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PolSAR Feature Extraction Via Tensor Embedding Framework for Land Cover Classification
IEEE Transactions on Geoscience and Remote Sensing ( IF 8.2 ) Pub Date : 2020-04-01 , DOI: 10.1109/tgrs.2019.2948042
Bo Ren , Biao Hou , Jocelyn Chanussot , Licheng Jiao

Polarimetric synthetic aperture radar (PolSAR) as a typical multi-channel sensor can obtain refined geometrical and geophysical information. In the PolSAR land cover classification task, feature extraction is regarded as a critical step for the final classification. It can employ multi-modal features from the original scattering data, polarimetric target decomposition, and other transformation space. Then, how to efficiently combine multi-modal polarimetric information and extract discriminant features is an important challenge for PolSAR image processing. Graph embedding methods have become a significant technique to deal with feature extraction and dimensionality reduction (DR) problems in recent years. It provides a unified linearization framework in machine learning and other pattern recognition tasks. In this article, an extended tensor embedding framework is introduced to extract the intrinsic features for PolSAR land cover classification. First, each pixel is represented by a feature cube that is constructed by groups of polarimetric scattering signals and target decomposition features in a fixed size patch. Second, an intrinsic matrix is constructed to describe the original geometrical and statistical properties of the samples, and a penalty matrix is designed to represent some constraints. Third, the vector-based algorithms are transformed into tensor space in an unified framework and based on the pair of matrices to obtain the projection matrices in each mode by an iterative optimization process. The effectiveness of the proposed methods is demonstrated on three RADARSAT2 data sets covering the regions of Xi’an, San Francisco, and Flevoland, respectively. The visualization and quantification results show that the proposed method has superiority in land cover classification.

中文翻译:

通过用于土地覆盖分类的张量嵌入框架的 PolSAR 特征提取

极化合成孔径雷达(PolSAR)作为典型的多通道传感器,可以获得精细的几何和地球物理信息。在 PolSAR 土地覆盖分类任务中,特征提取被视为最终分类的关键步骤。它可以利用来自原始散射数据、极化目标分解和其他变换空间的多模态特征。那么,如何有效地结合多模态极化信息并提取判别特征是PolSAR图像处理的一个重要挑战。近年来,图嵌入方法已成为处理特征提取和降维(DR)问题的重要技术。它在机器学习和其他模式识别任务中提供了统一的线性化框架。在本文中,引入了扩展的张量嵌入框架来提取 PolSAR 土地覆盖分类的内在特征。首先,每个像素由一个特征立方体表示,该立方体由一组固定大小的补丁中的偏振散射信号和目标分解特征构成。其次,构造一个内在矩阵来描述样本的原始几何和统计特性,并设计一个惩罚矩阵来表示一些约束。第三,将基于向量的算法转化为统一框架中的张量空间,并基于矩阵对通过迭代优化过程获得每种模式下的投影矩阵。在分别覆盖西安、旧金山和弗莱福兰地区的三个 RADARSAT2 数据集上证明了所提出方法的有效性。
更新日期:2020-04-01
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