基于Tsallis熵冗余度的超谱特征选择算法性能评价

Evaluating the performance of hyperspectral feature selection algorithm using Tsallis entropy redundancy

分析了超谱数据处理过程中概率统计和相关分析典型的特征选择准则,指出了直接将其用做评价指数的弊病,进而提出了一类基于Tsallis熵冗余度的评价指数。该指数正比于多个变量间的相关信息含量,可以很方便地构造出适合超谱数据特征选择算法的评价方法。在AVIRIS数据的评价实验中,当两组相同数目波段集合的总体分类精度相差不小于2%时,基于2次Tsallis熵冗余度的评价方法正确率可达75%;当总体分类精度相差不小于8%时,正确率可达90%。

Feature selection is a widely used technique in hyperspectral data processing,but there is little work. concerning the evaluation of the performances with respect to different feature selection methods especially when the ground truth map is absent. This paper analyzes the selection criterion from probabilistic statistics and correlation analysis, and points out the disadvantage of directly using them for evaluation application, and then proposes the evaluation index based on the Tsallis entropy redundancy. This index has direct proportion in the relevant information among multi variables, and can be easily extended for the classification performance evaluation of hyperspectral feature selection. The AVIRIS data has been applied to the proposed method and the results show that when the overall classification difference is no less than 20% ,the correct rate of evaluation is greater than 75% ,furthermore,when the difference is no less than 8 %, the correct rate is greater than 90 %.