Physical Degradation Model-Guided Interferometric Hyperspectral Reconstruction with Unfolding Transformer
This work addresses a domain-specific problem in remote sensing by improving IHI reconstruction, but it is incremental as it builds on existing deep learning and transformer methods.
The paper tackles the problem of reconstructing high-quality hyperspectral images from interferometric hyperspectral imaging (IHI) data, which suffers from complex errors and lacks training datasets, by proposing a physics-guided degradation model and a transformer-based network, achieving superior performance and generalization in experiments.
Interferometric Hyperspectral Imaging (IHI) is a critical technique for large-scale remote sensing tasks due to its advantages in flux and spectral resolution. However, IHI is susceptible to complex errors arising from imaging steps, and its quality is limited by existing signal processing-based reconstruction algorithms. Two key challenges hinder performance enhancement: 1) the lack of training datasets. 2) the difficulty in eliminating IHI-specific degradation components through learning-based methods. To address these challenges, we propose a novel IHI reconstruction pipeline. First, based on imaging physics and radiometric calibration data, we establish a simplified yet accurate IHI degradation model and a parameter estimation method. This model enables the synthesis of realistic IHI training datasets from hyperspectral images (HSIs), bridging the gap between IHI reconstruction and deep learning. Second, we design the Interferometric Hyperspectral Reconstruction Unfolding Transformer (IHRUT), which achieves effective spectral correction and detail restoration through a stripe-pattern enhancement mechanism and a spatial-spectral transformer architecture. Experimental results demonstrate the superior performance and generalization capability of our method.The code and are available at https://github.com/bit1120203554/IHRUT.