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Land Cover Classification With Superpixels and Jaccard Index Post-Optimization
AbstractIn this work, we consider the land cover classification task of the DeepGlobe Challenge. This task features the largest available labeled dataset for satellite imagery segmentation. We propose an approach to this problem where standard neural network image classification models are augmented by superpixel extraction and postprocessing that aims to directly optimize the average Jaccard index.
Related Material
[pdf][bibtex]@InProceedings{Davydow_2018_CVPR_Workshops,
author = {Davydow, Alex and Nikolenko, Sergey},
title = {Land Cover Classification With Superpixels and Jaccard Index Post-Optimization},
booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops},
month = {June},
year = {2018}
}
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