Beyond RGB: Very high resolution urban remote sensing with multimodal deep networks
Beyond RGB: Very high resolution urban remote sensing with multimodal deep networks
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DOI：
10.1016/j.isprsjprs.2017.11.011
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发表时间：
2018-06-01
影响因子：
12.7
通讯作者：
Lefevre, Sebastien
中科院分区：
文献类型：
--
作者：
Audebert, Nicolas;Le Saux, Bertrand;Lefevre, Sebastien
在这项工作中，我们研究了各种方法来处理非常高分辨率的多模态遥感数据的语义标记。特别是，我们研究了深度完全卷积网络如何适应于处理多模态和多尺度遥感数据进行语义标记。我们的贡献有三个方面：（a）我们提出了一种有效的多尺度方法来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）我们在两个公共数据集上验证了我们的方法，并获得了最先进的结果。我们的研究结果表明，后期融合可以从模糊数据中恢复错误，而早期融合可以更好地进行联合特征学习，但对丢失数据的敏感性更高。(C)2017年国际摄影测量与遥感学会（摄影测量和遥感学会）。Elsevier B.V.出版，保留所有权利。
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. (C) 2017 International Society for Photogrammetry and Remote Sensing, Inc. (ISPRS). Published by Elsevier B.V. All rights reserved.