单极化合成孔径雷达图像颜色特征编码与分类

针对目前单极化合成孔径雷达（SAR）伪彩色编码方法存在的细节信息和可视性不强的问题，提出一种颜色特征编码方法。该颜色特征编码方法首先对单极化SAR图像提取纹理特征；然后将每一个特征量化到0到255；其次对每一个灰度级赋予一个RGB颜色，编码成颜色特征图；最后对随机森林计算得到的特征重要性进行排序，每3维特征对应为R、G、B通道生成伪彩图。基于该颜色特征编码方法，提出一种新的分类方法。该分类方法首先根据目视效果选择可分性最好的伪彩图；然后采用统计区域合并（SRM）分割算法对其分割；其次将所有RGB伪彩图作为分类的特征，以随机森林为分类器进行分类，得到初步的结果；最后对初步的结果进行相对多数投票，得到最终的分类结果。方法验证采用两组TerraSAR-X单极化SAR数据，与基于HIS的颜色编码方法对比，该颜色特征编码方法生成的伪彩图信息熵得到了很大提升，且两组数据每类地物的分类精度都大幅度提高，因此证明了所提算法保留了更多的细节信息，获取更多的颜色信息，更利于可视化和地物分类，从而表明提出的颜色特征编码方法是可行的。

Color feature coding and classification of single polarized synthetic aperture radar image

Aiming at the problem of poor detail and visibility in current color coding methods of single polarization Synthetic Aperture Radar (SAR), a color feature coding method was proposed. Firstly, texture features were extracted from a single-polarized SAR image. Secondly, each feature was quantized to 0 to 255. Then an RGB color was assigned to each gray level to generate a color feature map. Finally, the importance of features calculated by random forest was sorted; the pseudo-color graphs were generated by each three dimensional feature corresponding to the R, G, and B channels. Based on the presented color feature coding method, a new classification method was proposed. Firstly, the pseudo color map with the best geographical separability was selected according to the visual effect, and then segmented by the Statistical Region Merging (SRM) segmentation algorithm. Secondly, all the RGB pseudo color maps were used as the classification features, and a random forest was used as the classifier and obtain the preliminary results. At the end, a relative majority vote was made on the preliminary results and the final classification results were obtained. In the method verification, two sets of TerraSAR-X single-polarization SAR data were used. By comparing the corresponding grayscale image with HIS-based color coding method, the color image information entropy generated by the proposed color feature coding method was greatly improved, and the classification accuracy of each type of ground features for two data sets was greatly improved. It is demonstrated that the proposed algorithm preserves more details for more color information, and it is more conducive to visualization and terrain classification, which indicating the proposed color feature coding method is feasible.