基于深度学习的超连续激光主动高光谱成像分类方法

摘要：高光谱成像(HSI)技术能够提供精细的光谱和空间对象信息。它具有区分材料的能力，因此已被广泛使用区域范围。 然而，传统的恒指指数强烈依赖于阳光，因此受到限制白天。本文提出了一种可见光/近红外有源HSI分类方法研制了可 见光/近红外超连续光谱激光器，用于光谱检测和物体成像黑暗中。此外，创建了基于深度学习的分类器混合DenseNet来 学习特征从活跃的恒生指数数据中并行地表示光谱和空间信息，并用于活跃恒生指数分类。通过将该方法成功地应用于 黑暗中选定的物体，我们证明，使用主动HSI分类方法，可以检测到的对象对实际应用的兴趣。正确的活动HSI分类不 同事件对象进一步支持该方法在伪装检测、生物医学变异检测、洞穴绘画等方面的可行性映射等等。

关键词：高光谱成像、超连续激光、分类、深度学习

期刊：Appl. Sci. 2020, 10, 3088;

DOI：10.3390/app10093088

设备：GaiaField

Deep-Learning-Based Active Hyperspectral Imaging Classification Method Illuminated by the Supercontinuum Laser

Abstract：Hyperspectral imaging (HSI) technology is able to provide fine spectral and spatial information of objects. It has the ability to discriminate materials and thereby has been used in a wide range of areas. traditional HSI strongly depends on the sunlight and hence is restricted to daytime. In this paper, a visible/near-infrared active HSI classification method illuminated by a visible/near-infrared supercontinuum laser is developed for spectra detection and objects imaging in the dark. Besides, a deep- learning-based classifier, hybrid DenseNet, is created to learn the feature representations of spectral and spatial information parallelly from active HSI data and is used for the active HSI classification. applying the method to a selection of objects in the dark successfully, we demonstrate that with the active HSI classification method, it is possible to detect objects of interest in practical applications. Correct active HSI classification of different objects further supports the viability of the method for camouflage detection, biomedical alteration detection, cave painting mapping.

Keywords：hyperspectral imaging; supercontinuum laser; classification; deep-learning