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.
Large urban zone classification on SPOT-5 imagery with convolutional neural networks
De Martino, Michaela;Moser, Gabriele;Serpico, Sebastiano B.
2016-01-01
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.File in questo prodotto:
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