| 一种综合利用图像和光谱信息的物体真假模式识别方法 |
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| 引用本文： | 徐江河,张飞舟,张立福,邓楚博,孙雪剑.一种综合利用图像和光谱信息的物体真假模式识别方法[J].武汉大学学报(信息科学版),2019,44(8):1174-1181. |
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| 作者姓名： | 徐江河 张飞舟 张立福 邓楚博 孙雪剑 |
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| 作者单位： | 1.北京大学地球与空间科学学院, 北京, 100871 |
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| 基金项目： | 国家自然科学基金重点项目41830108 |
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| 摘 要： | 传统模式识别方法在物体、人脸、指纹、军事目标识别等领域中只利用单一的图像信息。当研究对象的图像特征高度相似时，识别率较低，如对于真假目标的识别，仅仅利用物体的图像信息很难得到满意的识别结果。针对上述问题，提出了一种综合利用图像和光谱信息的物体真假模式识别方法。该方法采用卷积神经网络模型，通过迁移学习的方式构建图像识别模型，并依据物体图像的语义特征进行物体类别识别，在此基础上，基于逆传播（back propagation，BP）神经网络模型，结合物体的实测光谱数据进行物体真假识别。为了验证该方法的准确性和有效性，利用真假苹果和葡萄作为测试对象，单独利用图像信息和光谱信息进行识别时，识别率分别为38.50%和63.00%，而利用该综合方法得到的识别率为95.00%。可认为该方法提高了真假目标混杂情况下的识别准确度，可为物体识别、人脸识别、指纹识别、军事目标识别等领域的应用提供重要的参考，也为航天侦查载荷设计提供了新的思路。
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| 关 键 词： | 模式识别 深度学习 卷积神经网络 图像信息 光谱信息 |
| 收稿时间： | 2019-05-06 |
A Method of True and Fake Objects Pattern Recognition Integrating Image Information and Spectral Information |
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| Institution: | 1.School of Earth and Space Sciences, Peking University, Beijing 100871, China2.State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijng 100871, China |
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| Abstract: | At present, pattern recognition technology has been widely used in the fields of objects, faces, fingerprints, military target recognition, etc. However, pattern recognition method still has obvious shortcomings when applied to the above fields. It is currently restricted to the use of image information for identification. When the image features of the research object are highly similar, the accuracy of pattern recognition is low and cannot meet the actual application requirements. For example, in the case of mixed true and false targets, it is difficult to obtain satisfactory recognition results using only image information. Aiming at the above problems, a pattern recognition method integrating image information and spectral information is proposed in this paper. Firstly, the image recognition model based on the convolutional neural network model is built to identify object categories based on the semantic features of objects and obtain preliminary recognition results. Then, on the basis of the preliminary recognition results, the measured spectral data of the object (spectrum range 400-1 000 nm, spectral resolution 2 nm) is used to perform true and fake identification of the object based on the back propagation (BP) neural network model. The principle of true and false recognition is that the true and false targets are different in material, causing significant difference in their hyperspectral information. Finally, recognition results are obtained. In order to verify the accuracy of the proposed method, true and fake apples and grapes are used as experimental subjects and the result is that:The recognition accuracy obtained by using only image information is 38.50%, and the recognition accuracy obtained by using only spectral information is 63.00%, however, the recognition accuracy obtained by the method proposed in this paper is 95.00%. Compared with the existing pattern recognition method without spectral information participation, the pattern recognition method using image information and spectral information proposed in this paper improves the pattern recognition accuracy under the mixed condition of true and false targets, and can be widely applied to object recognition, face recognition, fingerprint recognition, military target recognition and other fields. |
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