面向遥感影像镶嵌的SVR色彩一致性处理

Support vector regression color normalization method for image mosaic

由于成像条件与环境的差异,多景待镶嵌遥感影像之间往往会出现色彩差异,针对此问题,提出一种基于支持向量回归(SVR)的色彩一致性处理方法。采用NDVI(归一化植被指数)阈值分割并结合光谱角匹配(SAM)的方法在影像重叠区域自动选取具有"不变特征"的像素作为样本;通过SVR建立输入影像到参考影像的灰度值变换方程,并对输入影像进行处理,使得待镶嵌影像具有与参考影像相同或者相似的亮度与对比度。采用TM、SPOT、无人机(UAV)影像等多源数据进行了实验,结果表明,该方法能够有效消除由系统因素引起的色差,与线性回归方法相比,该算法在方差、辐射分辨率等方面具有优势。

Due to the variation of imaging conditions onboard, the chromaticity among a collection of remote sensing images that are to create image mosaics often differs. In this regard, a method for keeping color consistency based on support vector regression （SVR） is presented. First, the pixels, with invariant features, are automatically selected in the overlapping areas, which are based on the image threshold segmentation and on spectral angle matching （SAM）. These pixels are used to build transformation equations on digital numbers between the original image and the reference image using SVR. Finally, the brightness of the images to be stitched is corrected to the same reference image using the corresponding transformation equations. The approach mentioned was implemented on the thematic mapper （TM） imagery, SPOT satellite imagery and unmanned aerial vehicle （ UAV ） images. Our results show that this method can effectively alleviate the chromaticity differences. Compared with the linear regression method, the above method achieves larger variance and higher radiation resolution.