Classification of Quickbird imagery over urban area using convolutional neural network

Abstract

During the past decades significant efforts have been made in developing various methods for Very high spatial resolution (VHSR) remotely sensed image classification; most of them are based on handcrafted learning-based features. Recently deep learning-based techniques have demonstrated excellent performance in remote sensing applications. In this paper we address the problem of urban imagery classification by developing a convolutional neural network (CNN) approach, which are the most popular deep learning approach for image classification. We design a custom CNN that operates on local patches in order to produce pixel-level classification map. The performance of the proposed model is validated on an exhaustive experimental comparison on a set of 20 QuickBird pansharpened multi-spectral images in urban zones. The obtained results outperform those obtained by different classification approaches on the same dataset.