In this paper, we present an in-depth analysis of the use of convolutional neural networks (CNN), a deep learning method widely applied in remote sensing-based studies in recent years, for burned area (BA) mapping combining radar and optical datasets acquired by Sentinel-1 and Sentinel-2 on-board sensors, respectively. Combining active and passive datasets into a seamless wall-to-wall cloud cover independent mapping algorithm significantly improves existing methods based on either sensor type. Five areas were used to determine the optimum model settings and sensors integration, whereas five additional ones were utilised to validate the results. The optimum CNN dimension and data normalisation were conditioned by the observed land cover class and data type (i.e., optical or radar). Increasing network complexity (i.e., number of hidden layers) only resulted in rising computing time without any accuracy enhancement when mapping BA. The use of an optimally defined CNN within a joint active/passive data combination allowed for (i) BA mapping with similar or slightly higher accuracy to those achieved in previous approaches based on Sentinel-1 (Dice coefficient, DC of 0.57) or Sentinel-2 (DC 0.7) only and (ii) wall-to-wall mapping by eliminating information gaps due to cloud cover, typically observed for optical-based algorithms.

在本文中，我们对卷积神经网络（CNN）的使用进行了深入的分析，卷积神经网络是近年来广泛用于基于遥感的研究中的深度学习方法，用于结合雷达和光学数据集的烧伤区域（BA）映射分别由Sentinel-1和Sentinel-2板载传感器采集。将主动和被动数据集组合到无缝的墙到墙云覆盖独立映射算法中，可以显着改善基于任何一种传感器类型的现有方法。五个区域用于确定最佳模型设置和传感器集成，而另外五个区域则用于验证结果。最佳CNN尺寸和数据归一化取决于所观察到的土地覆盖类别和数据类型（即光学或雷达）。网络复杂度不断提高（即，映射BA时，隐藏层的数量）仅导致计算时间增加，而没有任何准确性提高。在联合的主动/被动数据组合中使用最佳定义的CNN，可以实现（i）BA映射，其准确性与基于Sentinel-1（骰子系数，DC为0.57）或Sentinel-仅限于图2（DC 0.7），以及（ii）通过消除云覆盖导致的信息空白（通常在基于光学的算法中观察到）来进行墙到墙映射。