Polarimetric synthetic aperture radar (PolSAR) image segmentation is currently of great importance in image processing for remote sensing applications. However, it is a challenging task due to two main reasons. Firstly, the label information is difficult to acquire due to high annotation costs. Secondly, the speckle effect embedded in the PolSAR imaging process remarkably degrades the segmentation performance. To address these two issues, we present a contextual PolSAR image semantic segmentation method in this paper. With a newly defined channel-wise consistent feature set as input, the three-dimensional discrete wavelet transform (3D-DWT) technique is employed to extract discriminative multi-scale features that are robust to speckle noise. Then Markov random field (MRF) is further applied to enforce label smoothness spatially during segmentation. By simultaneously utilizing 3D-DWT features and MRF priors for the first time, contextual information is fully integrated during the segmentation to ensure accurate and smooth segmentation. To demonstrate the effectiveness of the proposed method, we conduct extensive experiments on three real benchmark PolSAR image data sets. Experimental results indicate that the proposed method achieves promising segmentation accuracy and preferable spatial consistency using a minimal number of labeled pixels.

中文翻译：

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具有3D离散小波变换和Markov随机场的极化SAR图像语义分割

极化合成孔径雷达（PolSAR）图像分割目前在遥感应用的图像处理中非常重要。但是，由于两个主要原因，这是一项艰巨的任务。首先，由于注释成本高，难以获取标签信息。其次，PolSAR成像过程中嵌入的斑点效应显着降低了分割性能。为了解决这两个问题，本文提出了一种上下文PolSAR图像语义分割方法。以新定义的通道方向一致特征集作为输入，三维离散小波变换（3D-DWT）技术用于提取对斑点噪声具有鲁棒性的判别性多尺度特征。然后，在分割过程中进一步应用马尔可夫随机场（MRF）在空间上增强标签平滑度。通过首次同时利用3D-DWT功能和MRF先验，分割过程中将上下文信息完全整合在一起，以确保准确，平滑的分割。为了证明该方法的有效性，我们对三个真实的基准PolSAR图像数据集进行了广泛的实验。实验结果表明，所提出的方法使用最少数量的标记像素实现了有希望的分割精度和较好的空间一致性。