Potential of Convolutional Neural Networks for Forest Mapping Using Sentinel-1 Interferometric Short Time Series
The monitoring of land cover and land use patterns is pivotal in the joint effort to fight deforestation in the Amazon and study its relation to climate change effects with respect to anthropogenic activities. Most of the region, typically monitored with optical sensors, is hidden by a persistent cloud cover for most of the wet season. The necessity for a consistent and reliable deforestation warning system based on cloud-independent radar data is therefore of particular interest. In this
... we investigated the potential of combining deep learning with Sentinel-1 (S-1) Interferometric Synthetic Aperture Radar (InSAR) short time series (STS), covering only 24 d of acquisitions, to map endangered areas in the Amazon Basin. To this end, we implemented a U-Net-like convolutional neural network (CNN) for multi-layer semantic segmentation, trained from scratch with different sets of input features to evaluate the viability of the proposed approach for different operating conditions. As input features, we relied on both multi-temporal backscatter and interferometric coherences at different temporal baselines. We provide a detailed performance benchmark of the different configurations, also considering the current state-of-the-art approaches based on S-1 STS and shallow learners. Our findings showed that, by exploiting the powerful learning capabilities of CNNs, we outperformed the STS-based approaches published in the literature and significantly reduced the computational load. Indeed, when considering the entire stack of Sentinel-1 data at a 6 d revisit time, we were able to maintain the overall accuracy and F1-score well above 90% and reduce the computational time by more than 50% with respect to state-of-the-art approaches, by avoiding the generation of handcrafted feature maps. Moreover, we achieved satisfactory results even when only S-1 InSAR acquisitions with a revisit time of 12 d or more were used, setting the groundwork for an effective and fast monitoring of tropical forests at a global scale.