Hyperspectral Image Analysis in Single-Modal and Multimodal setting
  using Deep Learning Techniques
Shivam Pande
Hyperspectral imaging provides precise classification for land use and cover
due to its exceptional spectral resolution. However, the challenges of high
dimensionality and limited spatial resolution hinder its effectiveness. This
study addresses these challenges by employing deep learning techniques to
efficiently process, extract features, and classify data in an integrated
manner. To enhance spatial resolution, we integrate information from
complementary modalities such as LiDAR and SAR data through multimodal
learning. Moreover, adversarial learning and knowledge distillation are
utilized to overcome issues stemming from domain disparities and missing
modalities. We also tailor deep learning architectures to suit the unique
characteristics of HSI data, utilizing 1D convolutional and recurrent neural
networks to handle its continuous spectral dimension. Techniques like visual
attention and feedback connections within the architecture bolster the
robustness of feature extraction. Additionally, we tackle the issue of limited
training samples through self-supervised learning methods, employing
autoencoders for dimensionality reduction and exploring semi-supervised
learning techniques that leverage unlabeled data. Our proposed approaches are
evaluated across various HSI datasets, consistently outperforming existing
state-of-the-art techniques.