Hyperspectral Band Selection within a Deep Reinforcement Learning Framework

In the last decades, hyperspectral imaging (HSI) has become an appealing field of remote sensing because of its richness in information. However, HSI suffers from redundancy and high computational complexity. To address this challenge, we present Policy Gradient Band Search - an intuitive and straightforward search strategy for dimensionality reduction of hyperspectral data. The family of policy gradient algorithms is a widely used and successful model-free approach in the reinforcement learning framework and is here applied for HSI band selection. In order to exhibit the effectiveness of our method, we evaluate our approach under consideration the spatial-spectral relationship in combination with a 3D-CNN based HSI classification network. Our approach yields satisfying empirical results in three HSI datasets, which are Pavia University, Salinas and Greding. The overall accuracy of 98.14%,96.64%,99.84% was separately accomplished on these datasets while being restricted by utilizing only four chosen bands.