Detecting photovoltaic solar panels using hyperspectral imagery and estimating solar power production

Daniel W. Czirjak

doi:10.1117/1.JRS.11.026007

14 April 2017 Detecting photovoltaic solar panels using hyperspectral imagery and estimating solar power production

Daniel W. Czirjak

Author Affiliations +

Journal of Applied Remote Sensing, Vol. 11, Issue 2, 026007 (April 2017). https://doi.org/10.1117/1.JRS.11.026007

Abstract

Remote sensing platforms have consistently demonstrated the ability to detect, and in some cases identify, specific targets of interest, and photovoltaic solar panels are shown to have a unique spectral signature that is consistent across multiple manufacturers and construction methods. Solar panels are proven to be detectable in hyperspectral imagery using common statistical target detection methods such as the adaptive cosine estimator, and false alarms can be mitigated through the use of a spectral verification process that eliminates pixels that do not have the key spectral features of photovoltaic solar panel reflectance spectrum. The normalized solar panel index is described and is a key component in the false-alarm mitigation process. After spectral verification, these solar panel arrays are confirmed on openly available literal imagery and can be measured using numerous open-source algorithms and tools. The measurements allow for the assessment of overall solar power generation capacity using an equation that accounts for solar insolation, the area of solar panels, and the efficiency of the solar panels conversion of solar energy to power. Using a known location with readily available information, the methods outlined in this paper estimate the power generation capabilities within 6% of the rated power.

Citation Download Citation

Daniel W. Czirjak "Detecting photovoltaic solar panels using hyperspectral imagery and estimating solar power production," Journal of Applied Remote Sensing 11(2), 026007 (14 April 2017). https://doi.org/10.1117/1.JRS.11.026007

Received: 12 December 2016; Accepted: 28 March 2017; Published: 14 April 2017

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available