论文标题
Polsar图像分类基于健壮的低级特征提取和马尔可夫随机场
PolSAR Image Classification Based on Robust Low-Rank Feature Extraction and Markov Random Field
论文作者
论文摘要
在各种遥感应用中，已经大力研究了极化合成孔径雷达（POLSAR）图像分类。但是，如今，这仍然是一项具有挑战性的任务。一个重要的障碍在于嵌入Polsar成像过程中的斑点效应，这极大地降低了图像的质量并使分类更加复杂。为此，我们提出了一种新型的Polsar图像分类方法，该方法通过低级别（LR）特征提取来消除斑点噪声，并通过Markov Random Field（MRF）强制实施平滑性先验。具体而言，我们利用基于高斯的鲁棒LR基质分解的混合物同时提取区分特征并消除复杂的噪声。然后，通过应用卷积神经网络以及对提取的特征的数据增强来获得分类图，在该特征中隐含了局部一致性，并且标签问题不足。最后，我们完善MRF的分类图来实施上下文平滑度。我们在两个基准POLSAR数据集上进行实验。实验结果表明，所提出的方法实现了有希望的分类性能和可取的空间一致性。
Polarimetric synthetic aperture radar (PolSAR) image classification has been investigated vigorously in various remote sensing applications. However, it is still a challenging task nowadays. One significant barrier lies in the speckle effect embedded in the PolSAR imaging process, which greatly degrades the quality of the images and further complicates the classification. To this end, we present a novel PolSAR image classification method, which removes speckle noise via low-rank (LR) feature extraction and enforces smoothness priors via Markov random field (MRF). Specifically, we employ the mixture of Gaussian-based robust LR matrix factorization to simultaneously extract discriminative features and remove complex noises. Then, a classification map is obtained by applying convolutional neural network with data augmentation on the extracted features, where local consistency is implicitly involved, and the insufficient label issue is alleviated. Finally, we refine the classification map by MRF to enforce contextual smoothness. We conduct experiments on two benchmark PolSAR datasets. Experimental results indicate that the proposed method achieves promising classification performance and preferable spatial consistency.
