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 MoreMetadata
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
Department of Electrical, Electronics, Telecommunication Engineering, University of Genoa, Genoa, Italy
Department of Electrical, Electronics, Telecommunication Engineering, University of Genoa, Genoa, Italy
Department of Electrical, Electronics, Telecommunication Engineering, University of Genoa, Genoa, Italy
Department of Electrical, Electronics, Telecommunication Engineering, University of Genoa, Genoa, Italy
Department of Electrical, Electronics, Telecommunication Engineering, University of Genoa, Genoa, Italy
Department of Electrical, Electronics, Telecommunication Engineering, University of Genoa, Genoa, Italy
Department of Electrical, Electronics, Telecommunication Engineering, University of Genoa, Genoa, Italy
Department of Electrical, Electronics, Telecommunication Engineering, University of Genoa, Genoa, Italy