POL-SAR Image Classification Based on Wishart DBN and Local Spatial Information
POL-SAR Image Classification Based on Wishart DBN and Local Spatial Information
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DOI：
10.1109/tgrs.2016.2514504
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发表时间：
2016-06-01
影响因子：
8.2
通讯作者：
Yang, Shuyuan
中科院分区：
文献类型：
--
作者：
Liu, Fang;Jiao, Licheng;Yang, Shuyuan
受流行的深度神经网络的启发，即，提出了一种新的极化合成孔径雷达（POL-SAR）图像分类方法--深度信念网络（DBN）。针对POL-SAR数据的特殊性，定义了一种新的受限玻尔兹曼机（RBM），命名为Wishart-Bernoulli RBM（WBRBM），并将其用于构建WishartDBN（W-DBN）深度网络。W-DBN在POL-SAR像元建模中充分利用了大量未标记的POL-SAR像元。此外，相干矩阵直接用于表示POL-SAR像元，无需人工特征提取，简单省时。利用局部空间信息和混淆矩阵对基于W-DBN的方法得到的初步分类结果进行清洗。该方法充分利用了POL-SAR数据的先验知识和局部空间信息，克服了传统方法对提取特征敏感、运算速度慢的缺点。在三个POL-SAR数据集上的实验表明，该方法比传统方法具有更好的效果和更快的速度。
Inspired by a popular deep neural network, i.e., deep belief network (DBN), a novel method for polarimetric synthetic aperture radar (POL-SAR) image classification is proposed in this paper. For the particularity of POL-SAR data, a new type of restricted Boltzmann machine (RBM) is specially defined, which we name the Wishart-Bernoulli RBM (WBRBM), and is used to form a deep network named as Wishart DBN (W-DBN). Numerous unlabeled POL-SAR pixels are made full use of in the modeling of POL-SAR pixels by W-DBN. In addition, the coherency matrix is used directly to represent a POL-SAR pixel without any manual feature extraction, which is simple and time saving. Local spatial information, together with the confusion matrix, is used in this paper to clean the preliminary classification result obtained by the method based on W-DBN. Making full use of the prior knowledge of POL-SAR data and local spatial information, the proposed method overcomes shortcomings of traditional methods, in which they are sensitive to extracted features and slow to execute. The experiments, tested on three POL-SAR data sets, show that the proposed method produces better results and is much faster than traditional methods.