Image features for pixel-wise detection of solar photovoltaic arrays in aerial imagery using a random forest classifier

Conference Paper

© 2016 IEEE. Power generation from distributed solar photovoltaic (PV) arrays has grown rapidly in recent years. As a result, there is interest in collecting information about the quantity, power capacity, and energy generated by such arrays; and to do so over small geo-spatial regions (e.g., counties, cities, or even smaller regions). Unfortunately, existing sources of such information are dispersed, limited in geospatial resolution, and otherwise incomplete or publically unavailable. As result, we recently proposed a new approach for collecting such distributed PV information that relies on computer algorithms to automatically detect PV arrays in high resolution aerial imagery [1], Here, we build on this work by investigating a detection algorithm based on a Random Forest (RF) classifier, and we consider its detection performance using several different sets of image features. The proposed method is developed and tested using a very large collection of publicly available [2] aerial imagery, covering 112.5 km 2 of surface area, with 2,328 manually annotated PV array locations. The results indicate that a combination of local color and texture (using the popular texton feature) features yield the best detection performance.

Full Text

Duke Authors

Cited Authors

  • Malof, JM; Bradbury, K; Collins, LM; Newell, RG; Serrano, A; Wu, H; Keene, S

Published Date

  • March 21, 2017

Published In

  • 2016 IEEE International Conference on Renewable Energy Research and Applications, ICRERA 2016

Start / End Page

  • 799 - 803

International Standard Book Number 13 (ISBN-13)

  • 9781509033881

Digital Object Identifier (DOI)

  • 10.1109/ICRERA.2016.7884446

Citation Source

  • Scopus