TY - GEN
T1 - Image features for pixel-wise detection of solar photovoltaic arrays in aerial imagery using a random forest classifier
AU - Malof, Jordan M.
AU - Bradbury, Kyle
AU - Collins, Leslie M.
AU - Newell, Richard G.
AU - Serrano, Alexander
AU - Wu, Hetian
AU - Keene, Sam
N1 - Publisher Copyright:
© 2016 IEEE.
PY - 2016
Y1 - 2016
N2 - 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.
AB - 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.
KW - Convolutional neural networks
KW - Deep learning
KW - Detection
KW - Energy
KW - Photovoltaic
KW - Solar
UR - http://www.scopus.com/inward/record.url?scp=85017230792&partnerID=8YFLogxK
U2 - 10.1109/ICRERA.2016.7884446
DO - 10.1109/ICRERA.2016.7884446
M3 - Conference contribution
AN - SCOPUS:85017230792
T3 - 2016 IEEE International Conference on Renewable Energy Research and Applications, ICRERA 2016
SP - 799
EP - 803
BT - 2016 IEEE International Conference on Renewable Energy Research and Applications, ICRERA 2016
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 5th IEEE International Conference on Renewable Energy Research and Applications, ICRERA 2016
Y2 - 20 November 2016 through 23 November 2016
ER -
