Key Laboratory of Technology in Geo-Spatial Information Processing and Application Systems, Institute of Electronics, Chinese Academy of Sciences, Beijing 100190, China.
Institute of Electronics, Chinese Academy of Sciences, Beijing 100190, China.
Sensors (Basel). 2018 May 24;18(6):1701. doi: 10.3390/s18061701.
With the increasing demand for high-resolution remote sensing images for mapping and monitoring the Earth's environment, geometric positioning accuracy improvement plays a significant role in the image preprocessing step. Based on the statistical learning theory, we propose a new method to improve the geometric positioning accuracy without ground control points (GCPs). Multi-temporal images from the ZY-3 satellite are tested and the bias-compensated rational function model (RFM) is applied as the block adjustment model in our experiment. An easy and stable weight strategy and the fast iterative shrinkage-thresholding (FIST) algorithm which is widely used in the field of compressive sensing are improved and utilized to define the normal equation matrix and solve it. Then, the residual errors after traditional block adjustment are acquired and tested with the newly proposed inherent error compensation model based on statistical learning theory. The final results indicate that the geometric positioning accuracy of ZY-3 satellite imagery can be improved greatly with our proposed method.
随着对用于测绘和监测地球环境的高分辨率遥感图像的需求不断增加，几何定位精度的提高在图像预处理步骤中起着重要作用。基于统计学习理论，我们提出了一种无需地面控制点 (GCP) 即可提高几何定位精度的新方法。我们的实验测试了来自 ZY-3 卫星的多时相图像，并应用了偏置补偿有理函数模型 (RFM) 作为区域网平差模型。改进并利用了一种简单且稳定的权重策略和广泛应用于压缩感知领域的快速迭代收缩阈值算法 (FIST) 来定义法方程矩阵并求解它。然后，获取传统区域网平差后的残差，并使用基于统计学习理论的新提出的固有误差补偿模型对其进行测试。最终结果表明，我们提出的方法可以大大提高 ZY-3 卫星图像的几何定位精度。
