新疆师范大学学报（自然科学版）2026，Vol.45Issue(2)：58-70,13.
基于GF-3雷达数据极化分解与深度学习的干旱区绿洲土地覆被及盐渍化分级研究
Land Cover Classification and Salinization Level Assessment of Oases in Arid Regions based on Polarimetric Decomposition of GF-3 SAR Data and Deep Learning
摘要
Abstract
Land use/land cover(LUCC)change is a key scientific issue for understanding human-land interactions,and its accurate monitoring provides essential support for regional sustainable development decision-making.This study takes the Keriya Oasis as the study area and constructs a multi-source remote sensing collaborative classification framework by integrating GaoFen-3(GF-3)fully polarimetric synthetic aperture radar(SAR)data,Landsat 8 OLI multispectral imagery,and in situ measurements of soil physicochemical properties.Soil salinization levels(slight,moderate,and severe)are employed as the core quantitative indicator of land cover quality and,together with land use types(cropland,vegetation,water bodies,and bare land),form the classification scheme.By applying eight polarimetric decomposition methods,a random forest-based feature selection algorithm,and a U-Net deep learning model,this study systematically explores optimal interpretation strategies for land use/land cover classification in arid oasis environments.The experimental results demonstrate that,compared with traditional image classification algorithms,the U-Net deep learning framework exhibits a significant advantage in classification accuracy,achieving an overall accuracy of 78.21%and a Kappa coefficient of 0.72.The model effectively integrates radar backscattering features,optical spectral information,and soil physicochemical parameters such as soil organic matter content.By constructing a multidimensional feature space,it successfully addresses the problem of spectral heterogeneity within vegetation-salinization mixed pixels.The proposed multi-source data fusion-based classification approach provides a novel technical pathway for oasis ecosystem monitoring.Moreover,the spatial heterogeneity captured by the classification results offers a robust scientific basis for land degradation control and resource management decision-making in oasis regions.关键词
积神经网络/GF-3/极化分解/克里雅绿洲/土地利用/覆被Key words
Convolutional neural network/GF-3/Polarization decomposition/Keriya Oasis/Land use/cover分类
天文与地球科学引用本文复制引用
刘翔宇,张飞,依力亚斯江·努尔麦麦提..基于GF-3雷达数据极化分解与深度学习的干旱区绿洲土地覆被及盐渍化分级研究[J].新疆师范大学学报（自然科学版）,2026,45(2):58-70,13.基金项目
国家自然科学基金项目(42561057). （42561057）
