2021 IEEE International Conference on Image Processing

19-22 September 2021 • Anchorage, Alaska, USA

Imaging Without Borders

2021 IEEE International Conference on Image Processing

19-22 September 2021 • Anchorage, Alaska, USA

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Paper Detail

Paper IDSS-RSDA.11
Paper Title SEMANTIC-AWARE NETWORK FOR AERIAL-TO-GROUND IMAGE SYNTHESIS
Authors Jinhyun Jang, Taeyong Song, Kwanghoon Sohn, Yonsei University, Republic of Korea
SessionSS-RSDA: Special Session: Computer Vision and Machine Learning for Remote Sensing Data Analysis
LocationArea C
Session Time:Tuesday, 21 September, 08:00 - 09:30
Presentation Time:Tuesday, 21 September, 08:00 - 09:30
Presentation Poster
Topic Special Sessions: Computer Vision and Machine Learning for Remote Sensing Data Analysis
IEEE Xplore Open Preview  Click here to view in IEEE Xplore
Abstract Aerial-to-ground image synthesis is an emerging and challenging problem that aims to synthesize a ground image from an aerial image. Due to the highly different layout and object representation between the aerial and ground images, existing approaches usually fail to transfer the components of the aerial scene into the ground scene. In this paper, we propose a novel framework to explore the challenges by imposing enhanced structural alignment and semantic awareness. We introduce a novel semantic-attentive feature transformation module that allows to reconstruct the complex geographic structures by aligning the aerial feature to the ground layout. Furthermore, we propose semantic-aware loss functions by leveraging a pre-trained segmentation network. The network is enforced to synthesize realistic objects across various classes by separately calculating losses for different classes and balancing them. Extensive experiments including comparisons with previous methods and ablation studies show the effectiveness of the proposed framework both qualitatively and quantitatively.