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.
Highlights
Renewable energy is a sustainable and inexhaustible energy, including biomass energy, wind energy, solar energy, etc., which plays an important role in solving the energy crisis
On the Duke California Solar Array data set, by adding the gated fusion module, the IoU of the test set was increased from 72.41% to 73.33%, F1 was increased from 84.00% to 84.61%, and recall was increased from 82.64% to 83.24%
This paper presented a novel fully connected convolutional neural network model that can automatically extract distributed photovoltaic power stations from remote sensing imagery
Summary
Renewable energy is a sustainable and inexhaustible energy, including biomass energy, wind energy, solar energy, etc., which plays an important role in solving the energy crisis. The main use of wind energy is to convert energy into electricity. Photovoltaic power generation is an effective way to use solar energy [3], of which there are two main forms: Centralized photovoltaic power generation and distributed photovoltaic power generation [4,5]. Centralized photovoltaic power stations are installed primarily in the desert and other ground areas and the generated electricity is usually incorporated into the national public power grid [6], while distributed photovoltaic power stations are generally installed on tops of buildings and the generated electricity is mainly for the inhabitants’
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