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Proceedings Paper

Illumination and shadow compensation of hyperspectral images using a digital surface model and non-linear least squares estimation
Author(s): Ola Friman; Gustav Tolt; Jörgen Ahlberg
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Paper Abstract

Object detection and material classification are two central tasks in electro-optical remote sensing and hyperspectral imaging applications. These are challenging problems as the measured spectra in hyperspectral images from satellite or airborne platforms vary significantly depending on the light conditions at the imaged surface, e.g., shadow versus non-shadow. In this work, a Digital Surface Model (DSM) is used to estimate different components of the incident light. These light components are subsequently used to predict what a measured spectrum would look like under different light conditions. The derived method is evaluated using an urban hyperspectral data set with 24 bands in the wavelength range 381.9 nm to 1040.4 nm and a DSM created from LIDAR 3D data acquired simultaneously with the hyperspectral data.

Paper Details

Date Published: 26 October 2011
PDF: 8 pages
Proc. SPIE 8180, Image and Signal Processing for Remote Sensing XVII, 81800Q (26 October 2011); doi: 10.1117/12.898084
Show Author Affiliations
Ola Friman, Swedish Defence Research Agency (Sweden)
Gustav Tolt, Swedish Defence Research Agency (Sweden)
Jörgen Ahlberg, Termisk Systemteknik (Sweden)

Published in SPIE Proceedings Vol. 8180:
Image and Signal Processing for Remote Sensing XVII
Lorenzo Bruzzone, Editor(s)

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