V. A. Krylov, M. de Martino, G. Moser, S. B. Serpico.
"Large Urban Zone classification on SPOT-5 Imagery with Convolutional Neural Networks",
IEEE Geoscience and Remote Sensing Symposium IGARSS 2016,
Proc. of IEEE IGARSS 2016, Beijing (China), July 10-16, 2016.
[link] [pdf] [presentation]
Abstract
In this paper we address the problem of urban optical imagery classification by developing a convolutional neural network (CNN) approach. We design a custom CNN that operates on local patches in order to produce dense pixel-level classification map. In this work we focus on a comprehensive dataset of 2.5-meter SPOT-5 imagery acquired at different dates and sites. The performance of the proposed model is validated on a five target-class problem and compared with a benchmark random forest classifier with a set of hand-picked features.
Bibtex
@INPROCEEDINGS{SerpicoIGARSS12,
author = {V. A. Krylov and M. de Martino and G. Moser and S. B. Serpico},
title = {Large Urban Zone classification on SPOT-5 Imagery with Convolutional Neural Networks},
year = {2016},
booktitle = {Proc. of IEEE IGARSS},
address = {Beijing, China},
}