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- W3149917314 abstract "在这研究，分析与一架合成的孔雷达(SAR ) 在一个煤采矿区域在一个构造地点，稻田和沉淀 seeper 土地的信息特征上被进行中等分辨率的图象。基于煤采矿区域的陆地盖子的特征，在质地特征抽取和 SAR 图象的一个灰色级的同现矩阵( GLCM )的一个选择方法上，我们在这研究建议为计算 GLCM 的最佳窗口尺寸是能有效地区分陆地盖子的不同类型的一个适当大小的窗口。下次，乐队联合在文章特征图象和过滤乐队的 SAR 图象上被执行固定一幅新多乐队图象。在有主要部件分析的新图象的转变以后，一个分类与大多数信息在三个主要部件乐队上有选择地被进行。最后， through training 并且与样品试验，一个更好三分层的 BP 神经网络被建立分类 SAR 图象。结果显示出那，由质地信息帮助了，神经网络分类在 14.6% 改进了 SAR 图象分类的精确性，与由没有质地信息的最大的可能性的评价的一个分类相比。" @default.
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- W3149917314 date "2009-01-01" @default.
- W3149917314 modified "2023-09-27" @default.
- W3149917314 title "Case study on the extraction of land cover information from the SAR image of a coal mining area" @default.
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