Analysis of Segmentated Images by Generative Adversarial Networks - Viriyasatr, Kittakorn; Luangluewut, Warakorn; Pawgasame, Wichai; Kaewmongkol, Pantape; Mitaim, Sanya; Thiennviboon, Phunsak (Defence Technology Academic Journal, 2024)
This article presents the study on semantic segmentation for aerial image classifi[1]cation using Generative Adversarial Networks (GANs). The aerial images were acquired by an Unmanned Aerial Vehicle (UAV). The proposed method utilized the RGB color space to classify into the total of 10 land-cover classes, including airport, stadium, forest, agricultural area, river, pond, car, road, building, and others. The experiments were conducted using MobileNetV2, ResNet50, ResNet50V2, DenseNet201, and VGG16 as the generator in the GAN framework. The experimental results demonstrate that each model achieved the accuracy of approximately 80% and the processing speed of 2 seconds per frame.