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条件随机场框架下基于随机森林的城市土地利用/覆盖遥感分类

DOI: 10.6046/gtzyyg.2014.04.09, PP. 51-55

Keywords: 条件随机场(CRFs),空间上下文,随机森林,高分辨率遥感图像,土地利用/覆盖分类

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Abstract:

针对“基于像素的条件随机场(conditionalrandomfields,CRFs)模型能否在m级分辨率的多光谱遥感图像分类中表现良好”的问题,提出了集成图像的光谱、方向梯度直方图和多尺度多方向Texton纹理等多种线索的CRFs模型定义方法。利用上述特征,选择随机森林(randomforests,RF)定义CRFs关联势函数;利用特征对比度加权的Potts函数定义CRFs交互势函数,并且建立了多标签的RF-CRFs模型;对该模型进行分项参数训练以及基于图割的α-膨胀算法推理;利用典型城区的QuickBird多光谱图像进行模型的验证与精度评价。结果表明RF-CRFs模型的分类精度可达82.52%以上,比RF分类器的分类精度提高了3.35%。

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