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One of the persistent challenges in multispectral image analysis is the interference caused by dense cloud cover and its resulting shadows, which can significantly obscure surface features. This becomes especially problematic when attempting to monitor surface changes over time using satellite imagery, such as from Landsat-8. In this study, rather than simply masking visual obstructions, we aimed to investigate the role and influence of clouds within the spectral data itself. To achieve this, we employed Independent Component Analysis (ICA), a statistical method capable of decomposing mixed signals into independent source components. By applying ICA to selected Landsat-8 bands and analyzing each component individually, we assessed the extent to which cloud signatures are entangled with surface data. This process revealed that clouds contribute to multiple ICA components simultaneously, indicating their broad spectral influence. With this influence on multiple wavebands, we managed to configure a set of components that could perfectly delineate the extent and location of clouds. Moreover, because Landsat-8 lacks cloud-penetrating wavebands, such as those in the microwave range (e.g., SAR), the surface information beneath dense cloud cover is not captured at all, making it physically impossible for ICA to recover what is not sensed in the first place. Despite these limitations, ICA proved effective in isolating and delineating cloud structures, allowing us to selectively suppress them in reconstructed images. Additionally, the technique successfully highlighted features such as water bodies, vegetation, and color-based land cover differences. These findings suggest that while ICA is a powerful tool for signal separation and cloud-related artifact suppression, its performance is ultimately constrained by the spectral and spatial properties of the input data. Future improvements could be realized by integrating data from complementary sensors—especially those operating in cloud-penetrating wavelengths—or by using higher spectral resolution imagery with narrower bands.
Details
Reconstruction period-US;
Deep learning;
Datasets;
Landsat;
Independent component analysis;
Cloud cover;
Satellite imagery;
Image processing;
Statistical methods;
Land cover;
Shadows;
Machine learning;
Vegetation;
Obstructions;
Image analysis;
Remote sensing;
Image reconstruction;
Spectral resolution;
Environmental monitoring;
Wavelengths;
Clouds
; Thony, Yan 1 ; Deng Liangdong 3 ; Malek, Adjouadi 1 1 Center for Advanced Technology and Education, Department of Electrical and Computing Engineering, College of Engineering and Computing, Florida International University, 10555 West Flagler St. EC 3900, Miami, FL 33174, USA; [email protected] (M.A.B.-P.); [email protected] (M.A.)
2 Center for Advanced Technology and Education, Department of Electrical and Computing Engineering, College of Engineering and Computing, Florida International University, 10555 West Flagler St. EC 3900, Miami, FL 33174, USA; [email protected] (M.A.B.-P.); [email protected] (M.A.), Knight Foundation School of Computing and Information Sciences, College of Engineering and Computing, Florida International University, 11200 SW 8th Street, CASE 354, Miami, FL 33199, USA
3 Knight Foundation School of Computing and Information Sciences, College of Engineering and Computing, Florida International University, 11200 SW 8th Street, CASE 354, Miami, FL 33199, USA