| 基于深度置信网络的CRISM影像火星表面矿物识别方法 |
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| 引用本文： | 张绪冰,王贤敏,王凯,岳桥兵,张良.基于深度置信网络的CRISM影像火星表面矿物识别方法[J].地质科技通报,2020,39(4):189-200. |
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| 作者姓名： | 张绪冰 王贤敏 王凯 岳桥兵 张良 |
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| 基金项目： | 武汉市科技计划应用基础前沿项目2019010701011403 |
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| 摘 要： | 鉴于传统的光谱特征参数方法存在不能综合考虑光谱在整个波长范围内的谱形、对于单一吸收带相似的不同矿物难以区分等问题，研究采用深度置信网络方法对火星专用小型侦察影像频谱仪（CRISM）高光谱影像中的火星表面矿物进行自动识别，该算法具体包括:①预训练阶段。利用非监督算法逐层训练受限玻尔兹曼机，自动学习模型参数，提取光谱特征。②调优阶段。将自动学习的光谱特征输入分类器，采用反向传播算法对模型进行监督微调，识别矿物在CRISM影像中的分布。在算法的研究中，采用光谱比值方法降低火星表面灰尘等噪声对矿物光谱的影响，并探讨样本数量、隐含层节点数、网络深度等对算法识别精度的影响，试图构建适宜于CRISM影像火星表面矿物识别的深度置信网络模型。以火星表面镁铁蒙脱石和氯盐为例进行测试，实验结果表明:该方法能够对火星表面矿物进行自动识别，准确率达到85%以上，与光谱参数法的识别结果基本叠合，并能够探测光谱参数法未能识别的部分矿物分布。
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| 关 键 词： | 识别 深度置信网络 火星矿物 CRISM影像 |
| 收稿时间： | 2019-04-01 |
Recognition of the Martian minerals based on the deep belief networks method: Application in the CRISM images |
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| Abstract: | In order to decrease recognition inaccuracies of the different minerals with similar single absorption peak by means of the spectral characteristic parameter methods which are difficult to estimate the spectrum of the whole wavelength range, this paper applied the deep belief networks (DBN) method to detect the Martian minerals from the hyperspectral images of the compact reconnaissance imaging spectrometer for Mars (CRISM). According to the method, firstly, the unsupervised layer-by-layer greedy algorithm is adopted to train each restricted Boltzmann machine (RBM) for the sake of learning parameters and extracting the spectral features of the minerals with a single bottom-up pass. Then, it takes advantage of the back propagation (BP) algorithm to tune the parameters learned in the train step and automatically identify the Martian minerals with coupling a suitable classifier. In this paper, the ratios of the minerals spectral and the dust spectral are utilized to identify the mineral samples for sake of decreasing the dust effect. Finally the influences of the sample size, the number of the hidden layer nodes, and the network depth are investigated to established the optimal deep belief networks for the recognition of the Martian minerals. As illustrated by the case of the Mg/Fe smectites and the chlorides from the CRISM images, the experimental results indicate that the recognition accuracy of the DBN method is more than 85%. In conclusion, the DBN method has a better performance in detecting some pixels of the minerals that the spectral parameter algorithm cannot detect in the CRISM images, and the deep learning method could be utilized in the recognition of the Martian minerals automatically. |
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