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The acquisition of land cover information using three indexes and TM image

机译:利用三个指标和TM图像获取土地覆盖信息

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To get the typical land cover information of urban area, present a method to get four typical land covers in study area by displaying three binary index images in RGB coordinate system without any algorithms. One scene Landsat TM image was used to calculate Normalized Difference Vegetation Index (NDVI), Negative Normalized Difference Vegetation Index (NNDVI) the paper present, and extract water information based on the spectral relationship of typical land objects. After compared the digital number of the typical land cover information of water, vegetation, impervious surface and soil in the three calculated layer, the four typical land cover information showed obvious differences. With carefully selecting appropriate threshold value for each index image, we obtained three binary images for water, vegetation and impervious surfaces. Then they were stacked to one image and assigned red to impervious surfaces, blue to water, and green to vegetation in a false color composite, and because the soil's digital number was zero in the three binary images, it was shown black color automatically. Two hundred sample points were randomly selected for an accuracy assessment using high resolution ZY-3(China) image obtained at almost the same time as reference. The overall accuracy of the classification is 86% with the Kappa coefficient of 0.802. The result indicates that the method presented in this paper is feasible.
机译:为了获得市区的典型土地覆盖信息,提出了一种通过在RGB坐标系中显示三个二进制索引图像而无需任何算法来获取研究区域中四个典型土地覆盖的方法。使用一幅Landsat TM图像来计算本文存在的归一化植被指数(NDVI),负归一化植被指数(NNDVI),并基于典型陆地物体的光谱关系提取水信息。比较了三个计算层中水,植被,不透水地表和土壤的典型地表覆盖信息的数字量后,四种典型地表覆盖信息表现出明显的差异。通过为每个索引图像仔细选择合适的阈值,我们获得了三个关于水,植被和不透水表面的二值图像。然后将它们堆叠为一张图像,并以假彩色合成将红色分配给不透水的表面,将蓝色分配给水,将绿色分配给植被,并且由于在三个二进制图像中土壤的数字值为零,因此会自动显示为黑色。使用几乎同时获得的高分辨率ZY-3(中国)图像作为参考,随机选择了200个采样点进行准确性评估。分类的总体准确性为86%,卡伯系数为0.802。结果表明本文提出的方法是可行的。

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