对比了线性混合光谱分解模型(SMA)与支持向量机(SVM)在TM影像上估算不透水面覆盖率(ISP)的精度,通过SVM模型拟合TM像元光谱特征与样本ISP间的关联而获得对未知像元ISP的估算能力。对于天津市主城区的TM影像,选择学校区、工矿区和住宅区的高分辨率影像分类结果作为训练样本(7020个)和验证样本(1500个),SVM模型的ISP估算均方差(15.4%)优于SMA估算结果(19.4%);在增加缨帽变化“绿度分量”及混合光谱分解“高反射率分量”作为SVM特征变量后,ISP估算精度提高为12%。研究结果表明：SVM模型能够拟合各像元光谱组分间非线性关系且具有较好小样本泛化的性能,适用于地面样本较少的大区域ISP制图;增加与ISP相关性大的光谱特征向量作为SVM输入能提供更多的区域地物空间分布信息,能够调整无样本的地表类型的ISP估算值,提高区域ISP估算的整体精度。

Impervious surface percentage (ISP) is the key parameter for urban regional environment research. This paper compares the ISP estimate-performance of spectral mixture analysis (SMA) and support vector machine (SVM) on TM image. The SVM model establish the non-linear relationship between spectral feature of TM pixels and corresponding ground sample ISP values and then be implied on without-sample TM pixels for ISP estimation. On the TM image of Tianjin urban area, we fi rst select high resolution image from Quickbird classifi cation results, including college, industrial and residential districts as training sample (7020 items) and then test sample (1500 items). The toot mean square error (RMSE) of SVM model is 15.4%, which is better than SMA with 19.4%. Additionally, after adding “greenness” of tasseled cap transform and “high-albedo” of SMA, the RMSE decreases to 12%. The results of the study indicate that SVM model is suitable for large area ISP mapping without insuffi cient samples because of the non-linear characteristic and good performance of small-sample generalization. By adding spectral feature vector having signifi cant relation with ISP, it can adjust the value of ISP estimation where the land cover types is lack of training samples and improve the overall accuracy of regional ISP estimation.