无参考图条件下的Landsat-7 SLC-off图像缺失像素填充算法研究

针对2003年以后Landsat-7扫描线纠正器失效问题，提出一种利用回归分析和三次样条插值对图像缺失像素进行恢复的算法，该方法无需使用参考图，并以非监督分类结果为准则。首先对图像进行预处理，包括条带定位、非监督分类和局部平均灰度计算；然后，利用回归分析计算在每个条带像素的3个方向上条带外侧像素的灰度变化趋势；在非监督分类准则下，通过判断各个方向条带两侧像素类型异同的策略确定条带当前像素的填充方向；随后，对条带边界点和非边界点采用不同算法分别填充。对非边界像素，在填充方向上利用三次样条插值计算当前像素的灰度插值；最后，对条带进行自适应滤波处理。实验结果表明，在不需要参考其它遥感影像与数据的条件下，研究方法与常用的有参考图方法相比较差别不大，而其均方根误差值17.590 4接近于最优的权重线性回归方法的17.400 6。

Filling Algorithm for Missing Pixels in Landsat-7 SLC-off Images Using No Reference Images

The Landsat-7 scan line corrector failed since 2003, to recover missing pixels in the image we propose a no-reference-image algorithm using regression analysis and cubic spline interpolation under unsupervised classification criteria in this paper. First, in the preprocess stage, stripe location, unsupervised classification and local average gray calculation are carried out. Then, regression analysis is applied to calculate the gray slopes of the pixels in the three directions outside the stripes. Under the unsupervised classification criteria, we determine the filling direction of the current pixel of the stripe by judging the similarities and differences of the types of non-missing pixels on both sides of the strip in each direction. Subsequently, different algorithms are used to fill in the strip boundary points and non-boundary points, respectively. For non-boundary pixels, cubic spline interpolation is performed in the filling direction. Finally, an adaptive filter denoises the stripe area. The experimental results show that no difference can be found from the proposed no-reference-image method to the commonly used methods with reference images. The RMSE value of the proposed method (17.5904) is close to the value of the optimal WLR method (17.4006). The visual effect of the proposed method is better, and notrace of stripes can be found. This algorithm can effectively recover the Landsat-7 satellite SLC-off images after 2003, and provide effective information for further geospatial analysis.