scholarly journals Large scale flow visualization and anemometry applied to lab-on-a-chip models of porous media

Lab on a Chip ◽  
2016 ◽  
Vol 16 (15) ◽  
pp. 2851-2859 ◽  
Author(s):  
Johan Paiola ◽  
Harold Auradou ◽  
Hugues Bodiguel
Keyword(s):  
Porous Media ◽  
High Resolution ◽  
Velocity Field ◽  
Flow Visualization ◽  
Experimental Technique ◽  
Large Scale ◽  
Lab On A Chip ◽  
Large Scale Flow ◽  
Very High

The following is a report on an experimental technique that allows one to quantify and map the velocity field with very high resolution and simple equipment in large 2D devices.

scholarly journals Melt Pond Retrieval Based on the LinearPolar Algorithm Using Landsat Data

2021 ◽  
Vol 13 (22) ◽  
pp. 4674
Author(s):  
Yuqing Qin ◽  
Jie Su ◽  
Mingfeng Wang
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Relative Error ◽  
Large Scale ◽  
The Arctic ◽  
Landsat Data ◽  
Melt Pond ◽  
Melt Ponds ◽  
Very High ◽  
Sentinel 2

The formation and distribution of melt ponds have an important influence on the Arctic climate. Therefore, it is necessary to obtain more accurate information on melt ponds on Arctic sea ice by remote sensing. The present large-scale melt pond products, especially the melt pond fraction (MPF), still require verification, and using very high resolution optical satellite remote sensing data is a good way to verify the large-scale retrieval of MPF products. Unlike most MPF algorithms using very high resolution data, the LinearPolar algorithm using Sentinel-2 data considers the albedo of melt ponds unfixed. In this paper, by selecting the best band combination, we applied this algorithm to Landsat 8 (L8) data. Moreover, Sentinel-2 data, as well as support vector machine (SVM) and iterative self-organizing data analysis technique (ISODATA) algorithms, are used as the comparison and verification data. The results show that the recognition accuracy of the LinearPolar algorithm for melt ponds is higher than that of previous algorithms. The overall accuracy and kappa coefficient results achieved by using the LinearPolar algorithm with L8 and Sentinel-2A (S2), the SVM algorithm, and the ISODATA algorithm are 95.38% and 0.88, 94.73% and 0.86, and 92.40%and 0.80, respectively, which are much higher than those of principal component analysis (PCA) and Markus algorithms. The mean MPF (10.0%) obtained from 80 cases from L8 data based on the LinearPolar algorithm is much closer to Sentinel-2 (10.9%) than the Markus (5.0%) and PCA algorithms (4.2%), with a mean MPF difference of only 0.9%, and the correlation coefficients of the two MPFs are as high as 0.95. The overall relative error of the LinearPolar algorithm is 53.5% and 46.4% lower than that of the Markus and PCA algorithms, respectively, and the root mean square error (RMSE) is 30.9% and 27.4% lower than that of the Markus and PCA algorithms, respectively. In the cases without obvious melt ponds, the relative error is reduced more than that of those with obvious melt ponds because the LinearPolar algorithm can identify 100% of dark melt ponds and relatively small melt ponds, and the latter contributes more to the reduction in the relative error of MPF retrieval. With a wider range and longer time series, the MPF from Landsat data are more efficient than those from Sentinel-2 for verifying large-scale MPF products or obtaining long-term monitoring of a fixed area.

scholarly journals High Resolution Satellite Images for Instantaneous Shoreline Extraction Using New Enhancement Algorithms

Geosciences ◽  
2019 ◽  
Vol 9 (3) ◽  
pp. 123 ◽  
Author(s):  
Donatella Dominici ◽  
Sara Zollini ◽  
Maria Alicandro ◽  
Francesca Della Torre ◽  
Paolo Buscema ◽  
...  
Keyword(s):  
High Resolution ◽  
Large Scale ◽  
Satellite Images ◽  
Human Life ◽  
Essential Element ◽  
Action Planning ◽  
High Resolution Satellite Images ◽  
High Resolution Images ◽  
Territorial Government ◽  
Very High

Knowledge of a territory is an essential element in any future planning action and in appropriate territorial and environmental requalification action planning. The current large-scale availability of satellite data, thanks to very high resolution images, provides professional users in the environmental, urban planning, engineering, and territorial government sectors, in general, with large amounts of useful data with which to monitor the territory and cultural heritage. Italy is experiencing environmental emergencies, and coastal erosion is one of the greatest threats, not only to the Italian heritage and economy, but also to human life. The aim of this paper is to find a rapid way of identifying the instantaneous shoreline. This possibility could help government institutions such as regions, civil protection, etc., to analyze large areas of land quickly. The focus is on instantaneous shoreline extraction in Ortona (CH, Italy), without considering tides, using WorldView-2 satellite images (50-cm resolution in panchromatic and 2 m in multispectral). In particular, the main purpose of this paper is to compare commercial software and ACM filters to test their effectiveness.

scholarly journals Context-Enabled Extraction of Large-Scale Urban Functional Zones from Very-High-Resolution Images: A Multiscale Segmentation Approach

2019 ◽  
Vol 11 (16) ◽  
pp. 1902 ◽  
Author(s):  
Shouji Du ◽  
Shihong Du ◽  
Bo Liu ◽  
Xiuyuan Zhang
Keyword(s):  
High Resolution ◽  
Large Scale ◽  
Satellite Images ◽  
Nearest Neighbor ◽  
Context Information ◽  
Functional Zone ◽  
High Resolution Satellite Images ◽  
Segmentation Approach ◽  
Functional Zones ◽  
Very High

Urban functional-zone (UFZ) analysis has been widely used in many applications, including urban environment evaluation, and urban planning and management. How to extract UFZs’ spatial units which delineates UFZs’ boundaries is fundamental to urban applications, but it is still unresolved. In this study, an automatic, context-enabled multiscale image segmentation method is proposed for extracting spatial units of UFZs from very-high-resolution satellite images. First, a window independent context feature is calculated to measure context information in the form of geographic nearest-neighbor distance from a pixel to different image classes. Second, a scale-adaptive approach is proposed to determine appropriate scales for each UFZ in terms of its context information and generate the initial UFZs. Finally, the graph cuts algorithm is improved to optimize the initial UFZs. Two datasets including WorldView-2 image in Beijing and GaoFen-2 image in Nanchang are used to evaluate the proposed method. The results indicate that the proposed method can generate better results from very-high-resolution satellite images than widely used approaches like image tiles and road blocks in representing UFZs. In addition, the proposed method outperforms existing methods in both segmentation quality and running time. Therefore, the proposed method appears to be promising and practical for segmenting large-scale UFZs.

scholarly journals High resolution modelling results of the wind flow over Canary Islands during the meteorological situation of the extratropical storm Delta (28–30 November 2005)

2008 ◽  
Vol 2 (1) ◽  
pp. 81-87 ◽  
Author(s):  
O. Jorba ◽  
C. Marrero ◽  
E. Cuevas ◽  
J. M. Baldasano
Keyword(s):  
High Resolution ◽  
Canary Islands ◽  
Large Scale ◽  
Static Stability ◽  
Horizontal Resolution ◽  
Wind Speeds ◽  
Warm Core ◽  
Average Wind ◽  
Significant Damage ◽  
Large Scale Flow

Abstract. On 28–29 November 2005 an extratropical storm affected the Canary Islands causing significant damage related to high average wind speeds and intense gusts over some islands of the archipelago. Delta was the twenty-sixth tropical or subtropical storm of the 2005 Atlantic hurricane season. It represents an unusual meteorological phenomenon for that region, and its impacts were underestimated by the different operational meteorological forecasts during the previous days of the arrival of the low near Canary Islands. The aim of this study is to reproduce the local effects of the flow that were observed over the Canary Islands during the travel of the Delta storm near the region using high-resolution mesoscale meteorological simulations. The Advanced Research Weather Research & Forecasting Model (WRF-ARW) is applied at 9, 3 and 1 km horizontal resolution using ECMWF forecasts as initial and boundary conditions. The high-resolution simulation will outline the main features that contributed to the high wind speeds observed in the archipelago. Variations in vertical static stability, vertical windshear and the intense synoptic winds of the southwestern part of Delta with a warm core at 850 hPa were the main characteristics that contributed to the development and amplification of intense gravity waves while the large-scale flow interacted with the complex topography of the islands.

scholarly journals Automatic 3-D Building Model Reconstruction from Very High Resolution Stereo Satellite Imagery

2019 ◽  
Vol 11 (14) ◽  
pp. 1660
Author(s):  
Partovi ◽  
Fraundorfer ◽  
Bahmanyar ◽  
Huang ◽  
Reinartz
Keyword(s):  
High Resolution ◽  
Satellite Imagery ◽  
Large Scale ◽  
Missing Values ◽  
Model Reconstruction ◽  
Large Area ◽  
Building Model ◽  
Building Models ◽  
D Model ◽  
Very High

Recent advances in the availability of very high-resolution (VHR) satellite data together withefficient data acquisition and large area coverage have led to an upward trend in their applicationsfor automatic 3-D building model reconstruction which require large-scale and frequent updates,such as disaster monitoring and urban management. Digital Surface Models (DSMs) generatedfrom stereo satellite imagery suffer from mismatches, missing values, or blunders, resulting inrough building shape representations. To handle 3-D building model reconstruction using suchlow-quality DSMs, we propose a novel automatic multistage hybrid method using DSMs togetherwith orthorectified panchromatic (PAN) and pansharpened data (PS) of multispectral (MS) satelliteimagery. The algorithm consists of multiple steps including building boundary extraction anddecomposition, image-based roof type classification, and initial roof parameter computation whichare prior knowledge for the 3-D model fitting step. To fit 3-D models to the normalized DSM(nDSM) and to select the best one, a parameter optimization method based on exhaustive searchis used sequentially in 2-D and 3-D. Finally, the neighboring building models in a building blockare intersected to reconstruct the 3-D model of connecting roofs. All corresponding experimentsare conducted on a dataset including four different areas of Munich city containing 208 buildingswith different degrees of complexity. The results are evaluated both qualitatively and quantitatively.According to the results, the proposed approach can reliably reconstruct 3-D building models, eventhe complex ones with several inner yards and multiple orientations. Furthermore, the proposedapproach provides a high level of automation by limiting the number of primitive roof types and byperforming automatic parameter initialization.

scholarly journals Very-high-resolution mapping of river-immersed topography by remote sensing

2008 ◽  
Vol 32 (4) ◽  
pp. 403-419 ◽  
Author(s):  
Denis Feurer ◽  
Jean-Stéphane Bailly ◽  
Christian Puech ◽  
Yann Le Coarer ◽  
Alain A. Viau
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Large Scale ◽  
Aerial Image ◽  
Depth Measurement ◽  
Very High Spatial Resolution ◽  
Remote Sensing Techniques ◽  
Logarithmic Relationship ◽  
Ground Penetrating ◽  
Very High

Remote sensing has been used to map river bathymetry for several decades. Non-contact methods are necessary in several cases: inaccessible rivers, large-scale depth mapping, very shallow rivers. The remote sensing techniques used for river bathymetry are reviewed. Frequently, these techniques have been developed for marine environment and have then been transposed to riverine environments. These techniques can be divided into two types: active remote sensing, such as ground penetrating radar and bathymetric lidar; or passive remote sensing, such as through-water photogrammetry and radiometric models. This last technique — which consists of finding a logarithmic relationship between river depth and image values — appears to be the most used. Fewer references exist for the other techniques, but lidar is an emerging technique. For each depth measurement method, we detail the physical principles and then a review of the results obtained in the field. This review shows a lack of data for very shallow rivers, where a very high spatial resolution is needed. Moreover, the cost related to aerial image acquisition is often huge. Hence we propose an application of two techniques, radiometric models and through-water photogrammetry, with very- high-resolution passive optical imagery, light platforms, and off-the-shelf cameras. We show that, in the case of the radiometric models, measurement is possible with a spatial filtering of about 1 m and a homogeneous river bottom. In contrast, with through-water photogrammetry, fine ground resolution and bottom textures are necessary.

scholarly journals Boundary-Aware Refined Network for Automatic Building Extraction in Very High-Resolution Urban Aerial Images

2021 ◽  
Vol 13 (4) ◽  
pp. 692
Author(s):  
Yuwei Jin ◽  
Wenbo Xu ◽  
Ce Zhang ◽  
Xin Luo ◽  
Haitao Jia
Keyword(s):  
High Resolution ◽  
Large Scale ◽  
Aerial Images ◽  
Data Sets ◽  
Building Extraction ◽  
Visual Interpretation ◽  
Urban Scenes ◽  
Multi Scale ◽  
Spatial Pyramid Pooling ◽  
Very High

Convolutional Neural Networks (CNNs), such as U-Net, have shown competitive performance in the automatic extraction of buildings from Very High-Resolution (VHR) aerial images. However, due to the unstable multi-scale context aggregation, the insufficient combination of multi-level features and the lack of consideration of the semantic boundary, most existing CNNs produce incomplete segmentation for large-scale buildings and result in predictions with huge uncertainty at building boundaries. This paper presents a novel network with a special boundary-aware loss embedded, called the Boundary-Aware Refined Network (BARNet), to address the gap above. The unique properties of the proposed BARNet are the gated-attention refined fusion unit, the denser atrous spatial pyramid pooling module, and the boundary-aware loss. The performance of the BARNet is tested on two popular data sets that include various urban scenes and diverse patterns of buildings. Experimental results demonstrate that the proposed method outperforms several state-of-the-art approaches in both visual interpretation and quantitative evaluations.

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