Hyperspectral imaging technology can obtain the spatial information and spectral information of the simulated operational background and its camouflage materials at the same time and identify and classify them according to their differences. In this paper, we collected the hyperspectral images (400–1000 nm) of the desert background, jungle background, desert camouflage netting, jungle camouflage netting, and jungle camouflage clothing through the hyperspectral imaging system, and the samples were preprocessed by denoising and black-and-white correction. Then, we analysed the region of interest (ROI) of the training samples by principal component analysis (PCA). After the pixels in the region of interest and their surrounding areas were averaged, 60% of the data was used as the training samples, and the remaining 40% was used as the test samples. According to their similarities and differences between them and referenced spectrum, the models of classification were established by combining the Naive Bayes (NB) algorithm, K-nearest neighbour (KNN) algorithm, random forest (RF) algorithm, and support vector machine (SVM) algorithm. The results show that among the four models, SVM model has the highest accuracy of classification and the recognition rate of jungle camouflage clothing is the highest. This study verifies the scientific and feasibility of hyperspectral imaging technology for camouflage identification and classification in a simulated operational environment, which has some practical significance.

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基于高光谱成像技术的模拟作战环境伪装识别研究

高光谱成像技术可以同时获得模拟操作背景及其伪装材料的空间信息和光谱信息，并根据它们的差异对其进行识别和分类。在本文中，我们通过高光谱成像系统收集了沙漠背景，丛林背景，沙漠迷彩网，丛林迷彩网和丛林迷彩服的高光谱图像（400–1000 nm），并对样本进行了降噪和黑色预处理。和白色校正。然后，我们通过主成分分析（PCA）分析了训练样本的感兴趣区域（ROI）。将感兴趣区域及其周围区域中的像素平均后，将60％的数据用作训练样本，其余40％的数据用作测试样本。根据它们与参考频谱之间的相似性和差异，结合朴素贝叶斯（NB）算法，K近邻（KNN）算法，随机森林（RF）算法和支持向量机（SVM）建立了分类模型。 ） 算法。结果表明，在四种模型中，SVM模型的分类精度最高，丛林迷彩服的识别率最高。这项研究验证了高光谱成像技术在模拟作战环境中进行伪装识别和分类的科学性和可行性，具有一定的现实意义。K最近邻（KNN）算法，随机森林（RF）算法和支持向量机（SVM）算法。结果表明，在四种模型中，SVM模型的分类精度最高，丛林迷彩服的识别率最高。这项研究验证了高光谱成像技术在模拟作战环境中进行伪装识别和分类的科学性和可行性，具有一定的现实意义。K最近邻（KNN）算法，随机森林（RF）算法和支持向量机（SVM）算法。结果表明，在四种模型中，SVM模型的分类精度最高，丛林迷彩服的识别率最高。这项研究验证了高光谱成像技术在模拟作战环境中进行伪装识别和分类的科学性和可行性，具有一定的现实意义。