AMBER -- Advanced SegFormer for Multi-Band Image Segmentation: an application to Hyperspectral Imaging
This work addresses the problem of limited global context capture in CNNs for hyperspectral imaging, offering a robust solution for remote sensing applications, though it is incremental as it builds upon the existing SegFormer architecture.
The paper tackled hyperspectral image segmentation by introducing AMBER, an advanced SegFormer that incorporates 3D convolutions and a Funnelizer layer, achieving state-of-the-art performance on benchmark datasets like Salinas and PRISMA with improved accuracy metrics.
Deep learning has revolutionized the field of hyperspectral image (HSI) analysis, enabling the extraction of complex spectral and spatial features. While convolutional neural networks (CNNs) have been the backbone of HSI classification, their limitations in capturing global contextual features have led to the exploration of Vision Transformers (ViTs). This paper introduces AMBER, an advanced SegFormer specifically designed for multi-band image segmentation. AMBER enhances the original SegFormer by incorporating three-dimensional convolutions, custom kernel sizes, and a Funnelizer layer. This architecture enables processing hyperspectral data directly, without requiring spectral dimensionality reduction during preprocessing. Our experiments, conducted on three benchmark datasets (Salinas, Indian Pines, and Pavia University) and on a dataset from the PRISMA satellite, show that AMBER outperforms traditional CNN-based methods in terms of Overall Accuracy, Kappa coefficient, and Average Accuracy on the first three datasets, and achieves state-of-the-art performance on the PRISMA dataset. These findings highlight AMBER's robustness, adaptability to both airborne and spaceborne data, and its potential as a powerful solution for remote sensing and other domains requiring advanced analysis of high-dimensional data.