Object-aided Generative Adversarial Networks for Remote Sensing Image Generation
Xingzhe Su, Daixi Jia, Fengge Wu, Junsuo Zhao, Changwen Zheng · 2024
While generative adversarial networks have made significant strides in natural image synthesis, their performance in specialized remote sensing (RS) imagery, particularly in capturing fine details of small objects like airplanes and ships, needs enhancement. This deficiency frequently results in shape distortion within the generated images. This challenge, compounded by the prohibitive costs of annotating RS images, motivates the development of an efficient unsupervised approach. In response, this paper introduces Object-Aided Generative Adversarial Network (OAGAN), an innovative model for unsupervised RS image generation. Initially, it employs an object-centric learning mechanism to extract structural semantic maps of foreground objects, without the need for labels. Subsequently, this paper proposes the Shape Encoding Layer (SEL) to encode the structural semantics of objects, which is seamlessly integrated into the intermediate layers of the generative model. This integration enables the model to prioritize the structural information of foreground objects. Additionally, to enhance the diversity of generated images, this paper designs a novel style regularization term. Comprehensive experiments are conducted on three distinct RS image datasets. Experiment results demonstrate that the proposed method surpasses state-of-the-art models in terms of the quality of generated images.