Using Up-to-Date GAN Methods for Aerial Images

Abstract

Object detection and segmentation in aerial images is currently a vibrant and significant field of research. The iSAID dataset has been created for object detection in images captured by aerial vehicles. In this study, image semantic segmentation was performed on the iSAID dataset using Generative Adversarial Networks (GANs). The compared GAN methods are CycleGAN, DCLGAN, SimDCL, and SSimDCL. All methods operate on unpaired images. DCLGAN and SimDCL methods are derived by taking inspiration from the CycleGAN method. In these methods, cost functions and network structures vary. This study thoroughly examines the methods, and their similarities and differences are observed. After semantic segmentation is performed, the results are presented using both visual and measurement metrics. Measurement metrics such as FID, KID, PSNR, FSIM, SSIM, and MAE are used. Experimental studies show that SSimDCL and SimDCL methods outperform other methods in iSAID image semantic segmentation. CycleGAN method, on the other hand, is observed to be less successful compared to other methods. The aim of this study is to perform automatic semantic segmentation in aerial images.