2 research outputs found
Feedback Neural Network based Super-resolution of DEM for generating high fidelity features
High resolution Digital Elevation Models(DEMs) are an important requirement
for many applications like modelling water flow, landslides, avalanches etc.
Yet publicly available DEMs have low resolution for most parts of the world.
Despite tremendous success in image super resolution task using deep learning
solutions, there are very few works that have used these powerful systems on
DEMs to generate HRDEMs. Motivated from feedback neural networks, we propose a
novel neural network architecture that learns to add high frequency details
iteratively to low resolution DEM, turning it into a high resolution DEM
without compromising its fidelity. Our experiments confirm that without any
additional modality such as aerial images(RGB), our network DSRFB achieves
RMSEs of 0.59 to 1.27 across 4 different datasets.Comment: Accepted for publication in IEEE IGARSS 2020 conferenc
AFN: Attentional Feedback Network based 3D Terrain Super-Resolution
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.Comment: Accepted as oral at ACCV 202