ABSTRACT

Clouds cover corrupts the spatial and spectral information of optical remote sensing (RS) images, which seriously affects the use of RS data. To solve the problem of missing information, a spatial-spectral adaptive method based on slow feature analysis (SFA) is proposed to restore cloudy scenes in this letter. SFA converts the sequence signal into slowly varying signal signatures and the clouds will be located in the first component. We propose spatial and spectral adaptive correction methods to reduce interference from highlighted pixels and jointly constrain the cloud coefficients in each band according to reflectance and gradient. The effectiveness of our method is verified on Landsat-8 OLI simulated and real cloudy datas, the restored results are visually rich in texture detail and moderately corrected. The average PSNR of the four real scenes is 42.9738 dB and coefficient of determination (R²) is 0.8203, and many indicators have proved that our method is better than the existing methods.

摘要

云覆盖破坏了光学遥感（RS）图像的空间和光谱信息，严重影响了RS数据的使用。为解决信息丢失的问题，本文提出了一种基于慢速特征分析（SFA）的空间-光谱自适应方法来恢复这封信中的多云场景。SFA 将序列信号转换为缓慢变化的信号特征，云将位于第一个分量中。我们提出了空间和光谱自适应校正方法，以减少来自突出像素的干扰，并根据反射率和梯度联合约束每个波段中的云系数。我们的方法的有效性在 Landsat-8 OLI 模拟和真实多云数据上得到验证，恢复的结果在视觉上纹理细节丰富且经过适度校正。R ² ) 为0.8203，多项指标证明我们的方法优于现有方法。