基于多项Logit模型的土地覆被分层分类方法研究
Research and Application of Land Cover Hierarchical Classification Approach Using Multinomial Logit Models
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摘要: 探讨了一种利用多项Logit模型分层提取土地覆盖专题信息的方法。考虑客观存在的异物同谱现象,构建分层分类体系,针对不同层的地物类别选取不同的预测变量构建多项Logit模型,分步骤地提取各地物类专题信息。将此方法应用于美国蒙大拿州中部地区的土地覆盖专题信息提取,结果表明,该方法较常规的使用同一组特征变量构建单一模型一次性地划分所有地物类的方法在总体分类精度上有了明显改善。Abstract: Since there exists the objective phenomenon that different classes sharing the same spectral characteristics,a hierarchical categorical mapping approach is developed using generalized linear models.According to the similarities of spectral characteristics,different classes of high similar spectral characteristics are merged into the same one.Then a hierarchical modeling scheme is formed.Different predictor variables are chosen to build different multinomial Logit models to extract classes in different layers.Mask technique is employed to extract thematic maps step by step.Ultimately,all thematic maps are incorporated into a whole one.Experimental results show that the hierarchical modeling using generalized linear models is an effective approach to improve land cover mapping quality.
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