A roof-contour guided multi-side interpolation method for building texture-mapping using remote sensing resource

2015 ◽  
Author(s):  
Yiming Yan ◽  
Xi Chen ◽  
Fengjiao Gao ◽  
Ye Zhang ◽  
Yi Shen ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Interpolation Method ◽  
Texture Mapping

A robust interpolation method for constructing digital elevation models from remote sensing data

Geomorphology ◽  
2016 ◽  
Vol 268 ◽  
pp. 275-287 ◽  
Author(s):  
Chuanfa Chen ◽  
Fengying Liu ◽  
Yanyan Li ◽  
Changqing Yan ◽  
Guolin Liu
Keyword(s):  
Remote Sensing ◽  
Interpolation Method ◽  
Digital Elevation Models ◽  
Remote Sensing Data ◽  
Sensing Data ◽  
Digital Elevation

scholarly journals FAST MESH INTERPOLATION AND MESH DECOMPOSITION WITH APPLICATIONS

2012 ◽  
Vol 12 (01) ◽  
pp. 1250006
Author(s):  
SHUHUA LAI ◽  
FUHUA (FRANK) CHENG
Keyword(s):  
Iterative Process ◽  
Interpolation Method ◽  
Low Frequency ◽  
Texture Mapping ◽  
Construction Process ◽  
Mesh Decomposition ◽  
Decomposition Scheme ◽  
New Approaches ◽  
Control Mesh ◽  
The Given

A new approach for constructing a smooth subdivision surface to interpolate the vertices of an arbitrary mesh is presented. The construction process does require setting up neither any linear systems, nor any matrix computation, but is simply done by iteratively moving vertices of the given mesh locally until control mesh of the required interpolating surface is reached. The new interpolation method has the simplicity of a local method in effectively dealing with meshes of a large number of vertices. It also has the capability of a global method in faithfully resembling the shape of a given mesh. Furthermore, the new method is fast and does not require a fairing step in the construction process because the iterative process converges to a unique solution at an exponential rate. Another important result of this work is, with the new iterative process, each mesh (surface) can be decomposed into a sum of simpler meshes (surfaces) which carry high-and low-frequency information of the given model. This mesh decomposition scheme provides us with new approaches to some classic applications in computer graphics such as texture mapping, denoising/smoothing/sharpening, and morphing. These new approaches are demonstrated in this paper and test results are included.

scholarly journals EVALUATION DIGITAL ELEVATION MODEL GENERATED BY SYNTHETIC APERTURE RADAR DATA

2016 ◽  
Vol XLI-B1 ◽  
pp. 57-62
Author(s):  
H. B. Makineci ◽  
H. Karabörk
Keyword(s):  
Remote Sensing ◽  
Digital Elevation Model ◽  
Interpolation Method ◽  
Digital Elevation Models ◽  
European Space Agency ◽  
Mountain Area ◽  
Synthetic Aperture Radar Data ◽  
Radar Images ◽  
Digital Elevation ◽  
Elevation Model

Digital elevation model, showing the physical and topographical situation of the earth, is defined a tree-dimensional digital model obtained from the elevation of the surface by using of selected an appropriate interpolation method. DEMs are used in many areas such as management of natural resources, engineering and infrastructure projects, disaster and risk analysis, archaeology, security, aviation, forestry, energy, topographic mapping, landslide and flood analysis, Geographic Information Systems (GIS). Digital elevation models, which are the fundamental components of cartography, is calculated by many methods. Digital elevation models can be obtained terrestrial methods or data obtained by digitization of maps by processing the digital platform in general. Today, Digital elevation model data is generated by the processing of stereo optical satellite images, radar images (radargrammetry, interferometry) and lidar data using remote sensing and photogrammetric techniques with the help of improving technology. <br><br> One of the fundamental components of remote sensing radar technology is very advanced nowadays. In response to this progress it began to be used more frequently in various fields. Determining the shape of topography and creating digital elevation model comes the beginning topics of these areas. <br><br> It is aimed in this work , the differences of evaluation of quality between Sentinel-1A SAR image ,which is sent by European Space Agency ESA and Interferometry Wide Swath imaging mode and C band type , and DTED-2 (Digital Terrain Elevation Data) and application between them. The application includes RMS static method for detecting precision of data. Results show us to variance of points make a high decrease from mountain area to plane area.

scholarly journals SUPER RESOLUTION RECONSTRUCTION BASED ON ADAPTIVE DETAIL ENHANCEMENT FOR ZY-3 SATELLITE IMAGES

2016 ◽  
Vol III-7 ◽  
pp. 213-217 ◽  
Author(s):  
Hong Zhu ◽  
Weidong Song ◽  
Hai Tan ◽  
Jingxue Wang ◽  
Di Jia
Keyword(s):  
Remote Sensing ◽  
Large Scale ◽  
Satellite Images ◽  
Interpolation Method ◽  
Super Resolution ◽  
Bilateral Filter ◽  
Redundant Information ◽  
Evaluation Standard ◽  
Detail Enhancement ◽  
Image Details

Super-resolution reconstruction of sequence remote sensing image is a technology which handles multiple low-resolution satellite remote sensing images with complementary information and obtains one or more high resolution images. The cores of the technology are high precision matching between images and high detail information extraction and fusion. In this paper puts forward a new image super resolution model frame which can adaptive multi-scale enhance the details of reconstructed image. First, the sequence images were decomposed into a detail layer containing the detail information and a smooth layer containing the large scale edge information by bilateral filter. Then, a texture detail enhancement function was constructed to promote the magnitude of the medium and small details. Next, the non-redundant information of the super reconstruction was obtained by differential processing of the detail layer, and the initial super resolution construction result was achieved by interpolating fusion of non-redundant information and the smooth layer. At last, the final reconstruction image was acquired by executing a local optimization model on the initial constructed image. Experiments on ZY-3 satellite images of same phase and different phase show that the proposed method can both improve the information entropy and the image details evaluation standard comparing with the interpolation method, traditional TV algorithm and MAP algorithm, which indicate that our method can obviously highlight image details and contains more ground texture information. A large number of experiment results reveal that the proposed method is robust and universal for different kinds of ZY-3 satellite images.

An Automatic Interpolation Method of Grayvalued Images Using Texture Mapping

1989 ◽  
Author(s):  
Takeshi Agui ◽  
Masahiro Okawa ◽  
Masayuki Nakajima
Keyword(s):  
Interpolation Method ◽  
Texture Mapping

scholarly journals DROUGHT FORECASTING BASED ON MACHINE LEARNING OF REMOTE SENSING AND LONG-RANGE FORECAST DATA

2016 ◽  
Vol XLI-B8 ◽  
pp. 157-158
Author(s):  
J. Rhee ◽  
J. Im ◽  
S. Park
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
Long Range ◽  
Initial Conditions ◽  
Interpolation Method ◽  
Spline Interpolation ◽  
Standardized Precipitation Index ◽  
Learning Models ◽  
Drought Forecasting ◽  
Machine Learning Models

The reduction of drought impacts may be achieved through sustainable drought management and proactive measures against drought disaster. Accurate and timely provision of drought information is essential. In this study, drought forecasting models to provide high-resolution drought information based on drought indicators for ungauged areas were developed. The developed models predict drought indices of the 6-month Standardized Precipitation Index (SPI6) and the 6-month Standardized Precipitation Evapotranspiration Index (SPEI6). An interpolation method based on multiquadric spline interpolation method as well as three machine learning models were tested. Three machine learning models of Decision Tree, Random Forest, and Extremely Randomized Trees were tested to enhance the provision of drought initial conditions based on remote sensing data, since initial conditions is one of the most important factors for drought forecasting. Machine learning-based methods performed better than interpolation methods for both classification and regression, and the methods using climatology data outperformed the methods using long-range forecast. The model based on climatological data and the machine learning method outperformed overall.

scholarly journals ESTIMATION OF AIR TEMPERATURE USING REMOTE SENSING BASED ON THERMAL DIFFUSIVITY APPROACH

2010 ◽  
Vol 3 (-) ◽  
Author(s):  
M.Rokhis Khomarudin ◽  
Ahmad Bey ◽  
Idung Risdiyanto
Keyword(s):  
Remote Sensing ◽  
Thermal Diffusivity ◽  
Surface Temperature ◽  
Air Temperature ◽  
Spatial Information ◽  
Interpolation Method ◽  
Remote Sensing Data ◽  
Vegetation Growth ◽  
Noaa Avhrr ◽  
Climate Station

The measurement of air temperature usually used thermometer in the meteorology or climate station under Bureau of Meteorology and Geophysics. In Indonesia, there are some limitations in air temperature measurement and then they could not provide the spatial high resolution information. The measurement of air temperature is very important for analyzing the human comfort, photosynthesis, and vegetation growth which we need saome details spatial information. However, when data were sparse, the underlying assumptions about the variation among sampled points often differed and the choice of interpolation method and parameters then became critical. Often though data may be too sparse to use any of the interpolation methods, alternate ways to derive spatially representative values of air temperature need to researched. The data that could provide spatial information are remote sensing. The objective of this research is to estimate air temperature using remote sensing data (NOAA/AVHRR and LANDSAT/TM), based on thermal diffusivity approach. The steps of this research include the calibration of surface temperature, the determination of amplitude, and the estimation of air temperature. Based on this research, the best equation to calculate surface temperature from NOAA AVHRR is Ulivieri et al equation. This equation shows the higher correlation between surface temperatures from NOAA/AVHRR and the observation in the field than the other equation. Physically, this research could estimate air temperature from satellites data, but statistically, this research has not enough significancy to describe the field observation. Keywords: physical model, temperature, remote sensing.

scholarly journals Reconstruction of cloud geometry using a scanning cloud radar

2015 ◽  
Vol 8 (6) ◽  
pp. 2491-2508 ◽  
Author(s):  
F. Ewald ◽  
C. Winkler ◽  
T. Zinner
Keyword(s):  
Remote Sensing ◽  
Radiative Transfer ◽  
Interpolation Method ◽  
Three Dimensional ◽  
Convective Cloud ◽  
Cloud Microphysics ◽  
Reconstruction Method ◽  
Cloud Radar ◽  
Barycentric Interpolation ◽  
Microphysical Parameters

Abstract. Clouds are one of the main reasons of uncertainties in the forecasts of weather and climate. In part, this is due to limitations of remote sensing of cloud microphysics. Present approaches often use passive spectral measurements for the remote sensing of cloud microphysical parameters. Large uncertainties are introduced by three-dimensional (3-D) radiative transfer effects and cloud inhomogeneities. Such effects are largely caused by unknown orientation of cloud sides or by shadowed areas on the cloud. Passive ground-based remote sensing of cloud properties at high spatial resolution could be crucially improved with this kind of additional knowledge of cloud geometry. To this end, a method for the accurate reconstruction of 3-D cloud geometry from cloud radar measurements is developed in this work. Using a radar simulator and simulated passive measurements of model clouds based on a large eddy simulation (LES), the effects of different radar scan resolutions and varying interpolation methods are evaluated. In reality, a trade-off between scan resolution and scan duration has to be found as clouds change quickly. A reasonable choice is a scan resolution of 1 to 2\\degree. The most suitable interpolation procedure identified is the barycentric interpolation method. The 3-D reconstruction method is demonstrated using radar scans of convective cloud cases with the Munich miraMACS, a 35 GHz scanning cloud radar. As a successful proof of concept, camera imagery collected at the radar location is reproduced for the observed cloud cases via 3-D volume reconstruction and 3-D radiative transfer simulation. Data sets provided by the presented reconstruction method will aid passive spectral ground-based measurements of cloud sides to retrieve microphysical parameters.

scholarly journals SUPER RESOLUTION RECONSTRUCTION BASED ON ADAPTIVE DETAIL ENHANCEMENT FOR ZY-3 SATELLITE IMAGES

2016 ◽  
Vol III-7 ◽  
pp. 213-217 ◽  
Author(s):  
Hong Zhu ◽  
Weidong Song ◽  
Hai Tan ◽  
Jingxue Wang ◽  
Di Jia
Keyword(s):  
Remote Sensing ◽  
Large Scale ◽  
Satellite Images ◽  
Interpolation Method ◽  
Super Resolution ◽  
Bilateral Filter ◽  
Redundant Information ◽  
Evaluation Standard ◽  
Detail Enhancement ◽  
Image Details

Super-resolution reconstruction of sequence remote sensing image is a technology which handles multiple low-resolution satellite remote sensing images with complementary information and obtains one or more high resolution images. The cores of the technology are high precision matching between images and high detail information extraction and fusion. In this paper puts forward a new image super resolution model frame which can adaptive multi-scale enhance the details of reconstructed image. First, the sequence images were decomposed into a detail layer containing the detail information and a smooth layer containing the large scale edge information by bilateral filter. Then, a texture detail enhancement function was constructed to promote the magnitude of the medium and small details. Next, the non-redundant information of the super reconstruction was obtained by differential processing of the detail layer, and the initial super resolution construction result was achieved by interpolating fusion of non-redundant information and the smooth layer. At last, the final reconstruction image was acquired by executing a local optimization model on the initial constructed image. Experiments on ZY-3 satellite images of same phase and different phase show that the proposed method can both improve the information entropy and the image details evaluation standard comparing with the interpolation method, traditional TV algorithm and MAP algorithm, which indicate that our method can obviously highlight image details and contains more ground texture information. A large number of experiment results reveal that the proposed method is robust and universal for different kinds of ZY-3 satellite images.

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