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Large urban zone classification on SPOT-5 imagery with convolutional neural networks | IEEE Conference Publication | IEEE Xplore

Large urban zone classification on SPOT-5 imagery with convolutional neural networks


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 oper...Show More

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.
Date of Conference: 10-15 July 2016
Date Added to IEEE Xplore: 03 November 2016
ISBN Information:
Electronic ISSN: 2153-7003
Conference Location: Beijing, China

References

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