6533b7d9fe1ef96bd126ce54
RESEARCH PRODUCT
Improving active learning methods using spatial information
Farid MelganiEdoardo PasolliFabio PacificiWilliam J. EmeryDevis Tuiasubject
Active learningContextual image classificationComputer sciencebusiness.industryvery-high-resolution (VHR) imagesTerrainspatial informationsupport vector machines (SVMs)Machine learningcomputer.software_genreRegularization (mathematics)Support vector machineArtificial intelligencebusinessImage resolutioncomputerSpatial analysisdescription
Active learning process represents an interesting solution to the problem of training sample collection for the classification of remote sensing images. In this work, we propose a criterion based on the spatial information that can be used in combination with a spectral criterion in order to improve the selection of training samples. Experimental results obtained on a very high resolution image show the effectiveness of regularization in spatial domain and open challenging perspectives for terrain campaigns planning. © 2011 IEEE.
| year | journal | country | edition | language |
|---|---|---|---|---|
| 2011-07-01 |