School of Resources and Environmental Engineering/Anhui Province Key Laboratory of Wetland Ecosystem Protection and Restoration, Anhui University, Hefei 230601, China.
Department of Resource and Environmental Sciences, Wuhan University, Wuhan 430072, China.
Sensors (Basel). 2021 Apr 25;21(9):3006. doi: 10.3390/s21093006.
Polarimetric synthetic aperture radar (PolSAR) image classification has played an important role in PolSAR data application. Deep learning has achieved great success in PolSAR image classification over the past years. However, when the labeled training dataset is insufficient, the classification results are usually unsatisfactory. Furthermore, the deep learning approach is based on hierarchical features, which is an approach that cannot take full advantage of the scattering characteristics in PolSAR data. Hence, it is worthwhile to make full use of scattering characteristics to obtain a high classification accuracy based on limited labeled samples. In this paper, we propose a novel semi-supervised classification method for PolSAR images, which combines the deep learning technique with the traditional scattering trait-based classifiers. Firstly, based on only a small number of training samples, the classification results of the Wishart classifier, support vector machine (SVM) classifier, and a complex-valued convolutional neural network (CV-CNN) are used to conduct majority voting, thus generating a strong dataset and a weak dataset. The strong training set are then used as pseudo-labels to reclassify the weak dataset by CV-CNN. The final classification results are obtained by combining the strong training set and the reclassification results. Experiments on two real PolSAR images on agricultural and forest areas indicate that, in most cases, significant improvements can be achieved with the proposed method, compared to the base classifiers, and the improvement is approximately 3-5%. When the number of labeled samples was small, the superiority of the proposed method is even more apparent. The improvement for built-up areas or infrastructure objects is not as significant as forests.
极化合成孔径雷达 (PolSAR) 图像分类在 PolSAR 数据应用中发挥了重要作用。近年来，深度学习在 PolSAR 图像分类中取得了巨大成功。然而，当标记训练数据集不足时，分类结果通常不尽如人意。此外，深度学习方法基于层次特征，这是一种不能充分利用 PolSAR 数据散射特性的方法。因此，充分利用散射特性，在有限的标记样本基础上获得高精度的分类结果是值得的。本文提出了一种新的基于散射特性的半监督 PolSAR 图像分类方法，该方法将深度学习技术与传统的散射特征分类器相结合。首先，仅使用少量训练样本，基于 Wishart 分类器、支持向量机 (SVM) 分类器和复值卷积神经网络 (CV-CNN) 的分类结果进行多数投票，从而生成强数据集和弱数据集。然后，使用强训练集通过 CV-CNN 对弱数据集进行重新分类。最后，通过结合强训练集和重新分类结果得到最终的分类结果。在农业和森林地区的两幅真实 PolSAR 图像上的实验表明，与基础分类器相比，所提出的方法在大多数情况下都可以显著提高分类精度，提高幅度约为 3-5%。当标记样本数量较少时，所提出的方法的优势更加明显。对于建成区或基础设施物体的改进不明显。
