Journal
GISCIENCE & REMOTE SENSING
Volume 54, Issue 4, Pages 453-470Publisher
TAYLOR & FRANCIS LTD
DOI: 10.1080/15481603.2017.1279729
Keywords
shadow classification; shadow normalization; shadow detection; post-hazard damage assessment; change detection
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Funding
- National Science Foundation Directorate of Engineering, Infrastructure Management and Extreme Events (IMEE) program [G00010529]
- United States Department of Transportation (USDOT) Office of the Assistant Secretary for Research & Technology (OST-R) Commercial Remote Sensing and Spatial Information (CRS&SI) Technologies Program [OASRTRS-14-H-UNM]
- Department of Geography at San Diego State University
- Div Of Civil, Mechanical, & Manufact Inn
- Directorate For Engineering [1361222] Funding Source: National Science Foundation
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Multi-temporal aerial imagery captured via an approach called repeat station imaging (RSI) facilitates post-hazard assessment of damage to infrastructure. Spectral-radiometric (SR) variations caused by differences in shadowing may inhibit successful change detection based on image differencing. This study evaluates a novel approach to shadow classification based on bi-temporal imagery, which exploits SR change signatures associated with transient shadows. Changes in intensity (brightness from red-green-blue images) and intensity-normalized blue waveband values provide a basis for classifying transient shadows across a range of material types with unique reflectance properties, using thresholds that proved versatile for very different scenes. We derive classification thresholds for persistent shadows based on hue to intensity ratio (H/I) images, by exploiting statistics obtained from transient shadow areas. We assess shadow classification accuracy based on this procedure, and compare it to the more conventional approach of thresholding individual H/I images based on frequency distributions. Our efficient and semi-automated shadow classification procedure shows improved mean accuracy (93.3%) and versatility with different image sets over the conventional approach (84.7%). For proof-of-concept, we demonstrate that overlaying bi-temporal imagery also facilitates normalization of intensity values in transient shadow areas, as part of an integrated procedure to support near-real-time change detection.
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