Deep-Learning-Based Active Hyperspectral Imaging Classification Method Illuminated by the Supercontinuum Laser
Source abstract
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. However, 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. By 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 and so on.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Remote-Sensing Image Classification · Remote Sensing in Agriculture · Advanced Image Fusion Techniques
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
College of Advanced Interdisciplinary Studies, National University of Defense Technology, Changsha 410073, China
State Key Laboratory of High-Performance Computing, College of Computer, National University of Defense Technology, Changsha 410073, China
Interdisciplinary Center of Quantum Information, National University of Defense Technology, Changsha 410073, China
Funding
National Natural Science Foundation of China · Scientific Researches Foundation of National University of Defense Technology · National Natural Science Foundation of Hunan Province · The Youth talent lifting project
References (31)
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