Remote sensing pushbroom-type imaging systems acquire entire columns of an image with a single detector. As a consequence, the miss-calibration of the detectors produces stripes on the image. In this context, this article introduces a new self-calibration destriping method based on an affine response model for the detectors, called statistical affine destriping (SAD). In contrast, some previous contributions were limited to a purely linear model, while many others only considered an additive structured noise model. It is based on the maximum a posteriori estimation of the gain and offset parameters attached to each detector given the observed image. Simple statistical prior assumptions are adopted: respectively, a Gaussian white noise model for the gains and offsets, and a first-order, homogeneous Markov model for the observed scene. Based on a simplification of the posterior likelihood, we propose a very efficient optimization scheme based on a constrained majorize-minimize principle, allowing us to process large dimension images. Moreover, simple empirical rules are given to tune the hyperparameters of the destriping method for high-resolution satellite images. Compared to the performance of a destriping method limited to gain correction, we observe that the new version provides reliable results in a wider range of situations. We also extend the method in two directions. On the one hand, we consider that some detectors may be atypical, with very high or very low gains or offsets. On the other hand, we extend the method to multispectral image destriping.

中文翻译：

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基于仿射探测器响应的推扫式图像的统计去条纹


遥感推扫式成像系统使用单个探测器获取图像的整列。因此，探测器的错误校准会在图像上产生条纹。在此背景下，本文介绍了一种基于探测器仿射响应模型的新自校准去条纹方法，称为统计仿射去条纹（SAD）。相比之下，以前的一些贡献仅限于纯线性模型，而许多其他贡献仅考虑加性结构化噪声模型。它基于给定观察图像的每个检测器所附加的增益和偏移参数的最大后验估计。采用简单的统计先验假设：增益和偏移分别采用高斯白噪声模型，观察场景采用一阶齐次马尔可夫模型。基于后验似然的简化，我们提出了一种基于约束最大最小化原则的非常有效的优化方案，使我们能够处理大尺寸图像。此外，给出了简单的经验规则来调整高分辨率卫星图像去条纹方法的超参数。与仅限于增益校正的去条带方法的性能相比，我们观察到新版本在更广泛的情况下提供了可靠的结果。我们还在两个方向上扩展该方法。一方面，我们认为某些探测器可能是非典型的，具有非常高或非常低的增益或偏移。另一方面，我们将该方法扩展到多光谱图像去条纹。