UAV-based training for fully fuzzy classification of Sentinel-2 fluvial scenes
Authors: Carbonneau Patrice , Belletti Barbara , Micotti Marco , Casteletti Andrea , Mariani Stefano , Bizzi Simone 
Published: March 2020

&lt;p&gt;&lt;span&gt;In current fluvial remote sensing approaches, there exists a certain dichotomy between the analysis of small channels at local scales which is generally done with airborne data and the analysis of entire basins at regional and national scales with satellite data. &lt;/span&gt;&lt;span&gt;One possible solution to this challeng&lt;/span&gt;&lt;span&gt;e&lt;/span&gt;&lt;span&gt; is to use low-altitude imagery from low-cost UAVs to provide sub-metric scale class information which can then be used to train fuzzy classification models for entire Sentinel 2 tiles&lt;/span&gt;&lt;span&gt;. &lt;/span&gt;&lt;span&gt;The fuzzy classification approach can allow for sub-pixel information and when extended to entire Sentinel 2 tiles, the method therefore develops information at a resolution of less than 10 meters (the best spatial resolution of Sentinel 2 bands) at regional scales. &lt;/span&gt;&lt;span&gt;In &lt;/span&gt;&lt;span&gt;this&lt;/span&gt;&lt;span&gt; contribution, we present &lt;/span&gt;&lt;span&gt;such &lt;/span&gt;&lt;span&gt;a method wh&lt;/span&gt;&lt;span&gt;ere&lt;/span&gt;&lt;span&gt; UAV &lt;/span&gt;&lt;span&gt;imagery &lt;/span&gt;&lt;span&gt;is used &lt;/span&gt;&lt;span&gt;as the training data for the fully fuzzy classification of&lt;/span&gt;&lt;span&gt; Sentinel 2 imagery. &lt;/span&gt;&lt;span&gt;We partition the fluvial corridor in three simple classes: water, dry sediment and vegetation.&amp;#160; Then we manually classify the local UAV imagery into highly accurate class rasters. In order to augment the value of the Sentinel 2 data, we use an established super-resolution method that delivers 10 meter spatial resolution across all 11 Sentinel 2 bands&lt;/span&gt;&lt;span&gt;. &lt;/span&gt;&lt;span&gt;We &lt;/span&gt;&lt;span&gt;then use the sub-metric UAV classifications as training data for the 10 meter super-resolved Sentinel 2 imagery and we&lt;/span&gt;&lt;span&gt; train &lt;/span&gt;&lt;span&gt;fuzzy classification &lt;/span&gt;&lt;span&gt;models using random forests, dense neural networks and convolutional neural networks (CNN). We find that CNN architectures perform best&lt;/span&gt; &lt;span&gt;and &lt;/span&gt;&lt;span&gt;can predict class membership within a pixel of &lt;/span&gt;&lt;span&gt;a new &lt;/span&gt;&lt;span&gt;Sentinel 2 &lt;/span&gt;&lt;span&gt;tile not seen in the training phase&lt;/span&gt;&lt;span&gt; with a mean error of 0% and an RMS error of 1&lt;/span&gt;&lt;span&gt;8&lt;/span&gt;&lt;span&gt;%. Crisp class predictions derived from the fuzzy models range in accuracy from 88% to 9&lt;/span&gt;&lt;span&gt;9&lt;/span&gt;&lt;span&gt;%, &lt;/span&gt;&lt;span&gt;even in the case of tiles never seen in the training phase&lt;/span&gt;&lt;span&gt;. &lt;/span&gt;&lt;span&gt;With this approach, it is now possible to deploy a low-cost UAV in order to train a transferable CNN model that can predict &lt;/span&gt;&lt;span&gt;fuzzy classes at very large scales from freely available Sentinel 2 imagery. &lt;/span&gt; &lt;span&gt;This approach can therefore serve as the basis for multi temporal classification and change detection of the Sentinel 2 archives.&lt;/span&gt;&lt;/p&gt;