20,942 research outputs found
Multi-image Super Resolution of Remotely Sensed Images using Residual Feature Attention Deep Neural Networks
Convolutional Neural Networks (CNNs) have been consistently proved
state-of-the-art results in image Super-Resolution (SR), representing an
exceptional opportunity for the remote sensing field to extract further
information and knowledge from captured data. However, most of the works
published in the literature have been focusing on the Single-Image
Super-Resolution problem so far. At present, satellite based remote sensing
platforms offer huge data availability with high temporal resolution and low
spatial resolution. In this context, the presented research proposes a novel
residual attention model (RAMS) that efficiently tackles the multi-image
super-resolution task, simultaneously exploiting spatial and temporal
correlations to combine multiple images. We introduce the mechanism of visual
feature attention with 3D convolutions in order to obtain an aware data fusion
and information extraction of the multiple low-resolution images, transcending
limitations of the local region of convolutional operations. Moreover, having
multiple inputs with the same scene, our representation learning network makes
extensive use of nestled residual connections to let flow redundant
low-frequency signals and focus the computation on more important
high-frequency components. Extensive experimentation and evaluations against
other available solutions, either for single or multi-image super-resolution,
have demonstrated that the proposed deep learning-based solution can be
considered state-of-the-art for Multi-Image Super-Resolution for remote sensing
applications
Deep learning based single image super-resolution : a survey
Single image super-resolution has attracted increasing attention and has a wide range of applications in satellite imaging, medical imaging, computer vision, security surveillance imaging, remote sensing, objection detection, and recognition. Recently, deep learning techniques have emerged and blossomed, producing “the state-of-the-art” in many domains. Due to their capability in feature extraction and mapping, it is very helpful to predict high-frequency details lost in low-resolution images. In this paper, we give an overview of recent advances in deep learning-based models and methods that have been applied to single image super-resolution tasks. We also summarize, compare and discuss various models from the past and present for comprehensive understanding and finally provide open problems and possible directions for future research
Guided Depth Super-Resolution by Deep Anisotropic Diffusion
Performing super-resolution of a depth image using the guidance from an RGB
image is a problem that concerns several fields, such as robotics, medical
imaging, and remote sensing. While deep learning methods have achieved good
results in this problem, recent work highlighted the value of combining modern
methods with more formal frameworks. In this work, we propose a novel approach
which combines guided anisotropic diffusion with a deep convolutional network
and advances the state of the art for guided depth super-resolution. The edge
transferring/enhancing properties of the diffusion are boosted by the
contextual reasoning capabilities of modern networks, and a strict adjustment
step guarantees perfect adherence to the source image. We achieve unprecedented
results in three commonly used benchmarks for guided depth super-resolution.
The performance gain compared to other methods is the largest at larger scales,
such as x32 scaling. Code for the proposed method will be made available to
promote reproducibility of our results
Super-resolution land cover mapping by deep learning
Super-resolution mapping (SRM) is a technique to estimate a fine spatial resolution land cover map from coarse spatial resolution fractional proportion images. SRM is often based explicitly on the use of a spatial pattern model that represents the land cover mosaic at the fine spatial resolution. Recently developed deep learning methods have considerable potential as an alternative approach for SRM, based on learning the spatial pattern of land cover from existing fine resolution data such as land cover maps. This letter proposes a deep learning-based SRM algorithm (DeepSRM). A deep convolutional neural network was first trained to estimate a fine resolution indicator image for each class from the coarse resolution fractional image, and all indicator maps were then combined to create the final fine resolution land cover map based on the maximal value strategy. The results of an experiment undertaken with simulated images show that DeepSRM was superior to conventional hard classification and a suite of popular SRM algorithms, yielding the most accurate land cover representation. Consequently, methods such as DeepSRM may help exploit the potential of remote sensing as a source of accurate land cover information
SRDA-Net: Super-Resolution Domain Adaptation Networks for Semantic Segmentation
Recently, Unsupervised Domain Adaptation was proposed to address the domain
shift problem in semantic segmentation task, but it may perform poor when
source and target domains belong to different resolutions. In this work, we
design a novel end-to-end semantic segmentation network, Super-Resolution
Domain Adaptation Network (SRDA-Net), which could simultaneously complete
super-resolution and domain adaptation. Such characteristics exactly meet the
requirement of semantic segmentation for remote sensing images which usually
involve various resolutions. Generally, SRDA-Net includes three deep neural
networks: a Super-Resolution and Segmentation (SRS) model focuses on recovering
high-resolution image and predicting segmentation map; a pixel-level domain
classifier (PDC) tries to distinguish the images from which domains; and
output-space domain classifier (ODC) discriminates pixel label distributions
from which domains. PDC and ODC are considered as the discriminators, and SRS
is treated as the generator. By the adversarial learning, SRS tries to align
the source with target domains on pixel-level visual appearance and
output-space. Experiments are conducted on the two remote sensing datasets with
different resolutions. SRDA-Net performs favorably against the state-of-the-art
methods in terms of accuracy and visual quality. Code and models are available
at https://github.com/tangzhenjie/SRDA-Net
Repeat multiview panchromatic super-resolution restoration using the UCL MAGiGAN system
High spatial resolution imaging data is always considered desirable in the field of remote sensing, particularly Earth observation. However, given the physical constraints of the imaging instruments themselves, one needs to be able to trade-off spatial resolution against launch mass as well as telecommunications bandwidth for transmitting data back to the Earth. In this paper, we present a newly developed super-resolution restoration system, called MAGiGAN, based on our original GPT-SRR system combined with deep learning image networks to be able to restore up to 4x higher resolution enhancement using multi-angle repeat images as input
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