In this work, we investigate various methods to deal with semantic labeling of very high resolution multi-modal remote sensing data. Especially, we study how deep fully convolutional networks can be adapted to deal with multi-modal and multi-scale remote sensing data for semantic labeling. Our contributions are threefold: (a) we present an efficient multi-scale approach to leverage both a large spatial context and the high resolution data, (b) we investigate early and late fusion of Lidar and multispectral data, (c) we validate our methods on two public datasets with state-of-the-art results. Our results indicate that late fusion make it possible to recover errors steaming from ambiguous data, while early fusion allows for better joint-feature learning but at the cost of higher sensitivity to missing data.

在这项工作中，我们研究了各种方法来处理超高分辨率多模式遥感数据的语义标记。尤其是，我们研究了深度卷积网络如何适用于处理用于语义标记的多模式和多尺度遥感数据。我们的贡献是三方面的：（a）我们提出了一种有效的多尺度方法，以充分利用大空间背景和高分辨率数据；（b）我们研究了激光雷达和多光谱数据的早期和晚期融合；（c）验证了我们的在两个具有最新结果的公共数据集上的方法。我们的结果表明，后期融合使得可以恢复来自歧义数据的错误，而早期融合可以更好地进行联合特征学习，但代价是对丢失的数据具有更高的敏感性。