  Multitask learning is a widely recognized technique in the field of computer
vision and deep learning domain. However, it is still a research question in
remote sensing, particularly for hyperspectral imaging. Moreover, most of the
research in the remote sensing domain focuses on small and single-task-based
annotated datasets, which limits the generalizability and scalability of the
developed models to more diverse and complex real-world scenarios. Thus, in
this study, we propose a multitask deep learning model designed to perform
multiple classification and regression tasks simultaneously on hyperspectral
images. We validated our approach on a large hyperspectral dataset called
TAIGA, which contains 13 forest variables, including three categorical
variables and ten continuous variables with different biophysical parameters.
We design a sharing encoder and task-specific decoder network to streamline
feature learning while allowing each task-specific decoder to focus on the
unique aspects of its respective task.
  Additionally, a dense atrous pyramid pooling layer and attention network were
integrated to extract multi-scale contextual information and enable selective
information processing by prioritizing task-specific features. Further, we
computed multitask loss and optimized its parameters for the proposed framework
to improve the model performance and efficiency across diverse tasks. A
comprehensive qualitative and quantitative analysis of the results shows that
the proposed method significantly outperforms other state-of-the-art methods.
We trained our model across 10 seeds/trials to ensure robustness. Our proposed
model demonstrates higher mean performance while maintaining lower or
equivalent variability. To make the work reproducible, the codes will be
available at
https://github.com/Koushikey4596/Multitask-Deep-Learning-Model-for-Taiga-datatset.
