Transformation of a high-dimensional color space for material classification

Images in red-green-blue (RGB) color space need to be transformed to other color spaces for image processing or analysis. For example, the well-known hue-saturation-intensity (HSI) color space, which separates hue from saturation and intensity and is similar to the color perception of humans, can aid many computer vision applications. For high-dimensional images, such as multispectral or hyperspectral images, transformation images to a color space that can separate hue from saturation and intensity would be useful; however, the related works are limited. Some methods could interpret a set of high-dimensional images to hue, saturation, and intensity, but these methods need to reduce the dimension of original images to three images and then map them to the trichromatic color space of RGB. Generally, dimension reduction could cause loss or distortion of original data, and, therefore, the transformed color spaces could not be suitable for material classification in critical conditions. This paper describes a method that can transform high-dimensional images to a color space called hyper-hue-saturation-intensity (HHSI), which is analogous to HSI in high dimensions. The transformation does not need dimension reduction, and, therefore, it can preserve the original information. Experimental results indicate that the hyper-hue is independent of saturation and intensity and it is more suitable for material classification of proximal or remote sensing images captured in a natural environment where illumination usually cannot be controlled.