利用多尺度特征的极化SAR深度学习分类

Polarimetric SAR Deep Learning Classification Using Multi-scale Features

针对极化合成孔径雷达(POLSAR)图像提取到的特征信息量低和噪声干扰等问题,提出了一种对不同特征信息进行多尺度融合的方法,并利用融合后的特征,通过深度学习网络算法进行目标地物分类。首先利用小波变换技术对Freeman、Yamaguchi等极化分解得到的特征分量在不同尺度上进行融合,再利用主成分分析(PCA)降维算法处理融合后的数据,最后输入到DeepLabV3网络结构中训练。利用该方法对白湖农场地区高分三号全极化SAR数据进行验证,对比融合前后的分类结果,提出的算法在分类精度上有明显的提升,证明了该方法的有效性。

Aiming at the problem of low feature information and noise interference extracted from polarimetric synthetic aperture radar(POLSAR)images,in this paper a multi-scale fusion method for different feature information is proposed,and the fused features are used to classify target by deep learning network algorithm.Firstly,feature components obtained from polarization decomposition such as Freeman and Yamaguchi are fused on different scales by wavelet transform technology,and then principal component analysis(PCA)dimension reduction algorithm is used to process the fused data,and finally DeepLabV3 network structure is input for training.Moreover,the full-polarization SAR data of Gaofen-3 in baihu farm area is used for verification,and the classification results before and after fusion are compared.The classification accuracy of the algorithm proposed in this paper has been improved significantly,which proved the effectiveness of the method.