CATSNet: a context-aware network for Height Estimation in a Forested
  Area based on Pol-TomoSAR data
Wenyu Yang Sergio Vitale Hossein Aghababaei Giampaolo Ferraioli Vito Pascazio Gilda Schirinzi
Tropical forests are a key component of the global carbon cycle. With plans
for upcoming space-borne missions like BIOMASS to monitor forestry, several
airborne missions, including TropiSAR and AfriSAR campaigns, have been
successfully launched and experimented. Typical Synthetic Aperture Radar
Tomography (TomoSAR) methods involve complex models with low accuracy and high
computation costs. In recent years, deep learning methods have also gained
attention in the TomoSAR framework, showing interesting performance. Recently,
a solution based on a fully connected Tomographic Neural Network (TSNN) has
demonstrated its effectiveness in accurately estimating forest and ground
heights by exploiting the pixel-wise elements of the covariance matrix derived
from TomoSAR data. This work instead goes beyond the pixel-wise approach to
define a context-aware deep learning-based solution named CATSNet. A
convolutional neural network is considered to leverage patch-based information
and extract features from a neighborhood rather than focus on a single pixel.
The training is conducted by considering TomoSAR data as the input and Light
Detection and Ranging (LiDAR) values as the ground truth. The experimental
results show striking advantages in both performance and generalization ability
by leveraging context information within Multiple Baselines (MB) TomoSAR data
across different polarimetric modalities, surpassing existing techniques.