Polarimetric SAR Image Semantic Segmentation With 3D Discrete Wavelet Transform and Markov Random Field
Polarimetric SAR Image Semantic Segmentation With 3D Discrete Wavelet Transform and Markov Random Field
复制标题
利用 3D 离散小波变换和马尔可夫随机场进行偏振 SAR 图像语义分割
DOI：
10.1109/tip.2020.2992177
复制
发表时间：
2020-01-01
影响因子：
10.6
通讯作者：
Xu, Zongben
中科院分区：
文献类型：
--
作者：
Bi, Haixia;Xu, Lin;Xu, Zongben
偏振合成孔径雷达（PolSAR）图像分割是当前遥感图像处理中的一个重要问题。然而，由于两个主要原因，这是一项具有挑战性的任务。首先，由于标注成本高，标签信息难以获取。其次，PolSAR成像过程中嵌入的散斑效应显著降低了分割性能。为了解决这两个问题，本文提出了一种基于上下文的PolSAR图像语义分割方法。以新定义的信道一致特征集为输入，采用三维离散小波变换（3D-DWT）技术提取对散斑噪声具有鲁棒性的判别多尺度特征。然后进一步应用马尔可夫随机场（MRF）来增强分割过程中标签的空间平滑性。首次同时利用3D-DWT特征和MRF先验，在分割过程中充分融合上下文信息，确保分割准确、流畅。为了证明该方法的有效性，我们在三个真实的基准PolSAR图像数据集上进行了大量的实验。实验结果表明，该方法在使用最少标记像素的情况下，获得了较好的分割精度和空间一致性。
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.