基于Landsat 8卫星图像的土地和作物分类:SD蒂米什瓦拉案例研究。

Mihai Hrebei, F. Sala
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引用次数: 9

摘要

农业越来越多地受益于包括卫星技术和信息学在内的有关领域的几种服务,这些服务最近作出了重大贡献。利用卫星影像的光谱信息,对蒂米什瓦拉平原的生境、作物进行分类分析。基于Landsat8卫星图像,利用ArcGIS 10软件对生境和农作物进行分析。所测试的算法中,采用极大似然法和监督分类法对卫星图像中包含的光谱信息进行分析。5月份记录的光谱波段与卫星指数NDVI之间的高且稳定的相关性决定了应使用某月的卫星图像进行领土分类、作物结构评价及其占用面积。NDVI指数与Band 543呈相互依赖关系,具有较高的统计准确性(p<< 0.001, r2 = 0.820)。基于极大似然算法的监督法分析,实现了2013年4类和2014年6类的土地和作物分类,并以较高的精度确定了作物面积。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Classification of land and crops based on satellite images Landsat 8: case study SD Timisoara.
Agriculture benefits increasingly from the several services of related areas, including satellite technology and informatics, which have a substantial contribution lately. The present study aimed to analyze and classify the habitat, and crops of the SD Timisoara based on spectral information from satellite images. Analysis of the habitat and agricultural crops was done with ArcGIS 10 software based on satellite images Landsat8. Of the tested algorithms, Maximum Likelihood was used and as method, the supervised classification was used in order to analyze the spectral information contained by satellite images. The high and stable level of the correlation between spectral bands and satellite index NDVI recorded in May, has determined that satellite images from the certain month should be used for the classification the territory, evaluation of crop structure and areas occupied by them. There was an interdependent relationship between NDVI index and Band 543 having high statistical accuracy (p<< 0.001, R 2 = 0.820).  Analysis done by supervised method based on Maximum likelihood algorithm has facilitated land and crop classification in four classes in the year 2013 and six classes in the year 2014 and the determination of crop areas with high level of accuracy.
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