If this kind of problem is faced where results are mismatched because of wrong mapping, Pix2Pix model which expects paired data can be used. It is important to note that the CycleGAN model expects unpaired data and it does not have any information on mapping SAR to RGB pixels, so it may map dark pixels in the source image to darker shaded pixels in the other image which may not be right always (especially in agricultural land areas). We will train a CycleGAN model for this case. ![]() We will train a deep learning model to translate SAR imagery to RGB imagery, thereby making optical data (translated) available even in extreme weather days and cloudy areas. In this sample notebook, we will see how we can make use of benefits of SAR and optical imagery to perform all season earth observation. The only disadvantage of SAR data is the unavailability of labelled data as it is more difficult for users to understand and label SAR data than optical imagery. Now a days a lot of organizations are investing in SAR data making it more available to users than before. This is the time when earth observation can reap maximum benefits, but optical sensors prevent us doing that. ![]() The ability of SAR data to let us see through clouds make it more valuable specially in cloudy areas and bad weather.
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