Simultaneous contextual classification of multitemporal and multiscale remote sensing imagery based on existing GIS data for training

Project: Research

Participants

  • Christian Heipke (Principal Investigator)
  • Franz Rottensteiner (Co-Investigator)
  • Alina Maas (Project staff)
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Details

Description

The goal of this project is to develop a novel methodology for the supervised context-based classification of multitemporal and multiscale remote sensing imagery using existing GIS data. With this method it should be possible to get the actual state of land coverage from actual remote sensing images without manually labeling training data, which is very costly and time consuming. Therefore this method can be used to update old maps automatically.

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StatusFinished
Start/end date1 Apr 2016 → 30 Sept 2020

Funding

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