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| Research on Improved Stacked Sparse Autoencoders for Mineral Hyperspectral Endmember Extraction |
| ZHU Ling, QIN Kai*, LI Ming，ZHAO Ying-jun |
| National Key Laboratory of Remote Sensing Information and Imagery Analyzing Technology, Beijing Research Institute of Uranium Geology, Beijing 100029, China |
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Abstract Rocks in nature are usually aggregates of various minerals. Due to the low spatial resolution of hyperspectral sensors, the hyperspectral data obtained are mostly the mixing spectrum of mineral components. Affected by factors such as noise interference and intimate mixing characteristics of minerals, endmember extracting and quantifying analysis of minerals is still a hotspot and difficulty topicin current research. Based on the deep learning theory, this study improves the autoencoder structure and proposes a new stacked sparse autoencoders method (SSAE), which provides a new idea for mineral hyperspectral unmixing. First of all, according to the characteristics of mineral mixing spectrum, three improvements have been made: first, the bias term of autoencoder neural network is removed; second, the batch normalization (BN) layer is added in front of the activation function of each hidden layer, and the Relu activation function is used for the final output layer; third, spectral angular distance (LSAD) is used as the objective function instead of the mean square error (ＬMSE). The proposed model obtained the parameters by optimizing the objective function through gradient descent method. Then, two simulation datasets with different mineral combinations and different mass fractions are established by using the Hapke model. The datasets include ten pure minerals, kaolinite, pyrophyllite, montmorillonite, chlorite, muscovite, calcite, hematite, dolomite, potassium feldspar and limonite. Finally, SSAE method is used to test the datasets. Test results of SSAE are compared with the results of six cases in the process of autoencoder network improvement as well as the results of VCA and SISAL. Experiments show that the accuracy of SSAE endmember extraction is greatly improved than before. The SSAE method can successfully identify all endmembers of two data sets. The mean errors of Spectral Angle Distance(SAD) are respectively 0.059 7 and 0.034 4, which is less different from the result of the VCA and is better than there sult of SISAL. SSAE method provides a new ideal for hyperspectral unmixing, and has a better promoting effect on the geological application and quantitative analysis of hyperspectral remote sensing.
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Received: 2020-03-02
Accepted: 2020-07-21
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Corresponding Authors:
QIN Kai
E-mail: h_rs_qk@163.com
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