Abstract
Existing polarimetric synthetic aperture radar (PolSAR) image classification methods cannot achieve satisfactory performance on complex scenes characterized by several types of land cover with significant levels of noise or similar scattering properties across land cover types. Hence, we propose a supervised classification method aimed at constructing a classifier based on self-paced learning (SPL). SPL has been demonstrated to be effective at dealing with complex data while providing classifier. In this paper, a novel Support Vector Machine (SVM) algorithm based on SPL with neighborhood constraints (SVM_SPLNC) is proposed. The proposed method leverages the easiest samples first to obtain an initial parameter vector. Then, more complex samples are gradually incorporated to update the parameter vector iteratively. Moreover, neighborhood constraints are introduced during the training process to further improve performance. Experimental results on three real PolSAR images show that the proposed method performs well on complex scenes.
Abstract (translated)
现有的极化合成孔径雷达（Polsar）图像分类方法无法在复杂场景下获得满意的性能，这些复杂场景具有多种类型的陆地覆盖层，这些地面覆盖层具有明显的噪声水平或类似的散射特性。因此，我们提出了一种监督分类方法，旨在构建基于自学习（SPL）的分类器。SPL已经证明在提供分类器的同时能够有效地处理复杂的数据。提出了一种基于邻域约束的支持向量机算法。该方法首先利用最简单的样本获得初始参数向量。然后逐步引入更复杂的样本，迭代更新参数向量。在训练过程中引入了邻域约束，进一步提高了训练效果。实验结果表明，该方法在复杂场景下具有良好的性能。
URL
https://arxiv.org/abs/1903.07243
