Illumination and shadow compensation of hyperspectral images using a digital surface model and non-linear least squares estimation

Ola Friman; Gustav Tolt; Jörgen Ahlberg

doi:10.1117/12.898084

26 October 2011 Illumination and shadow compensation of hyperspectral images using a digital surface model and non-linear least squares estimation

Ola Friman, Gustav Tolt, Jörgen Ahlberg

Author Affiliations +

Proceedings Volume 8180, Image and Signal Processing for Remote Sensing XVII; 81800Q (2011) https://doi.org/10.1117/12.898084
Event: SPIE Remote Sensing, 2011, Prague, Czech Republic

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.

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Ola Friman, Gustav Tolt, and Jörgen Ahlberg "Illumination and shadow compensation of hyperspectral images using a digital surface model and non-linear least squares estimation", Proc. SPIE 8180, Image and Signal Processing for Remote Sensing XVII, 81800Q (26 October 2011); https://doi.org/10.1117/12.898084

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