基于深度卷积神经网络的图上半监督极化SAR图像分类算法

To realize high quality image classification with few labeled data, a graph-based semi-supervised PolSAR image classification method using deep neural networks is proposed in this paper. The PolSAR image is modeled as a graph, based on which we design an energy function which incorporates a semi-supervision term, a convolutional neural network (CNN) term and a pairwise smoothness term. CNN is responsible for extracting discriminative polarimetric features. The semi-supervision term enforces that class information of labeled pixels keep fixed during the classification. The pairwise smoothness term enforces class label smoothness. Started from an initialized class label map generated using superpixel segmentation of polarimetric PauliRGB image, the proposed method iteratively optimizes the energy function until the model converges. Experimental results conducted on two benchmark PolSAR images show that our approach effectively improves the classification accuracy with limited numbers of labeled pixels.