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针对VIS-NIR高光谱、MIR强度和MIR偏振成像的多维伪装
作者:小柯机器人 发布时间:2026/1/13 10:24:58


近日,浙江大学李强团队研究了针对VIS-NIR高光谱、MIR强度和MIR偏振成像的多维伪装。2026年1月12日出版的《光:科学与应用》杂志发表了这项成果。

伪装在现代安全和军事行动中至关重要,它在规避探测和提高装备生存能力方面发挥着关键作用。然而,大多数现有的伪装装置仅在单一维度上运作,无法应对新兴的多维探测技术,包括可见光至近红外(VIS-NIR)高光谱成像和中红外(MIR)偏振成像。

研究组提出了一种多维伪装策略,通过分层结构实现同时进行VIS-NIR光谱伪装、MIR强度伪装和偏振伪装。该多维伪装装置的发射率为0.7,在MIR范围内的大观察角度下具有较低的线性偏振度(<1.5%),在VIS-NIR范围内具有较高的光谱相似度(>96.9%)。此外,该装置在植被背景下能够欺骗高光谱分类,并在MIR强度和偏振成像下融入其环境。该研究为多维伪装技术引入了一种新范式,并为电磁波操控开辟了新途径。

附:英文原文

Title: Multi-dimensional camouflage against VIS-NIR hyperspectral, MIR intensity, and MIR polarization imaging

Author: Qin, Rui, Zhu, Huanzheng, Zhu, Rongxuan, Ghosh, Pintu, Qiu, Min, Li, Qiang

Issue&Volume: 2026-01-12

Abstract: Camouflage is essential in modern security and military operations, playing a critical role in evading detection and enhancing the survivability of equipment. However, most existing camouflage devices operate in a single dimension, rendering them inadequate against emerging multi-dimensional detection techniques, including visible to near-infrared (VIS-NIR) hyperspectral imaging and mid-infrared (MIR) polarization imaging. In this work, we propose a multi-dimensional camouflage strategy that realizes simultaneous VIS-NIR spectrum camouflage, MIR intensity, and polarization camouflage by a hierarchical structure. The multi-dimensional camouflage device exhibits an emissivity of 0.7, a low degree of linear polarization (< 1.5%) at large observation angles in MIR range, and high spectral similarity (>96.9%) in the VIS-NIR range. Moreover, it deceives hyperspectral classification in vegetative background and blends into its environment under MIR intensity and polarization imaging. This work introduces a novel paradigm for multi-dimensional camouflage techniques and opens up new avenues for electromagnetic waves manipulation.

DOI: 10.1038/s41377-025-02145-w

Source: https://www.nature.com/articles/s41377-025-02145-w

期刊信息

Light: Science & Applications《光:科学与应用》,创刊于2012年。隶属于施普林格·自然出版集团,最新IF:19.4

官方网址:https://www.nature.com/lsa/
投稿链接:https://mts-lsa.nature.com/cgi-bin/main.plex