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The mid-infrared spectral (MIR) region is poorly exploited in Earth Observation (EO) applications despite its potential for impacting several application fields, from climatological studies to land management. Among these, high-temperature events (HTE) monitoring plays a key role. Here we discuss the expected impact on EO data and relevant scientific applications by presenting data simulations and relevant Signal-to-Noise Ratio (SNR) evaluation for an innovative, high spatial-resolution multispectral imager—based on Compressive Sensing approach and working in the MIR—studied in the frame of the ASI-funded "SISSI" project. The working principle, expected data output, and performances with impact on HTE detection and monitoring are presented. [ABSTRACT FROM AUTHOR]

Recent theoretical investigations have shown important radiometric disadvantages of interferential multiplexing in Fourier transform spectrometry that apparently can be applied even to coded aperture spectrometers. We have reexamined the methods of noninterferential multiplexing in order to assess their signal-to-noise ratio (SNR) performance, relying on a theoretical modeling of the multiplexed signals. We are able to show that quite similar SNR and radiometric disadvantages affect multiplex dispersive spectrometry. The effect of noise on spectral estimations is discussed. OCIS codes: 300.0300, 300.6300, 060.4230, 170.1630.

We describe an image compression strategy potentially capable of preserving the scientific quality of astrophysical data, simultaneously allowing a consistent bandwidth reduction to be achieved. Unlike strictly lossless techniques, by which moderate compression ratios are attainable, and conventional lossy techniques, in which the mean square error of the decoded data is globally controlled by users, near-lossless methods are capable of locally constraining the maximum absolute error, based on user's requirements. An advanced lossless/near-lossless differential pulse code modulation (DPCM) scheme, recently introduced by the authors and relying on a causal spatial prediction, is adjusted to the specific characteristics of astrophysical image data (high radiometric resolution, generally low noise, etc.). The background noise is preliminarily estimated to drive the quantization stage for high quality, which is the primary concern in most of astrophysical applications. Extensive experimental results of lossless, near-lossless, and lossy compression of astrophysical images acquired by the Hubble space telescope show the advantages of the proposed method compared to standard techniques like JPEG-LS and JPEG2000. Eventually, the rationale of virtually lossless compression, that is, a noise-adjusted lossles/near-lossless compression, is highlighted and found to be in accordance with concepts well established for the astronomers' community.