Evaluation of colour space effect on estimation accuracy of hyperspectral image by dimension extension based on RGB image

Abstract

Recently, the utilization of hyperspectral images containing several hundred wavelength information has been increasing in various fields. If a hyperspectral image can be estimated from a low-cost RGB image that has only R, G, and B wavelength information without using a hyperspectral camera, it would be useful in various fields. Herein, we propose a hyperspectral image estimation method based on RGB images, wherein RGB components and YUV colour space information calculated from the RGB are applied to a neural network for tuning, and the hyperspectral image is estimated by inputting the output from the tuning neural network to a decoding function of the trained autoencoder. To evaluate the estimation accuracy of hyperspectral images based on differences in the combination of RGB and colour space models, we conducted validity experiments for the estimation of hyperspectral images in three scenarios with different colour spaces: RGB and YUV, RGB and HSV, only RGB. The results showed that the scenario with RGB and YUV colour space exhibited the highest estimation accuracy of 0.913 by averaging all similarities for wavelength among the three scenarios; thus, the validity of the proposed method as an estimation method for hyperspectral images was verified.

Keywords

• hyperspectral image
• neural network
• autoencoder
• yuv colour space
• hsv colour space