分离阈值算法SEaTH是一种有效的自动选取分类特征并计算阈值的方法,但其只考虑了类间距离,容易存在信息的冗余,从而对分类精度造成一定影响。本文在SEaTH算法的基础上,综合考虑了特征间的相关性、类间距离以及类内距离,对SEaTH算法进行了优化,并将改进前后的两种方法运用到广东省肇庆市TM影像及环境一号卫星影像土地覆盖分类中进行对比分析。实验结果表明,改进后的方法筛选出的特征在提取地物上更为有效,尤其使耕地的分类精度提高了12.26%,使分类总体精度由80%提高到85.26%。耕地与林地分类精度的提高,对不易获取质量较好的多时相数据地区的土地覆盖分类具有重要意义。

The SEaTH method (SEparability and THresholds) can select features and compute thresholds automatically based on Jeffries-Matusita distance to separate classes, which may cause information redundancy and affect classification results. In this paper, a new method based on SEaTH is proposed, and then the method that considers the Jeffries-Matusita distance, the inner class distance, and the correlation of different features is used in the classification of multi-resolution remote sensing images of Zhaoqing, China. The result indicates that our method can select more effective information than the original SEaTH algorithm, with accuracy in extracting farmland improved by 12.26%, and total accuracy improved from 80% to 85.26%. Such an improvement is significant to the classification when multi-temporal data are difficult to be obtained.