| 基于多光谱影像和DEM的泥石流堆积扇识别研究——以白龙江流域武都段为例 |
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| 引用本文： | 杨小兵,王登贵.基于多光谱影像和DEM的泥石流堆积扇识别研究——以白龙江流域武都段为例[J].测绘与空间地理信息,2016(4):118-121. |
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| 作者姓名： | 杨小兵 王登贵 |
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| 作者单位： | 61243部队,新疆乌鲁木齐,830006 |
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| 摘 要： | 选取中国泥石流多发区白龙江流域武都段作为研究区,在对该区域泥石流堆积扇的形态特征和堆积范围进行实地调绘的基础上,利用高分辨率影像(SPOT)进行目视解译,获得研究区部分泥石流堆积扇和非泥石流堆积区的分布范围,将其作为已知样本区。利用该区域多光谱遥感影像(ASTER)和数字高程模型(DEM),提取包含波段比和主分量的几十种特征指标。通过运用方差分析和聚类分析等方法对各指标进行分析计算,选取对区别泥石流堆积扇最具显著意义的指标进行输入,进而采用基于像元的分类方法识别泥石流堆积扇。得到如下结论:SPOT与ASTER融合影像的波段比、主分量指标可以有效地突出土壤岩石中的矿物成分,对泥石流堆积扇的识别具有显著意义;利用筛选出的遥感指标和地形指标作为输入,进行监督分类识别泥石流堆积扇,能够有效地将遥感指标和地形指标相结合,提取的堆积扇覆盖范围与实际情况较为接近。
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| 关 键 词： | 泥石流堆积扇 高光谱遥感影像 数字高程模型 分类器 |
Debris Fol w Fan Rce ogni tion Study Based on Multi-spectar l Image and DE M:Wu du Segme nto f Bailong River Bais n as a Case Study |
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| YANG Xiao-bing;WANG Deng-gui.Debris Fol w Fan Rce ogni tion Study Based on Multi-spectar l Image and DE M:Wu du Segme nto f Bailong River Bais n as a Case Study[J].Geomatics & Spatial Information Technology,2016(4):118-121. |
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| Authors: | YANG Xiao-bing;WANG Deng-gui |
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| Institution: | YANG Xiao-bing;WANG Deng-gui;61243 Troops; |
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| Abstract: | The Bailong River Basin in China is selected as studying area where debris flow occurs frequently .Combing with field sur-vey and draw, through visual interpretation on SPOT images , the distribution range partial debris flow fans and non -debris flow are obtained as the sample area .The vegetation index , soil, topography , terrain features and many other indexes are extracted by using multi-spectral remote sensing images (ASTER) and DEM.Under the variance analysis and cluster analysis , each feature is evalua-ted.According to physical meaning of different remote sensing indicators , the most relevant features are selected as input , and then based on the object -oriented and pixel -based methods;the debris flow fan is identified respectively by means of the Support Vector Machine and Neural Net .The conclusions are shown as the following:The band ratio of the fused image with SPOT images and AS-TER can effectively highlight the mineral composition of the rock soil , which can be further used for the classification of debris flow fans;especially the indicators of iron oxide and vegetation identify it significantly; With the vegetation, soil, topography and terrain indexes selected as input , the debris flow fan is identified by using supervision classification .The remote sensing indexes and terrain indexes can be effectively combined and the result is close to the actual situation . |
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| Keywords: | debris flow fan hyper-spectral remote sense images DEM classifier |
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