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Xinjiang University (2019)

Extraction of Soil Salinization Information in Yutian Oasis Based on Polarized Decomposition Information of PALSAR Data

再屯古丽·亚库普;

Titre : Extraction of Soil Salinization Information in Yutian Oasis Based on Polarized Decomposition Information of PALSAR Data

Auteur : 再屯古丽·亚库普;

Grade : Master’s Theses 2019

Université : Xinjiang University

Résumé
In this paper the Yutian Oasis in southern section of the Taklimakan Desert which has long-term research foundation,was chosen as the study area,using GIS and remote sensing techniques analysis the soil physical and chemical properties.Polarimetric information obtained from quad-polarized PALSAR-2(Phased Array type L-band Synthetic Aperture Radar)data in a variety of target polarization decomposition treatment,and SVM classification trained with 11 less noise polarized parameter’s as the best classification features selecting through visual interpretation.Wishart maximum likelihood classification,support vector machine(SVM)classification and polarimetric decomposition methods are combined,and then different levels of soil salinization information extracted.Classification results analyzed and validated by Landsat-8 OLI image of the study area combined with field investigation.The four-polarization back-scatter coefficient values corresponding to the sampling points were extracted based on the previous results by the spatial analysis module of ArcGIS.Analysis the correlation between backscatters of sampling points and soil salt content,water content,pH value.On the basis of,the multiple linear regression(MLR),geographically weighted regression(GWR)and back propagation artificial neural network(BP ANN)adopted to establish the quantitative inversion models of soil salt content.Polarization entropy(H)and scattering angle(alpha)extracted from quad-polarized PALSAR-2 data using H/A/αtarget polarization decomposition treatment.The spatial distribution of the ground features obtained by wishart-H/A/αClassification method,and then H-αfeature space was established and determine the target features and the saline soil types.The main findings are as follows:1)Polarimetric information obtained from quad-polarized PALSAR-2 data in a variety of target polarization decomposition treatment.SVM classification trained with 12 less noise polarized parameter’s which included T11、anisotropylueneburg、entropyshannon、Span、Freeman2Vol、entropyshannonInorm、TSVMalphas、Yamaguchi3Odd、H/A/al1、VZ3Odd、Krogagerkd、CloudeT11 as the best classification features selecting through visual interpretation.Wishart maximum likelihood classification,support vector machine(SVM)classification and polarimetric decomposition methods are combined,and then different levels of soil salinization information extracted.Classification results analyzed and validated by Landsat-8 OLI image of the study area combined with field investigation.The results showed that,by comparing the confusion matrix,the classification accuracy of SVM methods higher than wishart classification.

Mots clés : soil salinity; PALSAR data; back-scattering coefficient; polarimetric decomposition; feature space;

Présentation (CNKI)

Page publiée le 4 juin 2020