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Extracting shelter forest in semi-arid sandy area based on Landsat ETM+ imagery

机译:基于Landsat ETM +影像的半干旱沙地防护林提取

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Taking a sub-area of semi-arid west Jilin Province as example, we mainly discuss the method of shelter forest extraction in sandy area from Landsat-7 ETM+ imagery in this study. After the comparison of the image fusion methods including HIS transforms, PCA transforms, Brovey transforms and Wavelet transforms, the method of Brovey transforms improved by wavelet analysis is presented for further processing. The details information in fused ETM+ image by this improved method is more considerable and fruitful. Using unsupervised classification in combination with supervised classification and threshold method based on NDVI, we extract the farmland shelterbelts from the fusion image finally. The accuracy of classification is more than 85%. From the experiment result, this method shows a better performance in the shelter forest extraction in a typical semi-arid sandy.
机译:本研究以吉林省西部半干旱地区为例,主要讨论了从Landsat-7 ETM +影像中提取沙地防护林的方法。在对包括HIS变换,PCA变换,Brovey变换和Wavelet变换的图像融合方法进行比较之后,提出了经过小波分析改进的Brovey变换方法,以进行进一步的处理。通过这种改进的方法在融合的ETM +图像中的细节信息更加可观和富有成果。将无监督分类与基于NDVI的监督分类和阈值方法相结合,最终从融合图像中提取农田防护林带。分类的准确性超过85%。从实验结果看,该方法在典型的半干旱沙质防护林提取中表现出较好的性能。

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