A deep convolutional neural network, with pre-training, for solar photovoltaic array detection in aerial imagery

Published

Conference Paper

© 2017 IEEE. In this work we consider the problem of developing algorithms that automatically identify small-scale solar photovoltaic arrays in high resolution aerial imagery. Such algorithms potentially offer a faster and cheaper solution to collecting small-scale photovoltaic (PV) information, such as their location, capacity, and the energy they produce. Here we build on previous algorithmic work by employing convolutional neural networks (CNNs), which have recently yielded major improvements in other image object recognition problems. We propose a CNN architecture for our recognition problem and then measure its detection performance on the same (publicly available) dataset that was used in previous publications. The results indicate that the CNN yields substantial performance improvements over previous results. We also investigate the recently popular approach of pre-training for CNNs.

Full Text

Duke Authors

Cited Authors

  • Malof, JM; Collins, LM; Bradbury, K

Published Date

  • December 1, 2017

Published In

  • International Geoscience and Remote Sensing Symposium (Igarss)

Volume / Issue

  • 2017-July /

Start / End Page

  • 874 - 877

International Standard Book Number 13 (ISBN-13)

  • 9781509049516

Digital Object Identifier (DOI)

  • 10.1109/IGARSS.2017.8127092

Citation Source

  • Scopus