Abstract
In recent years, the interpretation of SAR images has been significantly improved with the development of deep learning technology, and using conditional generative adversarial nets (CGANs) for SAR-to-optical transformation, also known as image translation, has become popular. Most of the existing image translation methods based on conditional generative adversarial nets are modified based on CycleGAN and pix2pix, focusing on style transformation in practice. In addition, SAR images and optical images are characterized by heterogeneous features and large spectral differences, leading to problems such as incomplete image details and spectral distortion in the heterogeneous transformation of SAR images in urban or semiurban areas and with complex terrain. Aiming to solve the problems of SAR-to-optical transformation, Serial GANs, a feature-preserving heterogeneous remote sensing image transformation model, is proposed in this paper for the first time. This model uses the Serial Despeckling GAN and Colorization GAN to complete the SAR-to-optical transformation. Despeckling GAN transforms the SAR images into optical gray images, retaining the texture details and semantic information. Colorization GAN transforms the optical gray images obtained in the first step into optical color images and keeps the structural features unchanged. The model proposed in this paper provides a new idea for heterogeneous image transformation. Through decoupling network design, structural detail information and spectral information are relatively independent in the process of heterogeneous transformation, thereby enhancing the detail information of the generated optical images and reducing its spectral distortion. Using SEN-2 satellite images as the reference, this paper compares the degree of similarity between the images generated by different models and the reference, and the results revealed that the proposed model has obvious advantages in feature reconstruction and the economical volume of the parameters. It also showed that Serial GANs have great potential in decoupling image transformation.
Highlights
Introduction iationsIn recent years, there have been more and more applications of remote sensing images in environmental monitoring, disaster prevention, intensive farming, and homeland security
The image transformation method we proposed uses synthetic aperture radar (SAR) images as prior information to restore and reconstruct SAR images based on the gradient contour and spectrum
To address the problem of feature loss and distortion in SAR-to-optical tasks, this paper proposed a feature-preserving heterogeneous image transformation model using Serial
Summary
There have been more and more applications of remote sensing images in environmental monitoring, disaster prevention, intensive farming, and homeland security. Optical images are widely used due to their high spectral resolution and easy interpretation. The disadvantage is that they are sensitive to meteorological conditions, especially clouds and haze, which severely limits their use for observation and monitoring of ground targets [1]. Synthetic aperture radar (SAR) sensors can overcome adverse meteorological conditions by creating images using a longer wavelength of radio waves to obtain all-day and all-weather continuous observations. SAR images have significant advantages over optical images, their application is still limited by the difficulty of SAR image interpretation. Because synthetic aperture radar is a side range-measuring instrument, the imaging effect is affected by the distance between
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