TY - JOUR
T1 - Spectral Reflectance Processing via Local Wavelength-Direction Correlations
AU - Xu, Guanglang
AU - Gritsevich, Maria
AU - Peltoniemi, Jouni
AU - Penttilä, Antti
AU - Wilkman, Olli
AU - Ihalainen, Olli
AU - Muinonen, Karri
N1 - Funding Information:
Manuscript received June 5, 2019; revised August 13, 2019; accepted August 31, 2019. Date of publication September 20, 2019; date of current version May 21, 2020. This work was supported by the Academy of Finland Consortium Project Albedo under Project 298137 and Project 298139. (Corresponding author: Guanglang Xu.) G. Xu, A. Penttilä, and O. Ihalainen are with the Department of Physics, University of Helsinki, 00100 Helsinki, Finland (e-mail: [email protected]).
Publisher Copyright:
© 2019 IEEE.
PY - 2020/6
Y1 - 2020/6
N2 - The spectral bidirectional reflectance distribution function (BRDF) maps incident radiation of a surface to its outgoing counterpart at different wavelengths. This function plays a fundamental role in characterizing the various types of earth surfaces. Due to its high dimensionality, the measurements, analysis, and simulation of spectral BRDF are challenging. In this letter, we introduce a new method for processing spectral reflectance using the so-called data-adjacency, i.e., the correlation between adjacent wavelengths and viewing directions. The results show that the benefits of efficient representation, noise reduction, and analysis capability can all be integrated to the data.
AB - The spectral bidirectional reflectance distribution function (BRDF) maps incident radiation of a surface to its outgoing counterpart at different wavelengths. This function plays a fundamental role in characterizing the various types of earth surfaces. Due to its high dimensionality, the measurements, analysis, and simulation of spectral BRDF are challenging. In this letter, we introduce a new method for processing spectral reflectance using the so-called data-adjacency, i.e., the correlation between adjacent wavelengths and viewing directions. The results show that the benefits of efficient representation, noise reduction, and analysis capability can all be integrated to the data.
KW - Remote sensing
KW - spectroradiometers
KW - wavelet transforms
UR - http://www.scopus.com/inward/record.url?scp=85077442260&partnerID=8YFLogxK
U2 - 10.1109/lgrs.2019.2939228
DO - 10.1109/lgrs.2019.2939228
M3 - Article
SN - 1545-598X
VL - 17
SP - 948
EP - 952
JO - IEEE Geoscience and Remote Sensing Letters
JF - IEEE Geoscience and Remote Sensing Letters
IS - 6
M1 - 8845665
ER -