scholarly journals A Multi-Scale Mapping Approach Based on a Deep Learning CNN Model for Reconstructing High-Resolution Urban DEMs

Water ◽  
2020 ◽  
Vol 12 (5) ◽  
pp. 1369
Author(s):  
Ling Jiang ◽  
Yang Hu ◽  
Xilin Xia ◽  
Qiuhua Liang ◽  
Andrea Soltoggio ◽  
...  
Keyword(s):  
High Resolution ◽  
Accuracy Assessment ◽  
Cost Effective ◽  
Reconstruction Method ◽  
Low Resolution ◽  
Multi Scale ◽  
Mapping Approach ◽  
Assessment Approach ◽  
Urban Topography ◽  
Complex Features

The scarcity of high-resolution urban digital elevation model (DEM) datasets, particularly in certain developing countries, has posed a challenge for many water-related applications such as flood risk management. A solution to address this is to develop effective approaches to reconstruct high-resolution DEMs from their low-resolution equivalents that are more widely available. However, the current high-resolution DEM reconstruction approaches mainly focus on natural topography. Few attempts have been made for urban topography, which is typically an integration of complex artificial and natural features. This study proposed a novel multi-scale mapping approach based on convolutional neural network (CNN) to deal with the complex features of urban topography and to reconstruct high-resolution urban DEMs. The proposed multi-scale CNN model was firstly trained using urban DEMs that contained topographic features at different resolutions, and then used to reconstruct the urban DEM at a specified (high) resolution from a low-resolution equivalent. A two-level accuracy assessment approach was also designed to evaluate the performance of the proposed urban DEM reconstruction method, in terms of numerical accuracy and morphological accuracy. The proposed DEM reconstruction approach was applied to a 121 km2 urbanized area in London, United Kingdom. Compared with other commonly used methods, the current CNN-based approach produced superior results, providing a cost-effective innovative method to acquire high-resolution DEMs in other data-scarce regions.

scholarly journals Super-resolution reconstruction of seismic section image via multi-scale convolution neural network

2021 ◽  
Vol 303 ◽  
pp. 01058
Author(s):  
Meng-Di Deng ◽  
Rui-Sheng Jia ◽  
Hong-Mei Sun ◽  
Xing-Li Zhang
Keyword(s):  
Neural Network ◽  
High Resolution ◽  
Convolutional Neural Network ◽  
Super Resolution ◽  
Image Features ◽  
Image Feature ◽  
Reconstruction Method ◽  
Low Resolution ◽  
Seismic Section ◽  
Multi Scale

The resolution of seismic section images can directly affect the subsequent interpretation of seismic data. In order to improve the spatial resolution of low-resolution seismic section images, a super-resolution reconstruction method based on multi-scale convolution is proposed. This method designs a multi-scale convolutional neural network to learn high-low resolution image feature pairs, and realizes mapping learning from low-resolution seismic section images to high-resolution seismic section images. This multi-scale convolutional neural network model consists of four convolutional layers and a sub-pixel convolutional layer. Convolution operations are used to learn abundant seismic section image features, and sub-pixel convolution layer is used to reconstruct high-resolution seismic section image. The experimental results show that the proposed method is superior to the comparison method in peak signal-to-noise ratio (PSNR) and structural similarity (SSIM). In the total training time and reconstruction time, our method is about 22% less than the FSRCNN method and about 18% less than the ESPCN method.

Deep neural networks to downscale ocean climate models

2020 ◽  
Author(s):  
Marie Déchelle-Marquet ◽  
Marina Levy ◽  
Patrick Gallinari ◽  
Michel Crepon ◽  
Sylvie Thiria
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
High Resolution ◽  
Deep Neural Networks ◽  
Climate Models ◽  
Ocean Currents ◽  
Sea Surface ◽  
Low Resolution ◽  
Ocean Climate ◽  
Multi Scale

<p>Ocean currents are a major source of impact on climate variability, through the heat transport they induce for instance. Ocean climate models have quite low resolution of about 50 km. Several dynamical processes such as instabilities and filaments which have a scale of 1km have a strong influence on the ocean state. We propose to observe and model these fine scale effects by a combination of satellite high resolution SST observations (1km resolution, daily observations) and mesoscale resolution altimetry observations (10km resolution, weekly observations) with deep neural networks. Whereas the downscaling of climate models has been commonly addressed with assimilation approaches, in the last few years neural networks emerged as powerful multi-scale analysis method. Besides, the large amount of available oceanic data makes attractive the use of deep learning to bridge the gap between scales variability.</p><p>This study aims at reconstructing the multi-scale variability of oceanic fields, based on the high resolution NATL60 model of ocean observations at different spatial resolutions: low-resolution sea surface height (SSH) and high resolution SST. As the link between residual neural networks and dynamical systems has recently been established, such a network is trained in a supervised way to reconstruct the high variability of SSH and ocean currents at submesoscale (a few kilometers). To ensure the conservation of physical aspects in the model outputs, physical knowledge is incorporated into the deep learning models training. Different validation methods are investigated and the model outputs are tested with regards to their physical plausibility. The method performance is discussed and compared to other baselines (namely convolutional neural network). The generalization of the proposed method on different ocean variables such as sea surface chlorophyll or sea surface salinity is also examined.</p>

A MULTI-RESOLUTION RECONSTRUCTION METHOD FOR NATURAL LINEAR FEATURES BASED ON RESTRICTED FRACTAL INTERPOLATION

Fractals ◽  
2017 ◽  
Vol 25 (02) ◽  
pp. 1750012
Author(s):  
BAO-DE JIANG ◽  
LIN ZHOU ◽  
LIANG WU ◽  
ZHONG XIE
Keyword(s):  
High Resolution ◽  
Reconstruction Method ◽  
Mathematical Function ◽  
Fractal Interpolation ◽  
Low Resolution ◽  
Linear Features ◽  
Fractal Interpolation Function ◽  
And Control ◽  
The Relationship ◽  
Geographical Characteristics

Multi-resolution representation has wide application demands in the field of Geographic Information System (GIS). The existing fractal interpolation methods can implement the reconstruction of natural linear features from low resolution to high resolution, but they are unable to quantify the relationship between the number of fractal iterations and the resolution, and the fractal interpolation results cannot maintain the geographical characteristics of natural linear features. This study presents a multi-resolution reconstruction method for natural linear features in GIS based on the restricted fractal interpolation. First, the geographical characteristics of natural linear features are extracted to restrict the parameters of fractal interpolation function and control the process of fractal interpolation. Second, a mathematical function is deduced for identifying the relationship between the number of fractal iterations and the resolution. The experiment results demonstrate that this study can reconstruct various preset high-resolution linear features from the low-resolution linear data while the geographical characteristics and random fractal characteristics of natural linear features are kept well.

scholarly journals Research on Super-resolution Reconstruction Method of Lidar 3D Range Profile

2021 ◽  
Author(s):  
Jianfeng Sun ◽  
Di Liu ◽  
Daoran Gong ◽  
Le Ma ◽  
Xin Zhang ◽  
...  
Keyword(s):  
High Resolution ◽  
Low Cost ◽  
Super Resolution ◽  
Reconstruction Method ◽  
Range Image ◽  
Low Resolution ◽  
Range Profile ◽  
Markov Random ◽  
Guidance Algorithm ◽  
Regional Similarity

At present, how to use low-cost and superior algorithms to obtain high-resolution 3D range image is the focus of lidar research. In this letter, the low-resolution Gm-APD lidar is combined with the high-resolution ICCD lidar to obtain the registered low-resolution range image and high-resolution intensity image. This letter proposes an improved image guidance algorithm. The algorithm uses a Markov random field model to define a global energy function. This function combines the distance fidelity term and the regularization term to obtain a high-resolution 3D range image by solving the optimization model. The experimental results show that compared with the traditional algorithms, the algorithm improves the resolution of the range images, the edge of the reconstructed image is sharper than the regional similarity guidance algorithm, and the image quality evaluation index has the better value.

scholarly journals APPLICATIONS OF MEDIUM C-BAND AND HIGH RESOLUTION X-BAND MULTITEMPORAL INTERFEROMETRY IN LANDSLIDE INVESTIGATIONS

2015 ◽  
Vol XL-1-W5 ◽  
pp. 737-743
Author(s):  
J. Wasowski ◽  
F. Bovenga ◽  
R. Nutricato ◽  
D. O. Nitti ◽  
M. T. Chiaradia
Keyword(s):  
High Resolution ◽  
Urban Areas ◽  
Regional Scale ◽  
Cost Effective ◽  
Engineering Structures ◽  
High Resolution Data ◽  
Multi Scale ◽  
Radar Images ◽  
X Band ◽  
Multi Temporal

With the increasing quantity and quality of the imagery available from a growing number of SAR satellites and the improved processing algorithms, multi-temporal interferometry (MTI) is expected to be commonly applied in landslide studies. MTI can now provide long-term (years), regular (weekly-monthly), precise (mm) measurements of ground displacements over large areas (thousands of km2), at medium (~20 m) to high (up to 1-3 m) spatial resolutions, combined with the possibility of multi-scale (regional to local) investigations, using the same series of radar images. We focus on the benefits as well as challenges of multisensor and multi-scale investigations by discussing MTI results regarding two landslide prone regions with distinctly different topographic, climatic and vegetation conditions (mountains in Central Albania and Southern Gansu, China), for which C-band (ERS or ENVISAT) and X-band COSMO-SkyMed (CSK) imagery was available (all in Stripmap descending mode). In both cases X-band MTI outperformed C-band MTI by providing more valuable information for the regional to local scale detection of slope deformations and landslide hazard assessment. This is related to the better spatial-temporal resolutions and more suitable incidence angles (40°-30° versus 23°) of CSK data While the use of medium resolution imagery may be appropriate and more cost-effective in reconnaissance or regional scale investigations, high resolution data could be preferentially exploited when focusing on urbanized landslides or potentially unstable slopes in urban/peri-urban areas, and slopes traversed by lifelines and other engineering structures.

scholarly journals Compressed Sensing, Pseudodictionary-Based, Superresolution Reconstruction

2016 ◽  
Vol 2016 ◽  
pp. 1-9 ◽  
Author(s):  
Chun-mei Li ◽  
Ka-zhong Deng ◽  
Jiu-yun Sun ◽  
Hui Wang
Keyword(s):  
High Resolution ◽  
Critical Factor ◽  
Image Resolution ◽  
Reconstruction Method ◽  
Low Resolution ◽  
Resolution Image ◽  
Single Frame ◽  
Sparse Coefficient ◽  
Quantitative Indicators ◽  
Better Than

The spatial resolution of digital images is the critical factor that affects photogrammetry precision. Single-frame, superresolution, image reconstruction is a typical underdetermined, inverse problem. To solve this type of problem, a compressive, sensing, pseudodictionary-based, superresolution reconstruction method is proposed in this study. The proposed method achieves pseudodictionary learning with an available low-resolution image and uses theK-SVD algorithm, which is based on the sparse characteristics of the digital image. Then, the sparse representation coefficient of the low-resolution image is obtained by solving the norm ofl0minimization problem, and the sparse coefficient and high-resolution pseudodictionary are used to reconstruct image tiles with high resolution. Finally, single-frame-image superresolution reconstruction is achieved. The proposed method is applied to photogrammetric images, and the experimental results indicate that the proposed method effectively increase image resolution, increase image information content, and achieve superresolution reconstruction. The reconstructed results are better than those obtained from traditional interpolation methods in aspect of visual effects and quantitative indicators.

HIGH-RESOLUTION SPECTRAL RECONSTRUCTION: METHOD AND APPLICATIONS FOR CONTAMINATION MEASUREMENT, DIGITAL SAMPLING, AND CONTINUITY ASSESSMENT

2020 ◽  
Author(s):  
Christopher Michael Jones ◽  
◽  
Yngve B Johansen ◽  
Artur Kotwicki ◽  
Cameron Rekully ◽  
...  
Keyword(s):  
High Resolution ◽  
Reconstruction Method ◽  
Digital Sampling ◽  
Spectral Reconstruction

scholarly journals Assessing Riyadh’s Urban Change Utilizing High-Resolution Imagery

Land ◽  
2019 ◽  
Vol 8 (12) ◽  
pp. 193
Author(s):  
Ali Alghamdi ◽  
Anthony R. Cummings
Keyword(s):  
Maximum Likelihood ◽  
High Resolution ◽  
Accuracy Assessment ◽  
Middle Eastern ◽  
Object Oriented ◽  
Training Data ◽  
High Resolution Imagery ◽  
Significant Research ◽  
Desert Areas ◽  
Spot 5

The implications of change on local processes have attracted significant research interest in recent times. In urban settings, green spaces and forests have attracted much attention. Here, we present an assessment of change within the predominantly desert Middle Eastern city of Riyadh, an understudied setting. We utilized high-resolution SPOT 5 data and two classification techniques—maximum likelihood classification and object-oriented classification—to study the changes in Riyadh between 2004 and 2014. Imagery classification was completed with training data obtained from the SPOT 5 dataset, and an accuracy assessment was completed through a combination of field surveys and an application developed in ESRI Survey 123 tool. The Survey 123 tool allowed residents of Riyadh to present their views on land cover for the 2004 and 2014 imagery. Our analysis showed that soil or ‘desert’ areas were converted to roads and buildings to accommodate for Riyadh’s rapidly growing population. The object-oriented classifier provided higher overall accuracy than the maximum likelihood classifier (74.71% and 73.79% vs. 92.36% and 90.77% for 2004 and 2014). Our work provides insights into the changes within a desert environment and establishes a foundation for understanding change in this understudied setting.

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