Assessment of signal-to-noise ratio of CHRIS/PROBA and other hyperspectral sensors using images acquired over the San Rossore (Italy) test site

Alessandro Barducci; Donatella Guzzi; Paolo Marcoionni; Ivan Pippi

doi:10.1117/12.628333

18 October 2005 Assessment of signal-to-noise ratio of CHRIS/PROBA and other hyperspectral sensors using images acquired over the San Rossore (Italy) test site

Alessandro Barducci, Donatella Guzzi, Paolo Marcoionni, Ivan Pippi

Author Affiliations +

Proceedings Volume 5982, Image and Signal Processing for Remote Sensing XI; 59820N (2005) https://doi.org/10.1117/12.628333
Event: SPIE Remote Sensing, 2005, Bruges, Belgium

Abstract

Using several hyperspectral images acquired by various aerospace sensors over San Rossore (Italy) test site, different issues concerning the assessment of the noise amplitude, which affects the remotely sensed images, are investigated. An innovative algorithm, developed by the authors, is presented and its performance is discussed. This procedure analyses the bit-planes extracted from any monochromatic image in the hyperspectral data cube, then it assesses the randomness of every bit-plane, and computes the signal-to-noise ratio for each spectral channel. Differently from more traditional signal-to-noise ratio estimators, which need an homogeneous area in the concerned image to isolate noise contribution only, the new algorithm is almost insensitive to scene texture. Due to this property the developed method is able to process the image of any observed ground area. The paper discusses other possible sources of systematic disturbance (like stripe-noise, smear effect, and so forth), which may dim the quality of remotely sensed data. Finally, the behaviour of signal-to-noise ratio of CHRIS and other hyperspectral sensors like MIVIS and VIRS-200 is shown as function of wavelengths.

Citation Download Citation

Alessandro Barducci, Donatella Guzzi, Paolo Marcoionni, and Ivan Pippi "Assessment of signal-to-noise ratio of CHRIS/PROBA and other hyperspectral sensors using images acquired over the San Rossore (Italy) test site", Proc. SPIE 5982, Image and Signal Processing for Remote Sensing XI, 59820N (18 October 2005); https://doi.org/10.1117/12.628333

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