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

Jordan M. Malof, Kyle Bradbury, Leslie M. Collins, Richard G. Newell, Alexander Serrano, Hetian Wu, Sam Keene

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

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 km2 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.

Original languageEnglish
Title of host publication2016 IEEE International Conference on Renewable Energy Research and Applications, ICRERA 2016
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages799-803
Number of pages5
ISBN (Electronic)9781509033881
DOIs
StatePublished - 2016
Event5th IEEE International Conference on Renewable Energy Research and Applications, ICRERA 2016 - Birmingham, United Kingdom
Duration: Nov 20 2016Nov 23 2016

Publication series

Name2016 IEEE International Conference on Renewable Energy Research and Applications, ICRERA 2016

Conference

Conference5th IEEE International Conference on Renewable Energy Research and Applications, ICRERA 2016
Country/TerritoryUnited Kingdom
CityBirmingham
Period11/20/1611/23/16

Keywords

  • Convolutional neural networks
  • Deep learning
  • Detection
  • Energy
  • Photovoltaic
  • Solar

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