Automatic Spectral Calibration of Hyperspectral Images:Method, Dataset and Benchmark

TL;DR

This paper introduces a learning-based method for automatic spectral calibration of hyperspectral images, supported by a large-scale dataset and a spectral illumination transformer, achieving state-of-the-art performance.

Contribution

It presents a novel learning-based calibration method, a large-scale diverse dataset, and a benchmark demonstrating superior results over existing techniques.

Findings

The proposed SIT achieves state-of-the-art calibration accuracy.

Low-light conditions pose greater challenges for spectral calibration.

The dataset covers diverse natural scenes and illumination conditions.

Abstract

Hyperspectral image (HSI) densely samples the world in both the space and frequency domain and therefore is more distinctive than RGB images. Usually, HSI needs to be calibrated to minimize the impact of various illumination conditions. The traditional way to calibrate HSI utilizes a physical reference, which involves manual operations, occlusions, and/or limits camera mobility. These limitations inspire this paper to automatically calibrate HSIs using a learning-based method. Towards this goal, a large-scale HSI calibration dataset is created, which has 765 high-quality HSI pairs covering diversified natural scenes and illuminations. The dataset is further expanded to 7650 pairs by combining with 10 different physically measured illuminations. A spectral illumination transformer (SIT) together with an illumination attention module is proposed. Extensive benchmarks demonstrate the SoTA performance of the proposed SIT. The benchmarks also indicate that low-light conditions are more challenging than normal conditions. The dataset and codes are available online:https://github.com/duranze/Automatic-spectral-calibration-of-HSI