一种面向对象分类的特征分析方法

A Feature Analysis Approach for Object-Oriented Classification

特征分析是面向对象分类成功的先决条件,通常采用试错方式进行。由于时间和成果要求等条件限制,通常不可能测试所有的特征,致使所选取特征常常并不是最佳特征,从而导致分类效果不理想。该文采用SaT特征分析方法,从大量特征集中准确提取特有特征,而且能获取相关联的阈值;通过其计算分离度值,可以实时地比较特征分类质量。实例验证表明,该方法能大大提高面向对象分类方法的精度和速度。

A good feature analysis is prerequisite for successful object-oriented classification in image analysis. In most cases,feature analysis is performed empirically with the method of trial and error in which certain features and thresholds are tested. Limited by time, it is impossible to examine all features. Therefore, the selected thresholds are not statistically optimized, and no conclusion can be drawn as to the quality of the features. A new method known as SaT has been developed in this paper. With SaT,it is possible to extract not only characteristic features but also the associated thresholds for any number of object classes from a large number Of object features available. The separability, the result of SaT analysis,serves as a comparative measure for classification quality at the same time. Conducted experiments have verified that this approach improves the precision and speed of object-oriented classification.