基于深度特征的双极化SAR遥感图像岩性自动分类

基于像元基元、极化合成孔径雷达（Synthetic Aperture Radar，SAR）数据和传统机器学习算法的岩性分类方法，易受SAR图像固有斑点噪声影响，精度不高.为了降低噪声的影响，本研究以大尺度像元邻域为基元，用于表征地表地质体的遥感图像特征和岩性语义信息；采用高分三号双极化SAR数据进行极化分解构建3通道假彩色合成影像；然后采用深度卷积神经网络（Deep Convolutional Neural Network，DCNN）迁移学习的方法，提取有效的深度特征表示，分别实现5 m和15 m两种空间分辨率下岩性遥感自动分类.结果表明:基于不同分辨率数据和不同DCNN算法，岩性遥感自动分类的总精度均大于80%，最高精度达到91%.基于大尺度像元邻域和DCNN迁移学习方法，能够实现基于SAR数据的高精度岩性分类. 

Automatic Lithology Classification Based on Deep Features Using Dual Polarization SAR Images

The lithology classification method based on pixel primitives, polarimetric synthetic aperture radar (SAR) data and traditional machine learning algorithm is easy to be affected by the inherent speckle noise, and the accuracy is not high. In order to reduce the effect of image noise, the neighborhood of large-scale pixels is considered as the primitive to characterize the spatial aggregation characteristics of surface geological units and the corresponding lithologic semantic information. Using GaoFen-3 dual polarization data, the polarization decomposition is carried out first, and a 3-channel color composite image is constructed as the input data of the subsequent model. Then, the deep convolutional neural network (DCNN) based migration learning method is used to extract the effective deep feature representation, so as to realize the automatic lithology classification under 5 m and 15 m spatial resolution conditions. The experiment results show that based on different resolution data and different DCNN algorithms, the total accuracy of automatic lithology classification is greater than 80%, and the highest accuracy is 91%. Generally, based on large-scale pixel neighborhood and DCNN migration learning method, high-precision lithology classification based on SAR data can be realized. The lithology remote sensing dataset based on dual polarization SAR created in this paper can also be used as the benchmark of lithology classification based on artificial intelligence.