Second, the hyper-feature maps are inputted into the G-FAN to reconstruct the missing data of the cloud-covered area by aggregating the electromagnetic backscattering information of the SAR image, and the spectral information of neighborhood and non-neighborhood pixels in the optical image. First, cloudy optical images and contemporary SAR images are concatenated and transformed into hyper-feature maps by pre-convolution. To overcome these weaknesses, we propose a novel cloud removal method using SAR-optical data fusion and a graph-based feature aggregation network (G-FAN).
While SAR images used as auxiliary information for cloud removal may be blurred and noisy, the similar non-local information of spectral and electromagnetic features cannot be effectively utilized by traditional cloud removal methods.
Among these methods, synthetic aperture radar (SAR)-based methods have more potential than others because SAR imaging is hardly affected by clouds, and can reflect ground information differences and changes.
Many cloud removal methods have been proposed to solve this issue. In observations of Earth, the existence of clouds affects the quality and usability of optical remote sensing images in practical applications.
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