Filling damaged pixels in satellite images is a key task present in many Remote Sensing applications. As a representative example of image restoration issue, we can refer to the failure of the Scan Line Corrector (SLC) on board the Landsat Enhanced Thematic Mapper Plus (ETM +) sensor, in which 22% of the scanned pixels in the SLC-off images were missed, thus creating unexpected stipe-type gaps in the scenes. In order to improve the usability of ETM + SLC-off data in a straightforward manner, in this paper we propose a unified methodology that automatically segments and repairs Landsat-7 scenes occluded by stripes. The proposed framework combines Morphology-based filtering, anisotropic diffusion and block-based pixel replication as an effective, fully unsupervised restoration methodology designed to cope with different gap sizes in Landsat images. Our approach does not require having as input data any prior gap mask, side reference image or time-dependent frames of the same scene to work properly. As shown in the experimental results, the current methodology performs adequately for a variety of multispectral remote sensing images with different stripe-size thicknesses and heterogeneous segments. We attest to the accuracy and robustness of our end-to-end framework throughout a variety of qualitative and quantitative evaluations involving state-of-the-art restoration methods.

在卫星图像中填充损坏的像素是许多遥感应用程序中的一项关键任务。作为图像恢复问题的代表性示例，我们可以参考Landsat Enhanced Thematic Mapper Plus（ETM +）传感器上扫描线校正器（SLC）的故障，其中SLC-off中22％的扫描像素处于关闭状态图像被遗漏，从而在场景中产生了意外的刀柄型间隙。为了以一种直接的方式提高ETM + SLC-off数据的可用性，在本文中，我们提出了一种统一的方法，该方法可以自动分割和修复被条纹遮挡的Landsat-7场景。提议的框架结合了基于形态学的滤波，各向异性扩散和基于块的像素复制，作为一种有效的，完全不受监督的恢复方法，旨在应对Landsat图像中的不同间隙尺寸。我们的方法不需要将任何先前的间隙蒙版，侧面参考图像或同一场景的时间相关帧作为输入数据即可正常工作。如实验结果所示，当前的方法对于具有不同条带大小厚度和异构段的各种多光谱遥感图像具有足够的性能。在涉及最先进修复方法的各种定性和定量评估中，我们证明了端到端框架的准确性和可靠性。当前的方法对于具有不同条纹大小厚度和异构片段的各种多光谱遥感图像具有足够的性能。在涉及最先进修复方法的各种定性和定量评估中，我们证明了端到端框架的准确性和可靠性。当前的方法对于具有不同条纹大小厚度和异构片段的各种多光谱遥感图像具有足够的性能。在涉及最先进修复方法的各种定性和定量评估中，我们证明了端到端框架的准确性和可靠性。