Finding appropriate class-separating metric and labeling rules is crucial in the construction of image classifiers. In this paper a Divergence-Chebyshev Neighbors Pursuit (DCNP) algorithm is proposed for rapid Polarimetric Synthetic Aperture Radar (PolSAR) image classification. First, an information-theoretic divergence is defined to measure the similarity of polarimetric features between pixels. Then a divergence-Chebyshev distance is defined to reveal the affinity of pixels in both the polarization and spatial domains. Moreover, inspired by human’s learning characteristic that the knowledge is learned little by little, the DCNP algorithm is designed to progressively determine the labels of unknown pixels. Some experiments are conducted on several real PolSAR image datasets and the results show that our method can achieve accurate classification with a small number of labeled data, and outperforms its counterparts in terms of several guidelines.

找到合适的分类度量和标记规则在图像分类器的构建中至关重要。在本文中，提出了一种用于快速极化合成孔径雷达 (PolSAR) 图像分类的发散-切比雪夫邻域追踪 (DCNP) 算法。首先，定义了一个信息论散度来衡量像素之间极化特征的相似性。然后定义发散-切比雪夫距离以揭示像素在偏振域和空间域中的亲和力。此外，受人类学习知识的学习特点的启发，DCNP算法被设计为逐步确定未知像素的标签。