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The automatic segmentation of residential solar panels based on satellite images: A cross learning driven U-Net method

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review

Abstract

Segmenting small-scale residential solar panels (RSPs) based on satellite images is an emerging data science problem in the renewable energy field. In this paper, we develop a cross learning driven U-Net (CrossNets) method and its extension, adaptive CrossNets, to automatically segment RSPs in satellite images. Proposed methods employ a group of generic U-Nets as a community and target to enhance the RSP segmentation performance. First, parameters of each generic U-Net in the community of CrossNets are initialized individually via the initialization with transfer learning and the classical initialization methods. Next, a novel training mechanism, cross learning, is developed to serve as a constraint for better optimizing CrossNets. Based on cross learning, each generic U-Net in the community first individually updates parameters at every epoch and next learns parameters from the best individual at specific epochs. Cross learning relieves the reliance of generic U-Nets on a careful initialization and better optimizes U-Nets. In testing, the result of the best performed generic U-Net in the community is selected as the final segmentation result of CrossNets. Adaptive CrossNets, a variant of CrossNets, is developed by applying an additional threshold to reduce the possibility of over-learning caused by cross learning. Satellite images collected from one city in U.S. are utilized to validate the performance of proposed methods. These images cover a large area of 135 km2 with 2794 RSPs. Compared with two generic U-Nets based benchmarks, our method can enhance the overall segmentation IoU by around 34% and 1.5%. Moreover, the segmentation robustness is improved from 1.191e−2 and 1.286e−4 to 2.481e−5. In addition, two new image datasets collected from other two cities in U.S. are applied to further examine the applicability of proposed methods.
Original languageEnglish
Article number106283
JournalApplied Soft Computing Journal
Volume92
Online published9 Apr 2020
DOIs
Publication statusPublished - Jul 2020

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

  1. SDG 7 - Affordable and Clean Energy
    SDG 7 Affordable and Clean Energy

Research Keywords

  • Computational intelligence
  • Data mining
  • Neural networks
  • Satellite images
  • Solar panels

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