Feedback Neural Network based Super-resolution of DEM for generating high fidelity features

• URL: http://arxiv.org/abs/2007.01940v1
• Date: Fri, 3 Jul 2020 21:10:19 GMT
• Title: Feedback Neural Network based Super-resolution of DEM for generating high fidelity features
• Authors: Ashish Kubade, Avinash Sharma, K S Rajan
• Abstract summary: We propose a novel neural network architecture that learns to add high frequency details iteratively to low resolution DEM. Our network DSRFB achieves RMSEs of 0.59 to 1.27 across 4 different datasets.
• Score: 4.722870664660785
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

Related papers

• A Heterogeneous Dynamic Convolutional Neural Network for Image Super-resolution [111.97970576223622]
  We present a heterogeneous dynamic convolutional network in image super-resolution (HDSRNet) The lower network utilizes a symmetric architecture to enhance relations of different layers to mine more structural information. The relevant experimental results show that the proposed HDSRNet is effective to deal with image resolving.
  arXiv  Detail & Related papers  (2024-02-24T03:44:06Z)
• RDRN: Recursively Defined Residual Network for Image Super-Resolution [58.64907136562178]
  Deep convolutional neural networks (CNNs) have obtained remarkable performance in single image super-resolution. We propose a novel network architecture which utilizes attention blocks efficiently.
  arXiv  Detail & Related papers  (2022-11-17T11:06:29Z)
• A Robust and Low Complexity Deep Learning Model for Remote Sensing Image Classification [1.9019295680940274]
  We present a robust and low complexity deep learning model for Remote Sensing Image Classification (RSIC) By conducting extensive experiments on the benchmark datasets NWPU-RESISC45, we achieve a robust and low-complexity model.
  arXiv  Detail & Related papers  (2022-11-05T06:14:30Z)
• Pyramid Grafting Network for One-Stage High Resolution Saliency Detection [29.013012579688347]
  We propose a one-stage framework called Pyramid Grafting Network (PGNet) to extract features from different resolution images independently. An attention-based Cross-Model Grafting Module (CMGM) is proposed to enable CNN branch to combine broken detailed information more holistically. We contribute a new Ultra-High-Resolution Saliency Detection dataset UHRSD, containing 5,920 images at 4K-8K resolutions.
  arXiv  Detail & Related papers  (2022-04-11T12:22:21Z)
• NeRF-SR: High-Quality Neural Radiance Fields using Super-Sampling [82.99453001445478]
  We present NeRF-SR, a solution for high-resolution (HR) novel view synthesis with mostly low-resolution (LR) inputs. Our method is built upon Neural Radiance Fields (NeRF) that predicts per-point density and color with a multi-layer perceptron.
  arXiv  Detail & Related papers  (2021-12-03T07:33:47Z)
• DEM Super-Resolution with EfficientNetV2 [0.0]
  Digital Elevation Model (DEM) datasets are such examples whereas their low-resolution versions are widely available, high-resolution ones are scarce. The proposed model increases the spatial resolution of DEMs up to 16times without additional information.
  arXiv  Detail & Related papers  (2021-09-20T16:26:58Z)
• Learning Frequency-aware Dynamic Network for Efficient Super-Resolution [56.98668484450857]
  This paper explores a novel frequency-aware dynamic network for dividing the input into multiple parts according to its coefficients in the discrete cosine transform (DCT) domain. In practice, the high-frequency part will be processed using expensive operations and the lower-frequency part is assigned with cheap operations to relieve the computation burden. Experiments conducted on benchmark SISR models and datasets show that the frequency-aware dynamic network can be employed for various SISR neural architectures.
  arXiv  Detail & Related papers  (2021-03-15T12:54:26Z)
• AFN: Attentional Feedback Network based 3D Terrain Super-Resolution [5.349223987137843]
  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.
  arXiv  Detail & Related papers  (2020-10-04T16:51:39Z)
• Accurate and Lightweight Image Super-Resolution with Model-Guided Deep Unfolding Network [63.69237156340457]
  We present and advocate an explainable approach toward SISR named model-guided deep unfolding network (MoG-DUN) MoG-DUN is accurate (producing fewer aliasing artifacts), computationally efficient (with reduced model parameters), and versatile (capable of handling multiple degradations) The superiority of the proposed MoG-DUN method to existing state-of-theart image methods including RCAN, SRDNF, and SRFBN is substantiated by extensive experiments on several popular datasets and various degradation scenarios.
  arXiv  Detail & Related papers  (2020-09-14T08:23:37Z)
• Real Image Super Resolution Via Heterogeneous Model Ensemble using GP-NAS [63.48801313087118]
  We propose a new method for image superresolution using deep residual network with dense skip connections. The proposed method won the first place in all three tracks of the AIM 2020 Real Image Super-Resolution Challenge.
  arXiv  Detail & Related papers  (2020-09-02T22:33:23Z)
• D-SRGAN: DEM Super-Resolution with Generative Adversarial Networks [0.0]
  LIDAR data has been used as the primary source of Digital Elevation Models (DEMs) DEMs have been used in a variety of applications like road extraction, hydrological modeling, flood mapping, and surface analysis. Deep learning techniques have become attractive to researchers for their performance in learning features from high-resolution datasets.
  arXiv  Detail & Related papers  (2020-04-09T19:57:49Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.