• Open questions for applying stripes:
  • What kind of stripes will we be seeing (and how do we define them maybe?)?
    • Solid stripes/missing scanlines
    • Multiplicative stripes
  • Stripe frequency
  • What is the structure of the stripes across the datacube
  • Mathematical parameters/model for stripes

Notes

• A digital architecture for striping noise compensation in push-broom hyperspectral cameras (spiedigitallibrary.org)
  • Push broom hyperspectral cameras affected by two types of fixed-pattern noise
    • Nonuniformity noise: unequal response of photodetectors lines in the focal plane arrays for homogeneous radiation in the spatial domain, for every spectral band.
      • General, non-striped noise across the entire image like static?
    • Striping noise: “superimposes a pattern of stripes along the scanning direction on every image collected by the camera”
• Remote Sensing | Free Full-Text | Reduction of Uncorrelated Striping Noise—Applications for Hyperspectral Pushbroom Acquisitions (mdpi.com)
  • Thermally induced dark current, causing random movement of electrons in sensors
  • “The thermally induced dark current variations are Gaussian distributed (Gaussian White Noise) and signal independent”
• De-striping hyperspectral imagery using wavelet transform and adaptive frequency domain filtering - ScienceDirect
  • “However, non-periodic stripes, as in the case of pushbroom and hyperspectral images, possess a range of low to high frequencies.”
  • Using Fourier transform filtering to remove noise
• Full article: Filtering high-resolution hyperspectral imagery in a maximum noise fraction transform domain using wavelet-based de-striping (tandfonline.com)
  • “This image-formation technique causes an in-track striping artefact due to sensitivity differences between individual pixels in the spatial line forming each spectral band. The striping patterns formed by such a system design are nonperiodic and are unique for each band. Besides striping, hyperspectral image cubes show nonsystematic noise in the spatial and spectral domains caused mainly by the physical characteristics of the sensing material (e.g. charge-coupled display and complementary metal-oxide semiconductor) and inherent electronic noise.”
• Hyperspectral Image Destriping and Denoising Using Stripe and Spectral Low-Rank Matrix Recovery and Global Spatial-Spectral Total Variation
  • Partitions images into three parts 1) gaussian noise 2) salt and pepper noise and 3) number of stripes and the intensity (clustering)

Usefu?l: Sci-Hub | Destriping of hyperspectral image data: an evaluation of different algorithms using EO-1 Hyperion data | 10.1117/1.JRS.8.083645 (hkvisa.net)