In this paper we investigate neural networks for classification in hyperspectral imaging with a focus on connecting the architecture of the network with the physics of the sensing and materials present. Spectroscopy is the process of measuring light reflected or emitted by a material as a function wavelength. Molecular bonds present in the material have vibrational frequencies which affect the amount of light measured at each wavelength. Thus the measured spectrum contains information about the particular chemical constituents and types of bonds. For example, chlorophyll reflects more light in the near-IR rage (800-900nm) than in the red (625-675nm) range, and this difference can be measured using a normalized vegetation difference index (NDVI), which is commonly used to detect vegetation presence, health, and type in imagery collected at these wavelengths. In this paper we show that the weights in a Neural Network trained on different vegetation classes learn to measure this difference in reflectance. We then show that a Neural Network trained on a more complex set of ten different polymer materials will learn spectral 'features' evident in the weights for the network, and these features can be used to reliably distinguish between the different types of polymers. Examination of the weights provides a human-interpretable understanding of the network.
翻译:在本文中,我们调查超光谱成像分类的神经网络,重点是将网络结构与现有感学和材料的物理物理进行连接,以研究超光成像的神经网络,重点是将网络结构与现有感学和材料的物理物理进行分类。光谱是测量光的光度过程,光是作为功能波长的一个材料反射或释放的光,材料中的分子键结在材料中具有振动频率,影响每个波长测量的光量。因此,测量的频谱包含关于特定化学成分和债券类型的信息。例如,叶绿素在接近IR的愤怒(800-900纳米)中反映的光亮度比红(625-675纳米)范围(625-675纳米)中的光(800-900纳米)反映的亮度更多,而这种差异可以通过一种正常的植被差异指数(NDVI)来测量,该指数通常用于测量植被的存在、健康和在这些波长所收集的图像中的类型。在本文件中,我们表明,受过培训的神经网络中的重量可以测量这种差异。我们然后表明,一个接受更复杂的10种不同聚合材料的神经网络的训练的神经网络,将学习网络的光的明显可见的光,这些特征可以用来对不同种类进行可靠的研究。