Improving specific class mapping from remotely sensed data by cost-sensitive learning
In many remote-sensing projects, one is usually interested in a small number of land-cover classes present in a study area and not in all the land-cover classes that make-up the landscape. Previous studies in supervised classification of satellite images have tackled specific class mapping problem b...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Taylor & Francis
2017
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| Online Access: | http://eprints.nottingham.ac.uk/41520/ http://eprints.nottingham.ac.uk/41520/ http://eprints.nottingham.ac.uk/41520/ http://eprints.nottingham.ac.uk/41520/1/Improving%20specific%20class%20mapping%20by%20cost-sensitive%20learning.pdf |