Enhancement and Optimization of Drone Oblique Photography Data Based on Generative Adversarial Network (GAN)

In order to solve the image quality problem caused by environmental changes, shooting angles and other factors in drone oblique photography data, GAN (Generative Adversarial Network) is introduced for data enhancement and optimization, aiming to improve the quality and accuracy of drone images. Firstly, the GAN framework is used for data enhancement, the generator is trained to generate a variety of high-quality images, and the generation process is optimized by combining the discriminator to ensure that the enhanced images have good realism and diversity. Secondly, in order to optimize the data quality, cGAN (Conditional Generative Adversarial Network) is introduced to conditionally adjust the input image to generate images that are highly adaptable to different environmental factors (such as lighting, weather, angle, etc.). Finally, the optimization effect is quantitatively evaluated by comparing the enhanced image with the original image, combined with image quality evaluation indicators (such as PSNR (peak signal-to-noise ratio), SSIM (structural similarity), etc.), and the training strategy of the GAN model is further adjusted to enhance its generalization ability in practical applications. cGAN (Conditional Generative Adversarial Network) shows the best image quality, with a PSNR of 30.2 dB and an SSIM of 0.91. Compared with the traditional unconditional GAN (PSNR 26.3 dB, SSIM 0.83), cGAN can generate clearer images with higher structural fidelity. The GAN-based UAV oblique photography data enhancement optimization method can effectively improve image quality, enrich data samples, and improve the accuracy of subsequent analysis.