin1Junshen Luo2https://orcid.org/0009-0003-3386-7401Teng Wang3https://orcid.org/0009-0002-7142-572XHao Chen4Qiuting Xu5Xinchang Zhang6https://orcid.org/0000-0001-8463-9757Guangdong Provincial Key Laboratory for Urbanization and Geo-simulation, School of Geography and Planning, Sun Yat-sen University, Guangzhou, ChinaSchool of Geography and Planning, Sun Yat-sen University, Guangzhou, ChinaSchool of Geography and Planning, Sun Yat-sen University, Guangzhou, ChinaGuangdong Department of Land and Resources, Institute of Surveying and Mapping, Guangzhou, ChinaGuangdong Department of Land and Resources, Institute of Surveying and Mapping, Guangzhou, ChinaGuangdong Department of Land and Resources, Institute of Surveying and Mapping, Guangzhou, ChinaSchool of Geography and Remote Sensing, Guangzhou University, Guangzhou, ChinaCropland abandonment has multifaceted and controversial impacts on the natural environment and socioeconomic development. Utilizing remote sensing data offers the potential for comprehensive coverage and large-scale insights into automated abandoned cropland identification. However, accurately capturing small abandoned cropland, particularly in regions, such as southern China, with fragmentized farmland, poses a significant challenge using the traditional optical image-based mapping methods due to their coarse spatial resolution. In addition, irregular and chaotic textures of abandoned cropland further complicate the accurate prediction using very high resolution (VHR) data. In this article, we propose a novel deep learning network termed pixel attention contrastive network (PACnet) to map fine-grained abandoned cropland based on VHR data. Cross-image pixel contrast learning is introduced to discern distinctive features distinguishing abandoned cropland from other land types across various interimages. Moreover, a criss-cross attention module is embedded to enhance the contrasting characteristics within individual intraimages. Experimental outcomes validate the efficacy of PACnet, showcasing the highest accuracy (OA = 93.8% and mIOU = 71.7%) when compared with classical semantic segmentation networks. Our proposal not only underscores the potency of VHR remote sensing data in finely delineating abandoned cropland but also carries significant implications for cropland abandonment impact analysis and informed policy formulation.https://ieeexplore.ieee.org/document/10336895/Abandoned croplandcontrastive learningdeep learning (DL)very high resolution (VHR) |
| spellingShingle | Haoyang Li Haomei Lin Junshen Luo Teng Wang Hao Chen Qiuting Xu Xinchang Zhang Fine-Grained Abandoned Cropland Mapping in Southern China Using Pixel Attention Contrastive Learning IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Abandoned cropland contrastive learning deep learning (DL) very high resolution (VHR) |
| title | Fine-Grained Abandoned Cropland Mapping in Southern China Using Pixel Attention Contrastive Learning |
| title_full | Fine-Grained Abandoned Cropland Mapping in Southern China Using Pixel Attention Contrastive Learning |
| title_fullStr | Fine-Grained Abandoned Cropland Mapping in Southern China Using Pixel Attention Contrastive Learning |
| title_full_unstemmed | Fine-Grained Abandoned Cropland Mapping in Southern China Using Pixel Attention Contrastive Learning |
| title_short | Fine-Grained Abandoned Cropland Mapping in Southern China Using Pixel Attention Contrastive Learning |
| title_sort | fine grained abandoned cropland mapping in southern china using pixel attention contrastive learning |
| topic | Abandoned cropland contrastive learning deep learning (DL) very high resolution (VHR) |
| url | https://ieeexplore.ieee.org/document/10336895/ |
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