摘要
为改善西北半干旱地区的土壤含水率监测精度,该文选择张掖地区黑河流域为研究区,提出了一种基于主被动遥感融合数据贝叶斯网络分类的土壤水分信息提取方法。该方法依据光学与雷达遥感数据本身在反演土壤水方面的各自优势,首先利用小波变换与IHS结合的算法将TM5、4、3与ASAR数据融合,融合规则采用局部距离最大替代法,在突出融合影像细节的同时,一定程度上保留了TM数据的光谱信息。然后构建BN网络进行分类,以融合后新的R'、G'、B'分量和TM6波段作为网络的输入,输出为5个不同的类别,分别对应5个不同等级的土壤水分含量。经实测数据对融合前后分类结果的比较分析,结果表明,此方法在植被区能取得更好的效果,分类精度达到76.1%,对荒漠区效果欠佳。因此该方法在植被覆盖区对提取区域土壤水分信息是可行的、有效的。
For improving the precision of soil moisture monitoring,a classifier based on integration of both active and passive remote sensing data and the Bayesian Networks for inversion of soil moisture was presented and tested in Heihe river basin,a semi-arid area in the north-west of China.In the algorithm the wavelet transform and IHS were combined to integrate TM3,TM4,TM5 and ASAR data.The method of maximum distance in local region was adopted as the fusion rule for prominent expression of the detailed information in the fusion image,and the spectral information of TM could be retained.Then the new R 、G、B components in the fusion image and the TM6 were used as the input of the Bayesian network,and the outputs were five different categories corresponding to different levels of soil moisture values.The field measurement was carried out for validation of the method.A better result was acquired in vegetation coverage area,and the precision of classification could reach up to 76.1%,but ineffective in desert areas.So the method is applicable for reflecting the distribution of soil moisture in vegetation covered area.
出处
《农业工程学报》
EI
CAS
CSCD
北大核心
2011年第6期187-192,I0003,共7页
Transactions of the Chinese Society of Agricultural Engineering
基金
国家重点基础研究发展计划(973项目)"陆表生态环境要素主被动遥感协同反演理论与方法"(2007CB714407)
中国测绘科学研究院科研基本业务经费(7771023)
关键词
土壤
水分
遥感
数据融合
贝叶斯分类
soils
moisture
remote sensing
data fusion
Bayesian Networks classification
