scholarly journals Urban Infrastructure Monitoring with a Spatially Adaptive Multi?looking InSAR Technique

2015 ◽  
Author(s):  
Jayanti Jessica Sharma ◽  
Jayson Eppler ◽  
Jennifer Busler
Keyword(s):  
Urban Infrastructure ◽  
Infrastructure Monitoring ◽  
Spatially Adaptive

The Citizen Engineer: Urban Infrastructure Monitoring via Crowd-Sourced Data Analytics

2017 ◽  
Author(s):  
Devin K. Harris ◽  
Mohamad Alipour ◽  
Scott T. Acton ◽  
Lisa R. Messeri ◽  
Andrea Vaccari ◽  
...  
Keyword(s):  
Data Analytics ◽  
Urban Infrastructure ◽  
Infrastructure Monitoring

scholarly journals SEMANTIC INTERPRETATION OF INSAR ESTIMATES USING OPTICAL IMAGES WITH APPLICATION TO URBAN INFRASTRUCTURE MONITORING

2015 ◽  
Vol XL-3/W3 ◽  
pp. 153-160 ◽  
Author(s):  
Y. Wang ◽  
X. X. Zhu
Keyword(s):  
Urban Infrastructure ◽  
Point Clouds ◽  
Semantic Interpretation ◽  
Deformation Analysis ◽  
Large Area ◽  
Infrastructure Monitoring ◽  
Semantic Classes ◽  
Optical Images ◽  
High Level ◽  
Semantic Object

Synthetic aperture radar interferometry (InSAR) has been an established method for long term large area monitoring. Since the launch of meter-resolution spaceborne SAR sensors, the InSAR community has shown that even individual buildings can be monitored in high level of detail. However, the current deformation analysis still remains at a primitive stage of pixel-wise motion parameter inversion and manual identification of the regions of interest. We are aiming at developing an automatic urban infrastructure monitoring approach by combining InSAR and the semantics derived from optical images, so that the deformation analysis can be done systematically in the semantic/object level. This paper explains how we transfer the semantic meaning derived from optical image to the InSAR point clouds, and hence different semantic classes in the InSAR point cloud can be automatically extracted and monitored. Examples on bridges and railway monitoring are demonstrated.

Automated 3-D cell population analysis in thick tissue sections using laser-scanning confocal-microscopy data

1993 ◽  
Vol 51 ◽  
pp. 562-563
Author(s):  
Hakan Ancin
Keyword(s):  
Laser Scanning ◽  
Population Analysis ◽  
Three Dimensional ◽  
Large Data ◽  
Laser Scanning Confocal Microscopy ◽  
Tissue Slices ◽  
Scanning Confocal Microscopy ◽  
Tissue Sections ◽  
Spatially Adaptive ◽  
Microscopy Data

This paper presents methods for performing detailed quantitative automated three dimensional (3-D) analysis of cell populations in thick tissue sections while preserving the relative 3-D locations of cells. Specifically, the method disambiguates overlapping clusters of cells, and accurately measures the volume, 3-D location, and shape parameters for each cell. Finally, the entire population of cells is analyzed to detect patterns and groupings with respect to various combinations of cell properties. All of the above is accomplished with zero subjective bias.In this method, a laser-scanning confocal light microscope (LSCM) is used to collect optical sections through the entire thickness (100 - 500μm) of fluorescently-labelled tissue slices. The acquired stack of optical slices is first subjected to axial deblurring using the expectation maximization (EM) algorithm. The resulting isotropic 3-D image is segmented using a spatially-adaptive Poisson based image segmentation algorithm with region-dependent smoothing parameters. Extracting the voxels that were labelled as "foreground" into an active voxel data structure results in a large data reduction.

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