Exploring the Sample Quality Using Rough Sets Theory for the Supervised Classification of Remotely Sensed Imagery

在遥远地察觉到的形象的监督分类进程，样品的数量是象过去常评估图象分类的钥匙一样影响图象分类的精确性的重要因素之一。一般来说，样品根据优先的知识，经验和更高的分辨率图象被获得。与样品和一样的采样模型的一样的尺寸，训练样本数据的几个集合能被获得。在这，集合，集合反映它完善光谱特征并且保证仅仅在分类的精确性被估计了以后，分类的精确性能被知道。在分类前，为指导并且优化作为结果的分类过程测量并且估计样品的质量将因此是有意义的研究。基于不平的集合，然后，为样品质量的一个新测量索引被建议。实验数据在 1999 年 8 月 8 日是中国黄河三角洲的陆地卫星 TM 形象。实验比较 Bhattacharrya 距离矩阵和纯净索引 Δ 和 Δ X 在样品质量上基于 5 样品数据并且也的不平的集合理论分析它的效果。

In the supervised classification process of remotely sensed imagery, the quantity of samples is one of the important factors affecting the accuracy of the image classification as well as the keys used to evaluate the image classification. In general, the samples are acquired on the basis of prior knowledge, experience and higher resolution images. With the same size of samples and the same sampling model, several sets of training sample data can be obtained. In such sets, which set reflects perfect spectral characteristics and ensure the accuracy of the classification can be known only after the accuracy of the classification has been assessed. So, before classification, it would be a meaningful research to measure and assess the quality of samples for guiding and optimizing the consequent classification process. Then, based on the rough set, a new measuring index for the sample quality is proposed. The experiment data is the Landsat TM imagery of the Chinese Yellow River Delta on August 8th, 1999. The experiment compares the Bhattacharrya distance matrices and purity index zl and △x based on rough set theory of 5 sample data and also analyzes its effect on sample quality.