Canopy Height Estimation from Spaceborne Imagery Using Convolutional Encoder-Decoder

Abstract

The recent advances in multimedia modeling with deep learning methods have significantly affected remote sensing applications, such as canopy height mapping. Estimating canopy height maps in large-scale is an important step towards sustainable ecosystem management. Apart from the standard height estimation method using LiDAR data, other airborne measurement techniques, such as very high-resolution passive airborne imaging, have also shown to provide accurate estimations. However, those methods suffer from high cost and cannot be used at large-scale nor frequently. In our study, we adopt a neural network architecture to estimate pixel-wise canopy height from cost-effective spaceborne imagery. A deep convolutional encoder-decoder network, based on the SegNet architecture together with skip connections, is trained to embed the multi-spectral pixels of a Sentinel-2 input image to height values via end-to-end learned texture features. Experimental results in a study area of 942 $\mathrm{km}^2$ yield similar or better estimation accuracy resolution in comparison with a method based on costly airborne images as well as with another state-of-the-art deep learning approach based on spaceborne images.