Academic Journal
Extracting Photovoltaic Panels From Heterogeneous Remote Sensing Images With Spatial and Spectral Differences
| العنوان: | Extracting Photovoltaic Panels From Heterogeneous Remote Sensing Images With Spatial and Spectral Differences |
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| المؤلفون: | Zhiyu Zhao, Yunhao Chen, Kangning Li, Weizhen Ji, Hao Sun |
| المصدر: | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 17, Pp 5553-5564 (2024) |
| بيانات النشر: | IEEE, 2024. |
| سنة النشر: | 2024 |
| المجموعة: | LCC:Ocean engineering LCC:Geophysics. Cosmic physics |
| مصطلحات موضوعية: | Deep learning, feature extraction, photovoltaic (PV) panels, remote sensing, spatial and spectral differences, Ocean engineering, TC1501-1800, Geophysics. Cosmic physics, QC801-809 |
| الوصف: | The accurate extraction of the installation area of the photovoltaic power station is an important basis for the management of the photovoltaic power generation system. Deep learning has proven to be a powerful tool for rapidly detecting the distribution of photovoltaic panels in remote sensing images. The wealth of information from various remote sensing sensors aids in distinguishing photovoltaic pixels within complex backgrounds. However, the distinct imaging characteristics of different sensors present challenges for deep learning models. In this article, we propose a deep learning extraction method for photovoltaic panels that effectively improves the spatial and spectral differences inherent in remote sensing images. Considering the characteristics of different sensors, two attention modules and a feature fusion module are applied to suppress the inconsistency of spatial resolution and spectral resolution. Based on the Unet model, we implement the photovoltaic power station identification method and compare it with several commonly used semantic segmentation models. Qualitative and quantitative accuracy assessments show that the PV-Unet method can effectively overcome the spatial and spectral differences of remote sensing images. It achieves 98.04% F1 score and 96.15% IoU on the test dataset, verifying the superiority of this method. PV-Unet method has the potential for identifying photovoltaic panels from multisource remote sensing data. |
| نوع الوثيقة: | article |
| وصف الملف: | electronic resource |
| اللغة: | English |
| تدمد: | 2151-1535 |
| Relation: | https://ieeexplore.ieee.org/document/10444778/; https://doaj.org/toc/2151-1535 |
| DOI: | 10.1109/JSTARS.2024.3369660 |
| URL الوصول: | https://doaj.org/article/0a8f9188ed264871b31b0a880ca0ae73 |
| رقم الانضمام: | edsdoj.0a8f9188ed264871b31b0a880ca0ae73 |
| قاعدة البيانات: | Directory of Open Access Journals |
| تدمد: | 21511535 |
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| DOI: | 10.1109/JSTARS.2024.3369660 |