Formula for spectral reflectance function of Munsell color

Studying of the optical properties of agricultural vegetation is one of the methods for plants condition estimation, prediction of their development and changes influenced by natural and anthropogenic factors.The work is dedicated to the investigation of spectral reflectance function of agricultural Brassica napus taking into account the degree of soil moisture. When most of the agricultural lands in Belarus are covered with vegetation in summer, employing the optical properties of agricultural vegetation for deciphering the soil depends on the degree of soil moisture. Insufficient numbers of days in year when the soil is not covered by vegetation or is in a plowed state requires in-situ optical measurements, because there are more than 50 % cloudy conditions in the year, especially in spring and autumn time.The study has been carried out near the Minsk 11.06.2020 (53.837004&#186; N, 27.487597&#186; E) in clear, cloudless day. The relief for investigated field is hilly-ridge, characterized by a predominance of elevation marks from 250 to 300 m and it is actively sown field. During the spectrometric measurements, the field has been sown with Brassica napus in the phenological phase of pod formation.When studying the spectral reflectance of Brassica napus, in-situ spectrometric measurements and analysis of a multispectral image have been carried out. Spectrometric measurements have been carried out by FSR-02 spectrometer (spectral range 400-900 nm, spectral resolution 4.3 nm) aiming to retrieve spectral reflectance function.The normalized vegetation index NDVI has been used for analyzing the multispectral image from Landsat 8 OLI system with a spatial resolution of 30 m. The results of a study of the correlation between the reflection coefficient of Brassica napus&#160;and the area of observed soils will be presented. In addition, the results of the analysis of quasi-synchronous values of the NDVI index and in-situ measurements of the spectral reflectance of Brassica napus will be discussed.

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An algorithm of spectral reflectance function reconstruction without sample training can integrate prior information

2017 2nd International Conference on Image, Vision and Computing (ICIVC) ◽

10.1109/icivc.2017.7984614 ◽

2017 ◽

Author(s):

Jian Gu ◽

Huimin Chen

Keyword(s):

Spectral Reflectance ◽

Prior Information ◽

Reflectance Function ◽

Function Reconstruction

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Spectral-reflectance function recovery for improved colour-constancy experiments

Displays ◽

10.1016/s0141-9382(02)00040-9 ◽

2002 ◽

Vol 23 (5) ◽

pp. 213-222 ◽

Cited By ~ 5

Author(s):

Juan L Nieves ◽

F Pérez-Ocón ◽

J Hernández-Andrés ◽

J Romero

Keyword(s):

Spectral Reflectance ◽

Colour Constancy ◽

Reflectance Function ◽

Function Recovery

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SAFARI 2000 SURFACE SPECTRAL REFLECTANCE AT SUA PAN, BOTSWANA, DRY SEASON 2000

ORNL Distributed Active Archive Center Datasets ◽

10.3334/ornldaac/780 ◽

2005 ◽

Author(s):

W. BUERMANN ◽

F. ECKARDT ◽

M. HELMLINGER

Keyword(s):

Spectral Reflectance ◽

Dry Season

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Skin Chromophore Estimation from Mobile Selfie Images using Constrained Independent Component Analysis

Electronic Imaging ◽

10.2352/issn.2470-1173.2020.14.coimg-357 ◽

2020 ◽

Vol 2020 (14) ◽

pp. 357-1-357-6

Author(s):

Luisa F. Polanía ◽

Raja Bala ◽

Ankur Purwar ◽

Paul Matts ◽

Martin Maltz

Keyword(s):

Independent Component Analysis ◽

Spectral Reflectance ◽

Source Separation ◽

Principal Component ◽

Component Analysis ◽

Previous Method ◽

Independent Component ◽

Light Transport ◽

Camera Model ◽

Constrained Independent Component Analysis

Human skin is made up of two primary chromophores: melanin, the pigment in the epidermis giving skin its color; and hemoglobin, the pigment in the red blood cells of the vascular network within the dermis. The relative concentrations of these chromophores provide a vital indicator for skin health and appearance. We present a technique to automatically estimate chromophore maps from RGB images of human faces captured with mobile devices such as smartphones. The ultimate goal is to provide a diagnostic aid for individuals to monitor and improve the quality of their facial skin. A previous method approaches the problem as one of blind source separation, and applies Independent Component Analysis (ICA) in camera RGB space to estimate the chromophores. We extend this technique in two important ways. First we observe that models for light transport in skin call for source separation to be performed in log spectral reflectance coordinates rather than in RGB. Thus we transform camera RGB to a spectral reflectance space prior to applying ICA. This process involves the use of a linear camera model and Principal Component Analysis to represent skin spectral reflectance as a lowdimensional manifold. The camera model requires knowledge of the incident illuminant, which we obtain via a novel technique that uses the human lip as a calibration object. Second, we address an inherent limitation with ICA that the ordering of the separated signals is random and ambiguous. We incorporate a domain-specific prior model for human chromophore spectra as a constraint in solving ICA. Results on a dataset of mobile camera images show high quality and unambiguous recovery of chromophores.

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A Flying Gray Ball Multi-illuminant Image Dataset for Color Research

Journal of Imaging Science and Technology ◽

10.2352/j.imagingsci.technol.2020.64.5.050411 ◽

2020 ◽

Vol 64 (5) ◽

pp. 50411-1-50411-8

Author(s):

Hoda Aghaei ◽

Brian Funt

Keyword(s):

Machine Vision ◽

Spectral Reflectance ◽

Color Constancy ◽

Ground Truth ◽

Practical Approach ◽

Post Processing ◽

Illumination Estimation ◽

Processing Step ◽

Image Dataset

Abstract For research in the field of illumination estimation and color constancy, there is a need for ground-truth measurement of the illumination color at many locations within multi-illuminant scenes. A practical approach to obtaining such ground-truth illumination data is presented here. The proposed method involves using a drone to carry a gray ball of known percent surface spectral reflectance throughout a scene while photographing it frequently during the flight using a calibrated camera. The captured images are then post-processed. In the post-processing step, machine vision techniques are used to detect the gray ball within each frame. The camera RGB of light reflected from the gray ball provides a measure of the illumination color at that location. In total, the dataset contains 30 scenes with 100 illumination measurements on average per scene. The dataset is available for download free of charge.

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