T OWARDS AUTOMATED SATELLITE IMAGE SEGMENTATION AND CLASSIFICATION FOR ASSESSING DISASTER DAMAGE USING DATA-SPECIFIC FEATURES WITH INCREMENTAL LEARNING

@inproceedings{Vetrivel2016TOA,
  title={T OWARDS AUTOMATED SATELLITE IMAGE SEGMENTATION AND CLASSIFICATION FOR ASSESSING DISASTER DAMAGE USING DATA-SPECIFIC FEATURES WITH INCREMENTAL LEARNING},
  author={Anand Vetrivel and Norman Kerle and Markus Gerke and Francesco Nex and George Vosselman and george. vosselman},
  year={2016}
}
Automated damage assessment based on satellite imagery is crucial for initiating fast response actions. Several methods based on supervised learning approaches have been reported as effective for automated mapping of damages using remote sensing images. However, adopting these methods for practical use is still challenging, as they typically demand large amounts of training samples to build a supervised classifier, which are usually not readily available. With the advancement in technologies… CONTINUE READING

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