基于深度卷积神经网络和条件随机场模型的PolSAR图像地物分类方法

近年来，极化合成孔径雷达(PolSAR)图像地物分类得到了深入研究。传统的PolSAR图像地物分类方法采用的特征往往需要针对具体问题进行设计，特征表征性不强。因此，该文提出一种基于卷积神经网络(CNN)和条件随机场(CRF)模型的PolSAR图像地物分类方法。利用预训练好的实现图像分类任务的卷积神经网络模型(VGG-Net-16)提取表征能力更强的图像特征，再通过CRF模型对多特征及上下文信息的有效利用来实现图像的地物分类。实验结果表明，与3种利用传统经典特征的方法相比，该方法能够提取更有效的特征，取得了更高的总体分类精度和Kappa系数。 

Terrain Classification of Polarimetric Synthetic Aperture Radar Images Based on Deep Learning and Conditional Random Field Model

In recent years, Polarimetric Synthetic Aperture Radar (PolSAR) image classification has been investigated extensively. The traditional PolSAR image terrain classification methods result in a weak feature representation. To overcome this limitation, this study aims to propose a terrain classification method based on deep Convolutional Neural Network (CNN) and Conditional Random Field (CRF). The pre-trained VGG-Net-16 model was used to extract more powerful image features, and then the terrain from the images was classified through the efficient use of multiple features and context information by conditional random fields. The experimental results show that the proposed method can extract more features effectively in comparison with the three methods using traditional classical features and it can also achieve a higher overall accuracy and Kappa coefficient.