CAJ | 학술논문

高光谱数据的空间分辨率普遍偏低,混合像元分布广泛,故模糊分类方法常用于此类型数据的信息提取。针对模糊分类的精度常受限于特征维数和模糊样本选取等问题,提出了基于随机森林( random forest,RF)算法的高维模糊分类方法。首先将RF算法用于特征选择和模糊样本获取,然后在低维特征空间中利用模糊样本进行模糊分类,通过2步分类、遵循假设前提一致原则,实现RF和模糊分类2种分类器的融合；并通过不同样本、不同实验区和分区优化前后的3个实验(包括20余次对比实验、60多次子实验),验证了该方法不仅提高了模糊分类的精度,具有分类的有效性和可推广性,而且具有可优化性和对原始样本质量的鲁棒性。
고광보수거적공간분변솔보편편저,혼합상원분포엄범,고모호분류방법상용우차류형수거적신식제취。침대모호분류적정도상수한우특정유수화모호양본선취등문제,제출료기우수궤삼림( random forest,RF)산법적고유모호분류방법。수선장RF산법용우특정선택화모호양본획취,연후재저유특정공간중이용모호양본진행모호분류,통과2보분류、준순가설전제일치원칙,실현RF화모호분류2충분류기적융합；병통과불동양본、불동실험구화분구우화전후적3개실험(포괄20여차대비실험、60다차자실험),험증료해방법불부제고료모호분류적정도,구유분류적유효성화가추엄성,이차구유가우화성화대원시양본질량적로봉성。
The spatial resolution of hyperspectral data is generally very low, the mixed pixels are extensively distributed, and hence fuzzy classification is commonly used in the mixed pixel analysis. As the accuracy of fuzzy classification is often limited by the feature dimensions and fuzzy samples selection, the random forest ( RF ) algorithm is put forward in this paper to select features and obtain fuzzy samples; in the low-dimensional feature space, fuzzy samples are used to make fuzzy classification. Fuzzy classification and RF are merged by using two-step classification,following the principle of unanimity assumption. Using different samples,different experimental areas and different partition optimization situations,the authors conducted three comparative experiments, and the results show that the method proposed in this paper solves the limitation of fuzzy classification and improves its accuracy. It is also proved that the classification accuracy of the method is robust for the original sample.