Calibration and Characterization of Hyperspectral Imaging Systems Used for Natural Scene Imagery

• 作者: Nsikak E. Ekpenyong
• 来源期刊: 光学与光子学期刊(英文)
• 学科: 医学
• 关键词: HYPERSPECTRAL IMAGING CALIBRATION
• 年，卷（期）: 2019,（7）
• 页码范围: 81-98
• 页数: 18页
• 分类号: R73

摘要

Calibration and characterization of focal plane hyperspectral imaging systems play an important role in natural scene imagery. Illumination plays a major role during imaging, as both the camera and electronically tunable filter may suffer low transmission at the ends of the visible spectrum, resulting in a low signal to noise ratio. It is important that the spectral characteristics of the imaging system as well as its geometric properties be well characterized and its radiometric performance known. The aim of this article is to identify the main sources of errors in a common design of focal-plane hyperspectral imaging system and devise ways of compensating for these errors. Calibration and characterization of a focal-plane hyperspectral imaging system include nominal wavelength accuracy analysis. This was carried out by capturing images of a mercury vapour lamp to study principal emission lines in the visible spectrum. The linearity of the hyperspectral imaging system was investigated by recording an input-output function. This was accomplished by comparing signals captured by the hyperspectral imaging system and luminance data recorded using a luminance meter. System noise characterization was done by repeated acquisitions of dark noise images captured under identical conditions. Main meridian analysis was accomplished by obtaining sample edge patches from the centre and near-boundary of hyperspectral image and then constructing edge and line spread functions. The final test image analysis involved verifying system calibration, image correction and compensation algorithms. Results show that with proper calibration and characterization of imaging systems, high quality images are obtained and can be used for research works which include hyperspectral image registration and hyperspectral image recognition for natural scenes.