Based on Self-Training , Co-Training and Low Density Separation, first builds initial classifiers using labeled samples, then improve themselves using unlabeled samples recursively.

分别应用自我训练和协同训练,低密度分割,首先利用少量已标注样本进行训练学习,建立初始分类器,然后利用大量未标注样本不断新分类器,从而提高分类器的性能。

A lot of experiments have been done using the same remote sensing images based on different kind of methods including supervision and semi-supervision, self-training and co-training, and Low Density Separation in this paper.

本文用不同的方法对同一批次的遥感影像数据进行了大量实验:运用基于朴素贝叶斯的全监督学习方法,与基于自我学习与协同学习的半监督学习方法进行比较,引入低密度分割进行影像的分类实验。