Combined Multi-Layer Feature Fusion and Edge Detection Method for Distributed Photovoltaic Power Station Identification

Yongshi Jie, Xianhua Ji, Anzhi Yue, Jingbo Chen, Yupeng Deng, Jing Chen and Yi Zhang
Additional contact information
Yongshi Jie: Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
Xianhua Ji: Engineering Quality Supervision Center of Logistics Support Department of the Military Commission, Beijing 100142, China
Anzhi Yue: Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
Jingbo Chen: Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
Yupeng Deng: Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
Jing Chen: Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
Yi Zhang: Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China

Energies, 2020, vol. 13, issue 24, 1-19

Abstract: Distributed photovoltaic power stations are an effective way to develop and utilize solar energy resources. Using high-resolution remote sensing images to obtain the locations, distribution, and areas of distributed photovoltaic power stations over a large region is important to energy companies, government departments, and investors. In this paper, a deep convolutional neural network was used to extract distributed photovoltaic power stations from high-resolution remote sensing images automatically, accurately, and efficiently. Based on a semantic segmentation model with an encoder-decoder structure, a gated fusion module was introduced to address the problem that small photovoltaic panels are difficult to identify. Further, to solve the problems of blurred edges in the segmentation results and that adjacent photovoltaic panels can easily be adhered, this work combines an edge detection network and a semantic segmentation network for multi-task learning to extract the boundaries of photovoltaic panels in a refined manner. Comparative experiments conducted on the Duke California Solar Array data set and a self-constructed Shanghai Distributed Photovoltaic Power Station data set show that, compared with SegNet, LinkNet, UNet, and FPN, the proposed method obtained the highest identification accuracy on both data sets, and its F1-scores reached 84.79% and 94.03%, respectively. These results indicate that effectively combining multi-layer features with a gated fusion module and introducing an edge detection network to refine the segmentation improves the accuracy of distributed photovoltaic power station identification.

Keywords: distributed photovoltaic power stations; remote sensing images; convolutional neural network; multi-layer features; edge (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (15)

Downloads: (external link)
https://www.mdpi.com/1996-1073/13/24/6742/pdf (application/pdf)
https://www.mdpi.com/1996-1073/13/24/6742/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:13:y:2020:i:24:p:6742-:d:465673

Access Statistics for this article

Energies is currently edited by Ms. Agatha Cao

More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().