Convolutional Autoencoder Applied to Short SAR Time Series for Under Canopy Object Detection

SAR time series are powerful assets for forest monitoring. In recent years, they were involved in various classical forest applications such as forest mapping [9] . These applications largely benefited from the advances of Deep Learning, particularly Unsupervised Learning, using Convolutional Autoencoders in applications such as wildfire detection [4] . Not only did purely temporal approaches offer high prediction performance compared to spatiotemporal variants, but the unsupervised autoencoder rivaled its supervised counterparts. The monitoring of forests also involves the detection of under-canopy targets, which could disturb protected environments. The literature mostly relies on classical SAR approaches PolSAR change detection [8] . A recent shift towards the usage of SAR time series displayed promising performance [10] . Thus, to fully exploit the potential of multi-temporal SAR imagery, this paper proposes the usage of unsupervised Deep Learning, particularly the Convolutional Autoencoder, to detect under forest cover objects.