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.
|Titolo:||Large urban zone classification on SPOT-5 imagery with convolutional neural networks|
|Data di pubblicazione:||2016|
|Appare nelle tipologie:||04.01 - Contributo in atti di convegno|