Next Article in Journal
An Improved Stanford Method for Persistent Scatterers Applied to 3D Building Reconstruction and Monitoring
Previous Article in Journal
Dissecting Performances of PERSIANN-CDR Precipitation Product over Huai River Basin, China

The Depths of Cast Shadow

by 1,2,* and 1
Ecosystem Management, School of Environmental and Rural Science, University of New England, Armidale, NSW 2351, Australia
NSW Office of Environment & Heritage, Coffs Harbour, NSW 2450, Australia
Author to whom correspondence should be addressed.
Remote Sens. 2019, 11(15), 1806;
Received: 17 June 2019 / Revised: 24 July 2019 / Accepted: 30 July 2019 / Published: 1 August 2019
(This article belongs to the Section Environmental Remote Sensing)
To improve the accuracy of analysis outputs from remotely sensed images, shadow and illumination effects need to be minimised or removed. Shadow behaviour at different spectral wavelengths needs to be understood to quantify shadow accurately. This study examined whether a normalised spectral signature of shadow is invariant to sun–object–sensor geometry and can be used to quantify shadow depth. A “FieldSpec® Pro FR” Spectroradiometer and a Canon 450D digital SLR camera were used to measure signatures of cast shadow. Our field-based experiment used an occulter to cast shadow onto a ‘Spectralon’ white plate at six incremental zenith angles and evaluated shadow behaviour within and between varying footprints. A white-balanced image of each shadow zenith was taken by the Canon 450D. The FR Spectroradiometer signatures were normalised to unit vector form and compared to longitudinal transect profiles of shadow from normalised camera images using a scattering index (SI). The normalised signatures show that shadow depth is darker and more ‘blue’ at the proximal areas and conversely that image brightness values increases towards distal areas. Since image brightness is a result of sun–object–sensor geometry, we conclude that a normalised spectral signature is invariant to geometry and can be used to quantify shadow depth. View Full-Text
Keywords: shadow; shadow detection; shadow depth; illumination; diffuse skylight; imagery shadow; shadow detection; shadow depth; illumination; diffuse skylight; imagery
Show Figures

Graphical abstract

MDPI and ACS Style

Cameron, M.; Kumar, L. The Depths of Cast Shadow. Remote Sens. 2019, 11, 1806.

AMA Style

Cameron M, Kumar L. The Depths of Cast Shadow. Remote Sensing. 2019; 11(15):1806.

Chicago/Turabian Style

Cameron, Mark, and Lalit Kumar. 2019. "The Depths of Cast Shadow" Remote Sensing 11, no. 15: 1806.

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

Back to TopTop