Quantitative integration of hydrogeophysical data: Conditional geostatistical simulation for characterizing heterogeneous alluvial aquifers

Geophysics ◽  
2005 ◽  
Vol 70 (3) ◽  
pp. H1-H10 ◽  
Author(s):  
Jens Tronicke ◽  
Klaus Holliger
Keyword(s):  
High Resolution ◽  
Spatial Information ◽  
Realistic Model ◽  
Geostatistical Simulation ◽  
Simulation Approach ◽  
Scale Invariant ◽  
Porosity Distribution ◽  
Alluvial Aquifers ◽  
Porosity Logs ◽  
Quantitative Integration

High-resolution geophysical parameter information, as it can be provided, for example, by crosshole georadar and seismic tomography, has proven to provide useful spatial information to complement traditional hydrological methods such as core analyses, logging techniques, and tracer or pumping tests. Quantitative integration of these diverse database components is one of the major challenges in the field of high-resolution hydrogeophysics because of their different scales of measurement and the usually weak petrophysical relations among the measurements. In this study, we systematically explore the usefulness of a conditional stochastic simulation approach based on simulated annealing for this purpose. First, we generate a realistic model of an alluvial aquifer consisting of a 2D scale-invariant porosity field. On the basis of this model, we generate synthetic neutron porosity logs and crosshole georadar tomographic surveys. We then use the proposed geostatistical simulation approach to integrate this hydrogeophysical database. The effectiveness of this approach to characterize the detailed porosity distribution in heterogeneous alluvial aquifers is assessed by comparing the results for a variety of simulated porosity fields that differ fundamentally in terms of their conditioning information. Our results indicate this approach has the potential to allow for a realistic hydrogeophysical characterization in the submeter range of the porosity distribution in heterogeneous alluvial aquifers.

scholarly journals HRCNet: High-Resolution Context Extraction Network for Semantic Segmentation of Remote Sensing Images

2020 ◽  
Vol 13 (1) ◽  
pp. 71
Author(s):  
Zhiyong Xu ◽  
Weicun Zhang ◽  
Tianxiang Zhang ◽  
Jiangyun Li
Keyword(s):  
Remote Sensing ◽  
Feature Extraction ◽  
High Resolution ◽  
Spatial Information ◽  
Semantic Segmentation ◽  
Context Information ◽  
Remote Sensing Images ◽  
Global Context ◽  
Boundary Information ◽  
Extraction Stage

Semantic segmentation is a significant method in remote sensing image (RSIs) processing and has been widely used in various applications. Conventional convolutional neural network (CNN)-based semantic segmentation methods are likely to lose the spatial information in the feature extraction stage and usually pay little attention to global context information. Moreover, the imbalance of category scale and uncertain boundary information meanwhile exists in RSIs, which also brings a challenging problem to the semantic segmentation task. To overcome these problems, a high-resolution context extraction network (HRCNet) based on a high-resolution network (HRNet) is proposed in this paper. In this approach, the HRNet structure is adopted to keep the spatial information. Moreover, the light-weight dual attention (LDA) module is designed to obtain global context information in the feature extraction stage and the feature enhancement feature pyramid (FEFP) structure is promoted and employed to fuse the contextual information of different scales. In addition, to achieve the boundary information, we design the boundary aware (BA) module combined with the boundary aware loss (BAloss) function. The experimental results evaluated on Potsdam and Vaihingen datasets show that the proposed approach can significantly improve the boundary and segmentation performance up to 92.0% and 92.3% on overall accuracy scores, respectively. As a consequence, it is envisaged that the proposed HRCNet model will be an advantage in remote sensing images segmentation.

scholarly journals Multiscale Semantic Feature Optimization and Fusion Network for Building Extraction Using High-Resolution Aerial Images and LiDAR Data

2021 ◽  
Vol 13 (13) ◽  
pp. 2473
Author(s):  
Qinglie Yuan ◽  
Helmi Zulhaidi Mohd Shafri ◽  
Aidi Hizami Alias ◽  
Shaiful Jahari Hashim
Keyword(s):  
High Resolution ◽  
Large Scale ◽  
Spatial Information ◽  
Feature Fusion ◽  
Aerial Images ◽  
Semantic Gap ◽  
Superior Performance ◽  
Lidar Data ◽  
Building Extraction ◽  
Hierarchical Features

Automatic building extraction has been applied in many domains. It is also a challenging problem because of the complex scenes and multiscale. Deep learning algorithms, especially fully convolutional neural networks (FCNs), have shown robust feature extraction ability than traditional remote sensing data processing methods. However, hierarchical features from encoders with a fixed receptive field perform weak ability to obtain global semantic information. Local features in multiscale subregions cannot construct contextual interdependence and correlation, especially for large-scale building areas, which probably causes fragmentary extraction results due to intra-class feature variability. In addition, low-level features have accurate and fine-grained spatial information for tiny building structures but lack refinement and selection, and the semantic gap of across-level features is not conducive to feature fusion. To address the above problems, this paper proposes an FCN framework based on the residual network and provides the training pattern for multi-modal data combining the advantage of high-resolution aerial images and LiDAR data for building extraction. Two novel modules have been proposed for the optimization and integration of multiscale and across-level features. In particular, a multiscale context optimization module is designed to adaptively generate the feature representations for different subregions and effectively aggregate global context. A semantic guided spatial attention mechanism is introduced to refine shallow features and alleviate the semantic gap. Finally, hierarchical features are fused via the feature pyramid network. Compared with other state-of-the-art methods, experimental results demonstrate superior performance with 93.19 IoU, 97.56 OA on WHU datasets and 94.72 IoU, 97.84 OA on the Boston dataset, which shows that the proposed network can improve accuracy and achieve better performance for building extraction.

scholarly journals High resolution remote sensing image segmentation based on graph theory and fractal net evolution approach

2015 ◽  
Vol XL-7/W4 ◽  
pp. 197-201 ◽  
Author(s):  
Y. Yang ◽  
H. T. Li ◽  
Y. S. Han ◽  
H. Y. Gu
Keyword(s):  
Remote Sensing ◽  
Image Segmentation ◽  
Graph Theory ◽  
High Resolution ◽  
Spatial Information ◽  
Remote Sensing Image ◽  
Sensing Applications ◽  
Initial Object ◽  
Object Layer ◽  
Evolution Approach

Image segmentation is the foundation of further object-oriented image analysis, understanding and recognition. It is one of the key technologies in high resolution remote sensing applications. In this paper, a new fast image segmentation algorithm for high resolution remote sensing imagery is proposed, which is based on graph theory and fractal net evolution approach (FNEA). Firstly, an image is modelled as a weighted undirected graph, where nodes correspond to pixels, and edges connect adjacent pixels. An initial object layer can be obtained efficiently from graph-based segmentation, which runs in time nearly linear in the number of image pixels. Then FNEA starts with the initial object layer and a pairwise merge of its neighbour object with the aim to minimize the resulting summed heterogeneity. Furthermore, according to the character of different features in high resolution remote sensing image, three different merging criterions for image objects based on spectral and spatial information are adopted. Finally, compared with the commercial remote sensing software eCognition, the experimental results demonstrate that the efficiency of the algorithm has significantly improved, and the result can maintain good feature boundaries.

scholarly journals An Image Registration Method Using Deep Residual Network Features for Multisource High-Resolution Remote Sensing Images

2021 ◽  
Vol 13 (17) ◽  
pp. 3425
Author(s):  
Xin Zhao ◽  
Hui Li ◽  
Ping Wang ◽  
Linhai Jing
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Image Registration ◽  
Feature Descriptor ◽  
Remote Sensing Images ◽  
Residual Network ◽  
Scale Invariant ◽  
Registration Method ◽  
Sensing Applications ◽  
Feature Based

Accurate registration for multisource high-resolution remote sensing images is an essential step for various remote sensing applications. Due to the complexity of the feature and texture information of high-resolution remote sensing images, especially for images covering earthquake disasters, feature-based image registration methods need a more helpful feature descriptor to improve the accuracy. However, traditional image registration methods that only use local features at low levels have difficulty representing the features of the matching points. To improve the accuracy of matching features for multisource high-resolution remote sensing images, an image registration method based on a deep residual network (ResNet) and scale-invariant feature transform (SIFT) was proposed. It used the fusion of SIFT features and ResNet features on the basis of the traditional algorithm to achieve image registration. The proposed method consists of two parts: model construction and training and image registration using a combination of SIFT and ResNet34 features. First, a registration sample set constructed from high-resolution satellite remote sensing images was used to fine-tune the network to obtain the ResNet model. Then, for the image to be registered, the Shi_Tomas algorithm and the combination of SIFT and ResNet features were used for feature extraction to complete the image registration. Considering the difference in image sizes and scenes, five pairs of images were used to conduct experiments to verify the effectiveness of the method in different practical applications. The experimental results showed that the proposed method can achieve higher accuracies and more tie points than traditional feature-based methods.

A 3D Optimized Frequency–Wavenumber (FK), Time–Space Optimized Symplectic (TSOS) Hybrid Method for Teleseismic Wave Modeling

2021 ◽  
Author(s):  
Weijuan Meng ◽  
Dinghui Yang ◽  
Xingpeng Dong ◽  
Jian Ma
Keyword(s):  
High Resolution ◽  
Seismic Wave ◽  
Hybrid Method ◽  
High Efficiency ◽  
Reference Model ◽  
3D Models ◽  
Realistic Model ◽  
High Accuracy ◽  
Memory Requirement ◽  
Time Space

ABSTRACT Although teleseismic waveform tomography can provide high-resolution images of the deep mantle, it is still unrealistic to numerically simulate the whole domain of seismic wave propagation due to the huge amount of computation. In this article, we develop a new three-dimensional hybrid method to address this issue, which couples the modified frequency–wavenumber (FK) method with the 3D time–space optimized symplectic (TSOS) method. First, the FK method, which is used to calculate the semianalytical incident wavefields in the layered reference model, is modified to compute the wavefields efficiently with a significantly low-memory requirement. Second, 3D TSOS method is developed to model the seismic wave propagating in the local 3D heterogeneous domain. The low memory requirement of the modified FK method and the high accuracy of the TSOS method make it feasible to obtain highly accurate synthetic seismograms efficiently. A crust–upper mantle model for P-, SV-, and SH-wave incidences is calculated to benchmark the accuracy and efficiency of the 3D optimized FK-TSOS method. Numerical experiments for 3D models with heterogeneities, undulated discontinuous interfaces, and realistic model in eastern Tibet, illustrate the capability of hybrid method to accurately capture the scattered waves caused by heterogeneities in 3D medium. The 3D optimized FK-TSOS method developed shows low-memory requirement, high accuracy, and high efficiency, which makes it be a promising forward method to further apply to high-resolution mantle structure images beneath seismic array.

scholarly journals Mapping Heat Stress Vulnerability and Risk Assessment at the Neighborhood Scale to Drive Urban Adaptation Planning

2020 ◽  
Vol 12 (3) ◽  
pp. 1056 ◽  
Author(s):  
Denis Maragno ◽  
Michele Dalla Fontana ◽  
Francesco Musco
Keyword(s):  
Risk Assessment ◽  
Heat Stress ◽  
High Resolution ◽  
Planning Process ◽  
Spatial Information ◽  
Local Level ◽  
Adaptation Planning ◽  
Stress Vulnerability ◽  
Neighborhood Scale ◽  
The City

Climate change is one of the most complex issues of the 21st century, and even though there is general consensus about the urgency of taking action at the city level, the planning and implementation of adaptation measures is advancing slowly. The lack of data and information to support the planning process is often mentioned as a factor hampering the adaptation processes in cities. In this paper, we developed and tested a methodology for heat stress vulnerability and risk assessment at the neighborhood scale to support designers, planners, and decision makers in developing and implementing adaptation strategies and measures at the local level. The methodology combines high-resolution spatial information and crowdsourcing geospatial data to develop sensitivity, adaptive capacity, vulnerability, exposure, and risk indicators. The methodology is then tested on the urban fabric of the city of Padova, Italy. Our results show that different vulnerability and risk values correspond to different typologies of urban areas. Furthermore, the possibility of combining high-resolution information provided by the indicators and land use categories is of great importance to support the adaptation planning process. We also argue that the methodology is flexible enough to be applied in different contexts.

Quantitative integration of sedimentological core descriptions and petrophysical data using high-resolution XRF core scans

2019 ◽  
Vol 110 ◽  
pp. 450-462 ◽  
Author(s):  
S. Henares ◽  
M.E. Donselaar ◽  
M.R. Bloemsma ◽  
R. Tjallingii ◽  
B. De Wijn ◽  
...  
Keyword(s):  
High Resolution ◽  
Petrophysical Data ◽  
Quantitative Integration ◽  
Core Descriptions

scholarly journals Hyperspectral Super-Resolution Technique Using Histogram Matching and Endmember Optimization

2019 ◽  
Vol 9 (20) ◽  
pp. 4444
Author(s):  
Byunghyun Kim ◽  
Soojin Cho
Keyword(s):  
High Resolution ◽  
Spatial Information ◽  
Super Resolution ◽  
Spatial Mismatch ◽  
Low Resolution ◽  
Practical Applications ◽  
Histogram Matching ◽  
Shear Transformation ◽  
Entire Dataset ◽  
Rgb Image

In most hyperspectral super-resolution (HSR) methods, which are techniques used to improve the resolution of hyperspectral images (HSIs), the HSI and the target RGB image are assumed to have identical fields of view. However, because implementing these identical fields of view is difficult in practical applications, in this paper, we propose a HSR method that is applicable when an HSI and a target RGB image have different spatial information. The proposed HSR method first creates a low-resolution RGB image from a given HSI. Next, a histogram matching is performed on a high-resolution RGB image and a low-resolution RGB image obtained from an HSI. Finally, the proposed method optimizes endmember abundance of the high-resolution HSI towards the histogram-matched high-resolution RGB image. The entire procedure is evaluated using an open HSI dataset, the Harvard dataset, by adding spatial mismatch to the dataset. The spatial mismatch is implemented by shear transformation and cutting off the upper and left sides of the target RGB image. The proposed method achieved a lower error rate across the entire dataset, confirming its capability for super-resolution using images that have different fields of view.

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