基于主被动遥感数据融合的土壤水分信息提取

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。

Soil moisture information extraction based on integration of active and passive remote sensing data

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 informati...