Hierarchical semantic model and scattering mechanism based PolSAR image classification
Hierarchical semantic model and scattering mechanism based PolSAR image classification
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基于分层语义模型和散射机制的PolSAR图像分类
DOI：
10.1016/j.patcog.2016.02.020
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发表时间：
2016-11
影响因子：
8
通讯作者：
Yuan Jialing
中科院分区：
文献类型：
--
作者：
Liu Fang;Shi Junfei;Jiao Licheng;Liu Hongying;Yang Shuyuan;Wu Jie;Hao Hongxia;Yuan Jialing
混合地形类型，如城市区域，是由聚集在一起的相似地物的后向散射产生的。在无监督极化合成孔径雷达(PolSAR)图像分类中，混合地形类型具有明显的明暗变化，因此将其归类为语义均匀区域是一个挑战。本文提出了一种PolSAR图像的分层语义模型(HSM)，该模型通过构建原始语义和中层语义来克服上述不足。原始级别的语义是由草图片段组成的极化示意图，是PolSAR图像的稀疏表示。中间层语义是一种区域地图，它可以从草图中提取混合地形类型作为语义同质区域。通过区域映射，将复杂的PolSAR场景划分为混合的、线性的和均匀的子空间。然后，根据三个子空间的特点，采用不同的分类方法，并引入极化信息来改善分类结果。在不同波段和不同传感器的PolSAR数据集上的实验结果表明，该方法在区域均匀度方面优于现有的方法，特别是在混合地形类型下。
The hybrid terrain type, such as the urban area, is generated by the backscattering of the similar ground objects aggregated together. It is a challenge to classify the hybrid terrain types as semantic homogeneous regions for unsupervised polarimetric SAR (PolSAR) image classification, since there are sharp bright-dark variations in intensity. In this paper, a hierarchical semantic model (HSM) is proposed for PolSAR images, which overcomes the above shortcoming by constructing the primal-level and middle-level semantics. The primal-level semantic is a polarimetric sketch map consisting of sketch segments, which is the sparse representation of a PolSAR image. The middle-level semantic is a region map which can extract hybrid terrain types as semantic homogeneous regions from the sketch map. Mapped by the region map, a complex PolSAR scene is partitioned into hybrid, linear and homogeneous subspaces. Then, according to the characteristics of the three subspaces, different methods are adopted, and furthermore polarimetric information is involved to improve the classification result. Experimental results on PolSAR data sets with different bands and sensors demonstrate that the proposed method is superior to the state-of-the-art methods in region homogeneity especially for hybrid terrain types.
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DOI：
10.1017/9781108946339
发表时间：
2021-08
期刊：
--
影响因子：
--
作者：
Andrew Blake;C. Crawford;P. Fletcher;Sophie Hackford;A. Hurlbert;D. Nilsson;C. Rovelli
通讯作者：
Andrew Blake;C. Crawford;P. Fletcher;Sophie Hackford;A. Hurlbert;D. Nilsson;C. Rovelli
影响因子：
1.7
作者：
Q. Summerfield
通讯作者：
Q. Summerfield
DOI：
10.1109/igarss.2009.5418271
发表时间：
2009-07
期刊：
2009 IEEE International Geoscience and Remote Sensing Symposium
影响因子：
--
作者：
L. Bombrun;Jean-Marie Beaulieu;Gabriel Vasile;J. Ovarlez;F. Pascal;M. Gay
通讯作者：
L. Bombrun;Jean-Marie Beaulieu;Gabriel Vasile;J. Ovarlez;F. Pascal;M. Gay
DOI：
10.1109/tcsvt.2014.2313897
发表时间：
2015-02
影响因子：
8.4
作者：
Liang Lin;Ruimao Zhang;Xiaohua Duan
通讯作者：
Liang Lin;Ruimao Zhang;Xiaohua Duan
影响因子：
4.8
作者：
Wu, Yonghui;Ji, Kefeng;Su, Yi
通讯作者：
Su, Yi