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OALib Journal期刊
ISSN: 2333-9721
费用：99美元
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基于sda和cart算法的面向对象分类研究
DOI: 10.3969/j.issn.1000-2006.2015.03.002, PP. 6-12
Keywords: 面向对象分类,逐步判别分析,cart模型,敏感度评价
Abstract:
以2005年深圳市福田区quickbird影像为主要数据源,根据面向对象多尺度分割结果,构建和分析了不同地类对象的光谱、形状和纹理信息特征,在此基础上利用逐步判别分析法(stepwisediscriminantanalysis,sda)结合分类回归树(classificationandregressiontrees,cart)构建多尺度、多变量分类模型。结果表明:①采用sda在一定程度上能够客观、准确地进行特征子集预筛选,笔者从32个特征中筛选出27个特征用于构建cart模型,并按其区分地类能力进行了重要性排序,其中光谱特征与纹理特征排序比较靠前,形状特征中仅lengthwidth排在第5位,剩余特征比较靠后。②利用分类回归树模型可进一步优化特征选取,并智能化计算出分离阈值,基本实现面向对象的自动化分类。其中mean_b3、lengthwidth、ratio_b3、gldve和ndvi是重要分类节点。③逐步判别分析结合分类回归树构建的分类模型,可以在提高或者不显著降低影像分类精度的条件下实现特征降维。当取相关指数r2>0.2时,构建的分类模型效果最优,cart16模型训练和验证精度分别为94.44%和83.37%,其特征子集规模最小,与原始特征数量相比减少了一半。
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