AFN: Attentional Feedback Network based 3D Terrain Super-Resolution

• URL: http://arxiv.org/abs/2010.01626v1
• Date: Sun, 4 Oct 2020 16:51:39 GMT
• Title: AFN: Attentional Feedback Network based 3D Terrain Super-Resolution
• Authors: Ashish Kubade, Diptiben Patel, Avinash Sharma, K. S. Rajan
• Abstract summary: We propose a novel fully convolutional neural network-based super-resolution architecture to increase the resolution of low-resolution Digital Elevation Model (LRDEM) We perform the super-resolution of LRDEM using an attention-based feedback mechanism named 'Attentional Feedback Network' (AFN), which selectively fuses the information from LRDEM and aerial image to enhance and infuse the high-frequency features and to produce the terrain realistically.
• Score: 5.349223987137843
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Terrain, representing features of an earth surface, plays a crucial role in many applications such as simulations, route planning, analysis of surface dynamics, computer graphics-based games, entertainment, films, to name a few. With recent advancements in digital technology, these applications demand the presence of high-resolution details in the terrain. In this paper, we propose a novel fully convolutional neural network-based super-resolution architecture to increase the resolution of low-resolution Digital Elevation Model (LRDEM) with the help of information extracted from the corresponding aerial image as a complementary modality. We perform the super-resolution of LRDEM using an attention-based feedback mechanism named 'Attentional Feedback Network' (AFN), which selectively fuses the information from LRDEM and aerial image to enhance and infuse the high-frequency features and to produce the terrain realistically. We compare the proposed architecture with existing state-of-the-art DEM super-resolution methods and show that the proposed architecture outperforms enhancing the resolution of input LRDEM accurately and in a realistic manner.

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