PGSOFuse-Net: PCA and GAN Integration for SAROptical Image Fusion

Abstract

To illustrate, radar images, popularly known as Synthetic Aperture Radar (SAR) and optical images are very useful in satellite-based information retrieval. But, the complementary nature of both these data led to misinterpretation of the information in various fields like military surveillance, weather forecasting, and resource management. However, the study of image fusion on optical SAR images has overcome some of the limitations in the remote sensing field with cloud removal, change detection, and infrastructure identification. There have been traditional approaches like wavelet analysis, cosine transform, and sparse representation in image fusion which were not more effective in analysing the nature of SAR and optical images. Hence, the advanced algorithms based on neural networks which are very much effective in image fusion, especially in medical diagnostics, social networks, and surveillance are used in the anticipated study. Recent studies of deep-learning-based approaches specifically Generative Adversarial Networks (GAN) architecture suggest decent developments in image fusion. With good developments in Principal Component Analysis for image compression, image segmentation, and image representation in satellite imagery, the suggested work develops a hybrid fusion model which is an innovative combination of Principal Component Analysis and GAN to produce the target image. The effectiveness of the proposed unsupervised algorithm improves the results and preserves the characteristics of both image types. The results specify the importance of the GAN in the SAR optical fusion with the proposed PCA GAN SAR Optical Fusion Network. Subsequently, the assessment of the quality metrics reveals that the current study outperforms prior works and is very useful for future geoinformation.