Physical Degradation Model-Guided Interferometric Hyperspectral Reconstruction with Unfolding Transformer

TL;DR

This paper introduces a physics-based degradation model and a novel transformer architecture for interferometric hyperspectral image reconstruction, significantly improving accuracy and generalization in remote sensing applications.

Contribution

The paper presents a new IHI reconstruction pipeline combining a physics-based degradation model with a transformer network, enabling realistic training data synthesis and enhanced spectral correction.

Findings

Superior reconstruction accuracy demonstrated in experiments

Enhanced generalization to various IHI conditions

Effective spectral correction and detail restoration

Abstract

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.