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- W2389741660 abstract "Sandy desertification is a severe environmental problem in arid areas,which threatens certainly the stability and sustainable development of oases,so it is very important to timely wxtract the information and spatial distribution of sandy desertification by applying remote sensing means.In this paper,the PCA data fusion methods and BP network classification for deriving the information of desertified lands in Qira Oasis located in the central southern marginal zone of the Taklimakan are researched.Because the Landsat-7 ETM+ Panchromatic band and the multispectral bands can be simultaneously obtained from the same sensor system under the same imaging conditions,it is available to get best data fusion results without registration.Firstly,Principal Component Analysis(PCA) is used to fuse the panchromatic and multispectral images of Landsat-7 ETM+ Satellite.And then,the BP Neural Network classification method is applied to classify the Landsat-7 ETM+ multispectral bands and the fusion images with the same training samples.The results reveal that PCA fusion image has more information and higher spatial resolution while maintaining the basic spectral characteristics of the original image,and also has a higher classification accuracy of the results than the Landsat-7 ETM+ multispectral image.It is useful to detect and monitor sandy desertification change in arid areas." @default.
- W2389741660 created "2016-06-24" @default.
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- W2389741660 date "2008-01-01" @default.
- W2389741660 modified "2023-09-23" @default.
- W2389741660 title "Study on Extracting the Information about Desertified Lands Based on Principal Component Fusion and BP Neural Network" @default.
- W2389741660 hasPublicationYear "2008" @default.
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