LPS eATE is a new LPS module for generating high resolution terrain information from stereo imagery. Featuring a completely new design and sophisticated algorithms for generating and classifying dense elevation surfaces, eATE provides an unparalleled environment for processing terrain data. This highly flexible solution provides capabilities for data types ranging from satellite to airborne frame and digital pushbroom sensors and operates in a multi-processor/multi-machine environment.
Screenshots
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High Density Terrain Data Production
Pixel-wise data extracted with LPS eATE. Point cloud, 3D and cross sectional views of the surface are shown in Fugro Viewer.
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Integrated Classification and Filtering
LPS eATE raw output using integrated classification to produce a bare earth model for minimal editing.
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LPS eATE Manager
Configure and manage the process to automatically extract high-density digital terrain data and generate contiguous output in a variety of formats.
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Enhanced Automatic Terrain Extraction Algorithms and Tools
Full control of processing parameters and an improved user experience for strategy definition and usage.
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Parallel and Distributed Processing in eATE
Take advantage of multi-core processing using IMAGINE batch tool, or distributed processing on multiple computers using Condor.
High Density Terrain Like Never Before
With modern airborne and satellite sensors achieving increasingly higher resolution, LPS eATE utilizes this characteristic to generate high-density results. Flexibility in terrain processing options provides full control for differing sensors, radiometry, terrain types, and ground cover.
Utilizing Modern Computing Environments
LPS eATE has the ability to take advantage of multi-core computer systems, and will also support parallel processing across a networked group of computers. This provides a scalable solution ideal for small or large terrain generation jobs.
Flexibility Through Innovation
LPS eATE enables users to produce multiple output formats of varying density simultaneously, including LAS point clouds. This allows increased flexibility, including the ability to visualize imagery in 3D by associating RGB values from imagery with terrain points in the LAS file. Classification can be applied to eliminate or preserve objects of choice in the output.