Medina José M, Díaz José A, Vukusic Pete
Opt Express. 2015 Apr 20;23(8):10198-212. doi: 10.1364/OE.23.010198.
Iridescent structural colors in biology exhibit sophisticated spatially-varying reflectance properties that depend on both the illumination and viewing angles. The classification of such spectral and spatial information in iridescent structurally colored surfaces is important to elucidate the functional role of irregularity and to improve understanding of color pattern formation at different length scales. In this study, we propose a non-invasive method for the spectral classification of spatial reflectance patterns at the micron scale based on the multispectral imaging technique and the principal component analysis similarity factor (PCASF). We demonstrate the effectiveness of this approach and its component methods by detailing its use in the study of the angle-dependent reflectance properties of Pavo cristatus (the common peacock) feathers, a species of peafowl very well known to exhibit bright and saturated iridescent colors. We show that multispectral reflectance imaging and PCASF approaches can be used as effective tools for spectral recognition of iridescent patterns in the visible spectrum and provide meaningful information for spectral classification of the irregularity of the microstructure in iridescent plumage.
生物学中的虹彩结构色呈现出复杂的空间变化反射特性，这取决于照明角度和观察角度。对虹彩结构色表面的此类光谱和空间信息进行分类，对于阐明不规则性的功能作用以及增进对不同长度尺度上颜色图案形成的理解至关重要。在本研究中，我们基于多光谱成像技术和主成分分析相似因子（PCASF），提出了一种用于微米尺度空间反射图案光谱分类的非侵入性方法。我们通过详细说明该方法在研究孔雀（Pavo cristatus）羽毛的角度依赖性反射特性中的应用，展示了此方法及其组成方法的有效性，孔雀是一种以展现明亮且饱和的虹彩颜色而闻名的雉科鸟类。我们表明，多光谱反射成像和PCASF方法可作为可见光光谱中虹彩图案光谱识别的有效工具，并为虹彩羽毛微观结构不规则性的光谱分类提供有意义的信息。
