CSPPNet: A Convolution and State-Space-Based Photovoltaic Panel Extraction Network Using Gaofen-2 High-Resolution Imagery
With the rapid development of photovoltaic (PV) industry, it is crucial to accurately identify PV panels using remote sensing data. However, the existing methods still face problems, such as difficulty in distinguishing PV panels from easily confused ground objects, such as dark buildings, roads, an...
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| Format: | Article |
| Language: | English |
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IEEE
2025-01-01
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| Series: | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
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| Online Access: | https://ieeexplore.ieee.org/document/10892037/ |
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| author | Wenqing Liu Hongtao Huo Luyan Ji Yongchao Zhao Xiaowen Liu Jialei Xie |
| author_facet | Wenqing Liu Hongtao Huo Luyan Ji Yongchao Zhao Xiaowen Liu Jialei Xie |
| author_sort | Wenqing Liu |
| collection | DOAJ |
| description | With the rapid development of photovoltaic (PV) industry, it is crucial to accurately identify PV panels using remote sensing data. However, the existing methods still face problems, such as difficulty in distinguishing PV panels from easily confused ground objects, such as dark buildings, roads, and black plastic, lacking the analysis of the directional characteristics, and inadequate capturing the global dependencies. To address these challenges, a convolution and state-space-based photovoltaic panel extraction network (CSPPNet) is proposed. Specifically, we construct a PV panel index based on visible and near-infrared bands to improve the separability of the PV panels from those easily confused ground objects. Second, considering the unique horizontal characteristics exhibited by the PV panels in remote sensing images, a south-facing orientation prior module is designed to enhance the horizontal features and improve our network in capturing horizontal objects. Finally, the encoder of our network adopts a parallel structure of depthwise separable convolution and state-space module to capture local detailed features and global semantic features of PV panels layer by layer. Furthermore, we build a high-quality dataset [named Photovoltaic Panels in the Eastern and Western Regions of China (PPEWRC)] containing four bands ranging from visible to near-infrared wavelength based on Gaofen-2 satellite images. The experimental results show that our proposed CSPPNet effectively reduces the misjudgment of PV panels, with high completeness and clear edges. The intersection over union, precision, recall, and <italic>F</italic>1-score can reach 77.37%, 87.42%, 85.11%, and 86.98%, respectively, on the PPEWRC dataset. Ablation experiments validate the effectiveness of each module and fusion process, providing insights into the sustainable utilization of renewable energy. |
| format | Article |
| id | doaj-art-8fdef4cceb2d457bb861ef0750e54bbb |
| institution | Kabale University |
| issn | 1939-1404 2151-1535 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
| spelling | doaj-art-8fdef4cceb2d457bb861ef0750e54bbb2025-08-20T03:40:15ZengIEEEIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing1939-14042151-15352025-01-01187644766110.1109/JSTARS.2025.354349010892037CSPPNet: A Convolution and State-Space-Based Photovoltaic Panel Extraction Network Using Gaofen-2 High-Resolution ImageryWenqing Liu0Hongtao Huo1https://orcid.org/0000-0002-1552-4400Luyan Ji2https://orcid.org/0000-0001-5369-4200Yongchao Zhao3Xiaowen Liu4Jialei Xie5https://orcid.org/0009-0007-0223-0554Department of Information and Cyber Security, People's Public Security University of China, Beijing, ChinaDepartment of Information and Cyber Security, People's Public Security University of China, Beijing, ChinaAerospace Information Research Institute, Chinese Academy of Sciences, Beijing, ChinaAerospace Information Research Institute, Chinese Academy of Sciences, Beijing, ChinaDepartment of Information and Cyber Security, People's Public Security University of China, Beijing, ChinaAerospace Information Research Institute, Chinese Academy of Sciences, Beijing, ChinaWith the rapid development of photovoltaic (PV) industry, it is crucial to accurately identify PV panels using remote sensing data. However, the existing methods still face problems, such as difficulty in distinguishing PV panels from easily confused ground objects, such as dark buildings, roads, and black plastic, lacking the analysis of the directional characteristics, and inadequate capturing the global dependencies. To address these challenges, a convolution and state-space-based photovoltaic panel extraction network (CSPPNet) is proposed. Specifically, we construct a PV panel index based on visible and near-infrared bands to improve the separability of the PV panels from those easily confused ground objects. Second, considering the unique horizontal characteristics exhibited by the PV panels in remote sensing images, a south-facing orientation prior module is designed to enhance the horizontal features and improve our network in capturing horizontal objects. Finally, the encoder of our network adopts a parallel structure of depthwise separable convolution and state-space module to capture local detailed features and global semantic features of PV panels layer by layer. Furthermore, we build a high-quality dataset [named Photovoltaic Panels in the Eastern and Western Regions of China (PPEWRC)] containing four bands ranging from visible to near-infrared wavelength based on Gaofen-2 satellite images. The experimental results show that our proposed CSPPNet effectively reduces the misjudgment of PV panels, with high completeness and clear edges. The intersection over union, precision, recall, and <italic>F</italic>1-score can reach 77.37%, 87.42%, 85.11%, and 86.98%, respectively, on the PPEWRC dataset. Ablation experiments validate the effectiveness of each module and fusion process, providing insights into the sustainable utilization of renewable energy.https://ieeexplore.ieee.org/document/10892037/Horizontal featuresphotovoltaic (PV) panel extractionremote sensing imagesstate space |
| spellingShingle | Wenqing Liu Hongtao Huo Luyan Ji Yongchao Zhao Xiaowen Liu Jialei Xie CSPPNet: A Convolution and State-Space-Based Photovoltaic Panel Extraction Network Using Gaofen-2 High-Resolution Imagery IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Horizontal features photovoltaic (PV) panel extraction remote sensing images state space |
| title | CSPPNet: A Convolution and State-Space-Based Photovoltaic Panel Extraction Network Using Gaofen-2 High-Resolution Imagery |
| title_full | CSPPNet: A Convolution and State-Space-Based Photovoltaic Panel Extraction Network Using Gaofen-2 High-Resolution Imagery |
| title_fullStr | CSPPNet: A Convolution and State-Space-Based Photovoltaic Panel Extraction Network Using Gaofen-2 High-Resolution Imagery |
| title_full_unstemmed | CSPPNet: A Convolution and State-Space-Based Photovoltaic Panel Extraction Network Using Gaofen-2 High-Resolution Imagery |
| title_short | CSPPNet: A Convolution and State-Space-Based Photovoltaic Panel Extraction Network Using Gaofen-2 High-Resolution Imagery |
| title_sort | csppnet a convolution and state space based photovoltaic panel extraction network using gaofen 2 high resolution imagery |
| topic | Horizontal features photovoltaic (PV) panel extraction remote sensing images state space |
| url | https://ieeexplore.ieee.org/document/10892037/ |
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