Hyperspectral Image Analysis in Single-Modal and Multimodal setting using Deep Learning Techniques

Abstract

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.