以新疆于田绿洲为研究区，利用四极化PALSAR-2数据进行多种目标极化分解处理，获取相应的极化特征参数。通过目视判读选取噪声较少的11种极化参数作为最佳特征信息对支持向量机分类法进行训练。多种极化分解方法与Wishart分类方法及支持向量机分类法相结合，提取研究区不同程度的盐渍化信息。经过目视判读和实地野外考察，结合Landsat-8陆地成像仪影像对分类结果进行定量分析和验证。由混淆矩阵的计算分析可知，相比Wishart分类方法，支持向量机分类法将分类精度从80.48%提高到88.00%，将Kappa系数从0.73提高到0.83。结果表明，单独的相干分解不能充分挖掘PALSAR-2数据包含的丰富信息，将目标极化分解参数用于特征信息分类处理，可以达到较好的分类效果；利用全极化PALSAR-2数据，结合目标极化分解方法和支持向量机分类法提取盐渍化信息有一定的优势。

Choosing Yutian Oasis as the study area, the polarimetric characteristic parameters are obtained from quad-polarized PALSAR-2 data in a variety of target polarization decomposition treatments. 11 polarized parameters with lesser noise are selected as the best classification features through visual interpretation to train the support vector machine classification. Wishart classification, support vector machine classification and several polarimetric decomposition methods are combined to extract the different levels of soil salinization information. Classification results are quantitatively analyzed and validated by Landsat-8 operational land imager image combined with visual interpretation and field investigation. The analysis results of confusion matrix show that, comparing with Wishart classification, the support vector machine classification increases the classification accuracy and Kappa coefficient from 80.48% to 88.00% and 0.73 to 0.83 respectively. It is illustrated that the individual coherent decomposition cannot fully exploit the rich information of PALSAR-2 data, and the good classification results can be achieved by using the target polarimetric decomposition parameter for features classification process. Using fully polarimetric PALSAR-2 data, the method combining target polarimetric decomposition with support vector machine classification has advantage to extracting salinization information.