厦门理工学院计算机与信息工程学院;
积雪作为冰冻圈重要组成部分，与全球气候变化和生态系统密切相关，精准识别积雪分布信息具有重要意义。合成孔径雷达(Synthetic Aperture Radar, SAR)数据的极化和散射特征在积雪识别中具有极大的应用潜力。以新疆玛纳斯河流域为研究区，提取全极化Radarsat-2数据后向散射特征和目标极化分解特征；为探索极化特征和散射特征对积雪识别的贡献，将获取的特征进行组合，得到3种特征集；采用随机森林算法对研究区积雪进行识别提取。结果显示，基于随机森林的Radarsat-2极化特征结合散射特征识别结果的总体精度和调和平均值(F1)达到最高，分别为83.00%和0.82,仅基于极化特征识别结果总体精度和F1分别为77.5%和0.76。研究结果表明，与单一极化特征相比，结合散射特征和极化特征能有效提高积雪识别精度，对山区大范围积雪识别具有极大的潜力。
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基本信息:
DOI：
中图分类号:P426.635;TN957.52
引用信息:
[1]康璇,李晖,黄林.利用分层特征组合策略的全极化SAR山区积雪识别[J].无线电工程,2022,52(12):2211-2221.
基金信息:
福建省自然科学基金(2019J01853); 厦门理工学院科研攀登计划(XPDKT19015)~~