Estimation of daylight spectral power distribution from uncalibrated hyperspectral radiance images.
Opt Express
; 32(6): 10392-10407, 2024 Mar 11.
Article
in En
| MEDLINE
| ID: mdl-38571252
ABSTRACT
This paper introduces a novel framework for estimating the spectral power distribution of daylight illuminants in uncalibrated hyperspectral images, particularly beneficial for drone-based applications in agriculture and forestry. The proposed method uniquely combines image-dependent plausible spectra with a database of physically possible spectra, utilizing an image-independent principal component space (PCS) for estimations. This approach effectively narrows the search space in the spectral domain and employs a random walk methodology to generate spectral candidates, which are then intersected with a pre-trained PCS to predict the illuminant. We demonstrate superior performance compared to existing statistics-based methods across various metrics, validating the framework's efficacy in accurately estimating illuminants and recovering reflectance values from radiance data. The method is validated within the spectral range of 382-1002 nm and shows potential for extension to broader spectral ranges.
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Language:
En
Journal:
Opt Express
Journal subject:
OFTALMOLOGIA
Year:
2024
Document type:
Article
Country of publication: