BJTU-UVA: The First Dataset for Automatic Spectral Calibration of Hyperspectral Images

<p dir="ltr">We are proud to introduce <b>BJTU-UVA</b>, the <b>first dataset designed specifically for the task of automatic spectral calibration</b> of hyperspectral images (HSIs). This dataset addresses the critical challenge of minimizing illumination variability without relying on manual intervention or physical references.</p><h2>Key Highlights</h2><ul><li><b>Task Proposal</b>:<br>We propose the novel task of <b>automatic spectral calibration</b>, aiming to advance the robustness of hyperspectral imaging in diverse real-world scenarios.</li><li><b>Dataset Characteristics</b>:</li><li><ul><li><b>Camera</b>: Specim IQ, featuring a spectral resolution of 3nm across the 400–1000nm range.</li><li><b>Recording Method</b>: Each scene is captured twice:</li><li><ol><li><b>Without reference board</b>: Captures raw scene data.</li><li><b>With white reference board</b>: Records illumination conditions under the same settings.<br>This approach ensures asynchronous yet precise pairing of <b>uncalibrated</b> and <b>calibrated</b> HSIs, effectively minimizing illumination variability.</li></ol></li><li><b>Dark Current Correction</b>: Dark current noise, intrinsic to the camera sensor, is carefully recorded and subtracted during post-processing, ensuring high data accuracy.</li></ul></li><li><b>Scene Diversity</b>:<br>The dataset encompasses a wide range of <b>urban and natural scenes</b>, captured under various weather conditions, lighting scenarios, and times of day.</li><li><b>Benchmarking Standard</b>:<br>BJTU-UVA establishes a new standard for spectral calibration by combining real-world scene variability with rigorous illumination recording, offering a robust foundation for testing and advancing spectral calibration techniques.</li></ul><h2>Citation</h2><p dir="ltr">@misc{du2024spectral,<br>title={Automatic Spectral Calibration of Hyperspectral Images: Method, Dataset and Benchmark},<br>author={Zhuoran Du and Shaodi You and Cheng Cheng and Shikui Wei},<br>year={2024},<br>eprint={2412.14925},<br>archivePrefix={arXiv},<br>primaryClass={cs.CV},<br>url={ <a href="https://arxiv.org/abs/2412.14925" rel="nofollow" target="_blank">https://arxiv.org/abs/2412.14925</a> },<br>}</p>