一种基于模糊关联分类的遥感图像分类方法

遥感图像分类是遥感领域的研究热点之一.提出了一种基于自适应区间划分的模糊关联遥感图像分类方法(fuzzy associative remote sensing classification,FARSC).算法根据遥感图像分类的特点,利用模糊C均值聚类算法自适应地建立连续型属性模糊区间,使用新的剪枝策略对项集进行筛选从而避免生成无用规则,采用一种新的规则重要性度量方法对多模糊分类规则进行融合,从而有效地提高分类效率和精确度.在UCI数据和遥感图像上所作实验结果表明,算法具有较高的分类精度以及对样本数量变化的不敏感性,对于解决遥感图像分类问题,FARSC算法具有较高的实用性,是一种有效的遥感图像分类方法.

Remote Sensing Image Classification Based on Fuzzy Associative Classification

The classification of remote sensing images is one of the most important issues in the remote sensing field. Due to inherent variability and uncertainty of the data, training data is hard to obtain in most real-world applications, which impact the classification accuracy of traditional classifiers greatly. In this paper, a novel fuzzy associative classifier based on fuzzy association rules, namely fuzzy associative remote sensing classification (FARSC), is developed for the classification of remote sensing images. The proposed algorithm employs fuzzy C-means to partition quantitative attributes according to their intrinsic characteristics, adopts new jointing and pruning techniques without generating useless candidate itemsets, and introduces a weighted parameter to score the fuzzy association rules, which fuses multiple rules to avoid the bias towards some classes. To evaluate the performance of the proposed algorithm, an experiment on the remote sensing image of Zhalong Nature Reserve is performed, compared with two other image classification algorithms: support vector machine and extreme leaning machine. The experimental results show that the proposed algorithm not only has higher classification accuracy, but also is insensitive to the variation of amount of the training data. Hence FARSC can effectively overcome the problem of the lack of training data set in the actual remote sensing classification and get a high classification accuracy.