2024/02/06 by Rukmal Ratnayake, Ratnayake, R. M. K. L., D. M. U. P. Sumanasekara +9
Engineering · #FOS: Electrical engineering #Image and Video Processing (eess.IV) #Remote-Sensing Image Classification #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2402.03835
openalex publication_date 2024/02/06 · openalex created_date 2024/02/08 · openalex updated_date 2026/07/28
In recent years, transformer-based deep learning networks have gained popularity in Hyperspectral (HS) unmixing applications due to their superior performance. The attention mechanism within transformers facilitates input-dependent weighting and enhances contextual awareness during training. Drawing inspiration from this, we propose a novel attention-based Hyperspectral Unmixing algorithm called Transformer-based Endmember Fusion with Spatial Context for Hyperspectral Unmixing (FusionNet). This network leverages an ensemble of endmembers for initial guidance, effectively addressing the issue of relying on a single initialization. This approach helps avoid suboptimal results that many algorithms encounter due to their dependence on a singular starting point. The FusionNet incorporates a Pixel Contextualizer (PC), introducing contextual awareness into abundance prediction by considering neighborhood pixels. Unlike Convolutional Neural Networks (CNNs) and traditional Transformer-based approaches, which are constrained by specific kernel or window shapes, the Fusion network offers flexibility in choosing any arbitrary configuration of the neighborhood. We conducted a comparative analysis between the FusionNet algorithm and eight state-of-the-art algorithms using three widely recognized real datasets and one synthetic dataset. The results demonstrate that FusionNet offers competitive performance compared to the other algorithms.