Robust Semisupervised Classification for PolSAR Image With Noisy Labels
Robust Semisupervised Classification for PolSAR Image With Noisy Labels
复制标题
带有噪声标签的 PolSAR 图像的鲁棒半监督分类
DOI：
10.1109/tgrs.2017.2728186
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发表时间：
2017-11-01
影响因子：
8.2
通讯作者：
Jiao, Licheng
中科院分区：
文献类型：
--
作者：
Hou, Biao;Wu, Qian;Jiao, Licheng
有监督极化合成孔径雷达（PolSAR）图像分类的鲁棒性主要受两个方面的影响，即标记训练像元的数量和质量。具体而言，对于大规模的PolSAR图像，人工标记像素数量有限，限制了自动分类方法的性能，而人工标记的训练像素质量较低，会对斑点和不纯细胞产生不忠实的影响。为了解决上述两个基本问题，我们提出了一个强大的半监督概率图为基础的分类框架。首先，半监督学习计划的实施，同时利用标记和未标记的像素信息补偿。此外，由先验信息推导出的相邻像素之间的结构关系，进一步有利于减少有限的标记像素的影响。其次，在训练分类器的过程中加入了一个鲁棒的分类损失函数，以增强对带噪标记像素的鲁棒性。第三，不忠实的有限标记数据可以解决与混合生成/歧视性分类框架，其中标记和未标记的像素被同时利用来学习低质量像素的高级特征。在真实的PolSAR数据集上进行的实验验证了该框架在特定方面的有效性，与现有方法相比，该方法在视觉性能和分类精度上均具有优势。总的来说，我们的模型在数据集弗莱沃兰上的分类精度至少提高了20%，在Oberpfaffenhofen上提高了10%，在渭河上提高了5%。
The robustness of the supervised polarimetric synthetic aperture radar (PolSAR) image classification is severely affected by two main aspects, namely, the quantity and quality of the labeled training pixels. Specifically, limited manually labeled pixels with respect to the large scale of PolSAR image have limited the performance of the automatic classification methods, while manually labeled training pixels shall be unfaithful with the speckle and impure cell for their low qualities. In order to address the above two fundamental problems, we propose a robust semisupervised probability graphic-based classification framework. First, a semisupervised learning scheme is implemented to simultaneously exploit both labeled and unlabeled pixels for information compensation. Moreover, structural relationship among neighboring pixels inducing from the prior information is further benefit to reduce the influence of limited labeled pixels. Second, a robust classification loss function is added in the process of training classifier to enhance the robustness to the noisy labeled pixels. Third, unfaithful limited labeled data can be settled with a hybrid generative/discriminative classification framework, where labeled and unlabeled pixels are simultaneously exploited for learning high-level feature for the low-quality pixels. The effectiveness of the proposed framework on the specific aspect is validated in experiments on real PolSAR data sets, which reveal the superiority in both visual performance and classification accuracy compared with the state-of-the-art methods. Totally speaking, our model has improved the classification accuracy by at least 20% on data set Flevoland, 10% on Oberpfaffenhofen, and 5% on Weihe River than the compared ones.