The problem of temperature and spectral emissivity assessment from hyperspectral remotely sensed data is discussed with reference to monitoring of active fires and hot targets. A new algorithm, called similar pixel addition, was developed, which allows us to retrieve the temperature of burning areas by employing spectral data collected at thermal infrared (TIR) wavelengths. The new algorithm resolves the uncertainty connected with temperature-emissivity separation assuming a slow spatial variation of emissivity, hence reducing the number of unknowns involved in the inversion of a couple of similar pixels at once. Performance of this procedure is thoroughly discussed and compared with results from two other algorithms operating in the TIR and shortwave infrared spectral ranges. This paper shows results obtained applying the new algorithm to hyperspectral images gathered by the Multispectral Infrared and Visible Imaging Spectrometer in Northern Italy (Alps) over a natural fire that broke out in July 1999. This paper is completed with a theoretical discussion of the involved topics.

The problem of assessing the noise amplitude affecting remotely sensed hyperspectral images and the corresponding signal-to-noise ratio is discussed. An original algorithm for noise estimation, which performs the analysis of image bit-planes in order to assess their randomness, is described. Differently from more traditional signal-to-noise estimators, which need a homogeneous area in the concerned image to isolate noise contributions, this estimator is almost insensitive to scene texture, a circumstance that allows the developed method to carefully assess the noise amplitude of nearly any observed targets. The developed algorithm has been compared with the well-known noise estimator scatterplot method, for which a novel implementation based on the Hough transform is presented. Hyperspectral and multispectral data cubes collected by the following aerospace imagers, MIVIS, VIRS-200, and MOMS-2P on PRIRODA, have been utilized for investigating the performance of the two considered estimators. Outcomes from processing synthetic and natural images are presented and discussed along this paper.

The increasing amount of data generated by space applications poses several challenges due to limited resources available onboard: power, memory, computation, data rate. In this paper, we propose Compressed Sensing (CS) as the key tool to face those challenges via compressive imaging. This signal processing technique, only recently applied to space applications, dramatically simplifies the image acquisition featuring native compression/encryption and enabling onboard image analysis, allowing to design simpler and lighter optical systems. In this paper, we try to answer the following question: To what extent are the potential benefits of CS going to materialize in a realistic "space big data" application scenario? To this purpose, we first review compressive imaging techniques and already existing prototypes and concepts, critically discussing the technological issues involved. Then, we propose a set of instrument concepts in the application domains of space science, planetary exploration and earth observation, most suitable for a CS-based application. For the most promising of them, we go deeper into the analysis showing preliminary reconstruction performance tests.

This paper describes a simple, iterative atmospheric correction procedure based on the MODTRAN^®5 radiative transfer code. Such a procedure receives in input a spectrally resolved at-sensor radiance image, evaluates the different contributions to received radiation, and corrects the effect of adjacency from surrounding pixels permitting the retrieval of ground reflectance spectrum for each pixel of the image. The procedure output is a spectral ground reflectance image obtained without the need of any user-provided a priori hypothesis. The novelty of the proposed method relies on its iterative approach for evaluating the contribution of surrounding pixels: a first run of the atmospheric correction procedure is performed by assuming that the spectral reflectance of the surrounding pixels is equal to that of the pixel under investigation. Such information is used in the subsequent iteration steps to estimate the spectral radiance of the surrounding pixels, in order to make a more accurate evaluation of the reflectance image. The results are here presented and discussed for two different cases: synthetic images produced with the hyperspectral simulation tool PRIMUS and real images acquired by CHRIS–PROBA sensor. The retrieved reflectance error drops after a few iterations, providing a quantitative estimate for the number of iterations needed. Relative error after the procedure converges is in the order of few percent, and the causes of remaining uncertainty in retrieved spectra are discussed.

Compressive sensing (CS) has been proposed as a disruptive approach to developing a novel class of optical instrumentation used in diverse application domains. Thanks to sparsity as an inherent feature of many natural signals, CS allows for the acquisition of the signal in a very compact way, merging acquisition and compression in a single step and, furthermore, offering the capability of using a limited number of detector elements to obtain a reconstructed image with a larger number of pixels. Although the CS paradigm has already been applied in several application domains, from medical diagnostics to microscopy, studies related to space applications are very limited. In this paper, we present and discuss the instrumental concept, optical design, and performances of a CS imaging spectrometer for ultraviolet-visible (UV–Vis) stellar spectroscopy. The instrument—which is pixel-limited in the entire 300 nm–650 nm spectral range—features spectral sampling that ranges from 2.2 nm@300 nm to 22 nm@650 nm, with a total of 50 samples for each spectrum. For data reconstruction quality, the results showed good performance, measured by several quality metrics chosen from those recommended by CCSDS. The designed instrument can achieve compression ratios of 20 or higher without a significant loss of information. A pros and cons analysis of the CS approach is finally carried out, highlighting main differences with respect to a traditional system.