Polarimetric SAR Image Classification Based on Deep Learning and Hierarchical Semantic Model
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摘要: 针对复杂场景的极化合成孔径雷达（Synthetic aperture radar，SAR）图像，堆叠自编码模型能够自动学习高层特性，有效表示城区、森林等复杂地物的结构，然而，却难以保持图像的边界和细节.为了克服该缺点，本文结合深度自编码器和极化层次语义模型（Polarimetric hierarchical semantic model，PHSM），提出了新的无监督的极化SAR图像分类算法.该方法根据极化层次语义模型，将复杂的极化SAR图像划分为聚集、匀质和结构三大区域.对聚集区域，采用堆叠自编码模型进行高层特征表示，并构造字典得到稀疏特征进行分类；对匀质区域，采用层次模型进行分类；对于结构区域，进行线目标保留和边界定位.实验结果表明，该算法通过不同的分类策略优势互补，能够得到区域一致性好且边界保持的分类结果.Abstract: Stacked auto-encoder model can effectively represent the complex terrain structures, such as the urban and the forest, by automatically learning high-level features. However, it has difficulty in preserving details and edges. In order to overcome this shortcoming, a new unsupervised polarimetric synthetic aperture radar (PolSAR) classification method is proposed by combining the deep learning and the polarimetric hierarchical semantic model (PHSM). According to the PHSM, a PolSAR image is partitioned into aggregated, homogeneous and structural regions. For aggregated regions, a stacked auto-encoder model is applied to learn high-level features, and further the sparse representation and classification is constructed by learning a dictionary with high-level features. For homogeneous regions, hierarchical segmentation and classification is applied. In addition, edges are located and line objects are preserved for structural regions. Experimental results demonstrate that the proposed method can obtain good performance in both region homogeneity and edge preservation.1) 本文责任编委 柯登峰
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图 8 San Francisco地区极化SAR图像分类结果图
Fig. 8 Classification maps of the PolSAR image on San Francisco area
图 9 Ottawa地区极化SAR图像分类结果图c
Fig. 9 Classification maps of the PolSAR image on Ottawa area
表 1 不同算法的分类结果统计(%)
Table 1 Classification accuracies for different algorithms (%)
Wishart Wishart MRF SAE 本文算法 城区 61.33 92.47 96.11 92.94 海洋 96.05 99.23 93.64 97.09 森林 93.52 58.52 95.79 98.13 平均精度 83.63 83.41 95.18 96.05 Kappa系数 78.48 76.75 84.64 94.30 
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表 2 文中算法的混淆矩阵(%)
Table 2 Confusion matrix for the proposed method (%)
城区 海洋 森林 城区 92.94 2.70 4.36 海洋 0.40 97.09 2.51 森林 0.32 1.55 98.13
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表 3 不同算法的运行时间(s)
Table 3 Running time for different algorithms (s)
Wishart Wishart MRF SAE 本文算法 时间 9.79 68.41 109.55 98.82
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