Earth Observation (EO) systems are generating an ever-increasing amount of data to be handled on board yet with limited resources, which sometimes hinders a full exploitation of the information content. In this paper, we present a demonstrator of a super-resolved compressive imager operating in whiskbroom mode in the Visible-Near Infrared (VISNIR) and Medium Infrared (MIR) spectral ranges. The demonstrator, which is under development in the frame of the EU H2020 funded SURPRISE project, is based on the use of a Digital MicroMirror Device (DMD) as a core element of its architecture and it is inspired by a single-pixel camera in order to avoid the use of large focal plane arrays. The demonstrator has 10 channels in the VNIR and two channels in the MIR and it can reach a super-resolution factor from 4 x 4 to 32 x 32, that is the ratio between the number of pixels of the image reconstructed at the end of the process and the number of pixels of the detector. Besides, on the grounds of the results obtained by image reconstruction tests on simulated datasets by using Deep Learning based algorithms, data are expected to be natively compressed with a Compression Ratio up to 50%. The study is expected to provide valuable insight for the future development of a novel class of EO instruments with improved performances in terms of ground sampling distance, native compression and on-board processing capabilities. Additional presentation content can be accessed on the supplemental content page.
The paper describes the challenges faced during the optical design of a multispectral imager in the Mid Wave InfraRed (MWIR) based on super-resolution and compressive sensing, performed in the framework of the SISSI project funded by the Italian Space Agency. The project aimed to improve ground sampling distance and to mitigate possible saturation/blooming effects. The operating principle is the one of the single pixel camera. The use of CS architecture allows the scene reconstruction without loss of significant information by using a reduced number of acquisitions. A Digital Micromirror Device (DMD) was used to modulate the observed image by using a binary ON-OFF pattern. A sequence of such spatially-coded acquisitions is integrated by a condenser lens and focused on the 2D detector array, each element of which acting, in parallel, as the single element detector of a single pixel camera. Multispectral acquisition is obtained by deposition of different band pass filters on the 2D detector. The optical design has posed strict constraints on the optics involved. The collection optics, focusing on the DMD plane, must match with the condensing optics. An additional issue is due to the DMD working in reflection mode. A further requirement is given by the Airy disk size. Such constraints lead to an upper limit for the optics f-number. Chromatic aberration introduces further difficulties in glasses selection with high transmissivity in the MWIR. The final optical design of the system involves both reflective and dielectric optical elements, making use of aspheric and free-form surfaces.
An effective maintenance plan of railways bridges needs suitable, user-friendly tools for preventive inspection. The latter, actually, when applied to large infrastructures such as railways bridges, requires a large amount of resources and it is time consuming. Presently, most inspection relies only on visual inspection, often with a lack of adequate procedures for documentation filing and an easy consultation of it. In this paper we present a series of experiments conducted with the aim of developing an ICT tool for supporting the inspection of railways bridges. The developed tool integrates a full range of diverse data and guarantees easy access for their storage and consultation. The data integrated in the ICT platform includes images acquired with diverse techniques (3D scanning, high definition photography, photogrammetry, thermography, fluorescence LIDAR) and integrates them with data from traditional survey methods. In order to make easier the consultation to the user, the data are integrated onto a 3D model of the bridge. In particular, we will focus on the results obtained by using the fluorescence LIDAR and 3D LIDAR scanning technique on a masonry bridge for its preventive inspection and their integration in the ICT platform. All data were also complemented with the results from traditional survey methods. False-color coded thematic maps based on fluorescence LIDAR data highlighted the presence of biofilms and patinas on the bridge’s surface, providing complementary information with respect to those from thermographic images at large.
The need of high-resolution Earth Observation (EO) images for scientific and commercial exploitation has led to the generation of an increasing amount of data with a material impact on the resources needed to handle data on board of satellites. In this respect, Compressive Sensing (CS) can offer interesting features in terms of native compression, onboard processing and instrumental architecture. In CS instruments the data are acquired natively compressed by leveraging on the concept of sparsity, while on-board processing is offered at low computational cost by information extraction directly from CS data. In addition, instrument’s architecture can enjoy super-resolution capabilities that ensure a higher number of pixels in the reconstructed image with respect to that natively provided by the detector. In this paper, we present the working principle and main features of a CS demonstrator of a super-resolved instrument for EO applications with ten channels in the visible and two channels in the medium infrared. Besides the feature of merging in a single step the acquisition and compression phases of the image generation, its architecture allows to reach a superresolution factor of at least 4x4 in the images reconstructed at the end of process. The outcome of the research can open the way to the development of a novel class of EO instruments with improved Ground Sampling Distance (GSD) - with respect to that one provided natively by the number of sensing elements of the detector - and impact EO applications thanks to native compression, on-board processing capabilities and increased GSD.
The validation of the correct functioning of star trackers is a crucial step for the success of any space mission. In recent years, there has been an increasing interest for the development of miniaturized, light-weighted test systems to be installed directly on the star tracker, opening the way to the test of completely assembled, on-board attitude sensors in pre-launch environment. Here we present the main technical characteristics of a new prototype, the MINISTAR, recently developed by a consortium of Italian enterprises and the Applied Physics Institute of the National Research Council. Besides its main technical features, here we present the results of a set of tests for its performance evaluation, with particular reference to its radiometric and geometric calibration in the laboratory. The MINISTAR can generate synthetic images of dynamic star fields for the simultaneous test of multiple star trackers (up to three head devices). The generation of dynamic star fields in a realistic scenario also includes large objects, such as the Sun or the Moon, and disturbances (e.g. cosmic rays and stray light effects). Thanks to its reduced dimensions and weight, the MINISTAR is suitable for the installation on the star tracker’s baffle. Special attention has also been paid to the use of materials and technologies that could be compliant for vacuum operation in the future. Thanks to the design and construction of an interchangeable mechanical interface, the MINISTAR is compatible with the majority of star trackers available on the market.
In this paper, we propose an innovative concept for an optical payload for Earth Observation, which operates in the medium infrared, based on two emerging technological approaches: super-resolution and compressive sensing. The aim is to improve payload performances in terms of ground spatial resolution and mitigation of some effects, such as saturation and blooming, that are often a limit for obtaining high quality level products in many application domains, such as the detection and monitoring of fire, lava, and, more generally, hotspots. Both approaches are based on the use of a Spatial Light Modulator (SLM), an optoelectronic device consisting of an array of micro-mirrors electronically actuated. The main advantages of the proposed concept consist in: (1) increased ground spatial resolution with respect to the number of pixels of the detector used; (2) expected mitigation of the blooming and saturation effects of the single pixel when high temperature hotspots are observed; (3) compressed-format capture typical of compressive sensing, which eliminates the need for a separate compression card, saving mass, memory and energy consumption.
Star trackers are electro-optical devices that are used to determine the attitude of satellite platforms. The test of star trackers is typically carried out by means of Optical Ground Support Equipment (OGSE). In this paper we present a new prototype of OGSE, the MINISTAR, recently developed by a consortium of Italian enterprises and the Applied Physics Institute of the National Research Council. The MINISTAR is a miniaturized electro-optical device able to generate synthetic images of dynamic star fields for the simultaneous test of up to three star trackers. Their performance can be evaluated in terms of its optics, electronics and on-board attitude software. The MINISTAR is able to perform a dynamic simulation of the apparent motion of the observed scene in order to test the star tracker in a realistic working scenario. It can be placed directly on the star tracker under test and, thanks to its reduced dimensions and weight, the test and validation phase can be performed while the star tracker is assembled on the satellite platform. The MINISTAR is also able to simulate the presence of large objects, such as the Sun, the Earth and the Moon, custom objects and disturbances like cosmic rays and stray light effects. The prototype has been built paying special attention to the employed materials and technology in order to minimize the weight and to ensure its compatibility with most star tracker models available on the market.
This paper presents the results of a study aimed at investigating the potential of Compressive Sensing (CS) technologies for optical space instruments. Besides assessing the pros and cons for a wide set of proposed instrumental concepts for space applications, the study analyzed in further detail two CS-based instrument concepts, each targeting a specific application: an UV-VIS hyperspectral imager on orbiter for stellar spectro-photometry and a MIR camera for sky observation and real-time detection of Near Earth Objects (NEO). The proposed UV-VIS hyperspectral imager relies on a classical CS approach and addresses the CS reconstruction of the full image in order to implement slitless spectrophotometry of stars. The CS-based MIR camera for NEO detection instead explores a novel approach aiming at information extraction without a prior full reconstruction of the image. Besides outlining the optical design of the instruments, its key elements and a pros and cons analysis of the architecture, this paper presents the performance assessment of these instruments for typical application scenarios by means of simulated data. The results showed that, from the point of view of data reconstruction quality, a good performance can be achieved by the designed instruments in terms of compression ratio (CR) and image reconstruction. In terms of system budgets, the CS architecture offered only some marginal benefits with respect to their traditional counterparts, mainly due to the lack of a compression board. Most advantages are instead provided in terms of downlink requirements and memory buffer.
The Italian Space Agency selected the imaging interferometer ALISEO (Aerospace Leap-frog Imaging Stationary interferometer for Earth Observation) as the main payload for a technological optical mission based on the small satellite MIOsat. The simple design of such an instrument, based on Sagnac configuration, makes it a promising for Earth observation missions.
The ALISEO instrument acquires an image of 10 Km by 10 Km with a spatial resolution better than 10 m and a spectral resolution of 200 cm-1 (7 nm @ 0.6 μm) in the 0.4 – 1 μm spectral range.
ALISEO does not employ any moving part to generate the phase delays between the two interfering beams. The sensor acquires target images modulated by a pattern of autocorrelation functions of the energy coming from each scene pixel, and the resulting fringe pattern remains fixed with respect to the instrument’s field-of-view. The complete interferogram of each target location is retrieved by introducing a relative source-observer motion, which allows any image pixels to be observed under different viewing-angles corresponding to different Optical Path Differences (OPDs).
In this paper various optical configurations are analyzed in order to meet the mission requirements. Optical configurations are discussed taking into account: detector size, spatial resolution, and entrance pupil aperture. The proposed configurations should avoid vignetting, reduce geometric and chromatic aberrations, and comply with the size and weight constrains requested by space mission. Optical configurations, based on both refractive and reflective focusing elements, are presented and discussed. Finally, some properties pertaining to the selected Sagnac configuration are discussed in conjunction with spectral estimations and data processing.
In this paper we propose an instrument which is based on a similar payload developed in the framework of the MIOSAT mission of the Italian Space Agency. The instrument is designed on the basis of the following goals: low coast, modularity, plug and play capability, and it should have both wide spectral and spatial range coverage. It will be therefore developed following a modular concept in order to achieve a hyperspectral imager working from visible near infrared up to thermal infrared region.
Compressive sensing (sampling) is a novel technology and science domain that exploits the option to sample radiometric and spectroscopic signals at a lower sampling rate than the one dictated by the traditional theory of ideal sampling. In the paper some general concepts and characteristics regarding the use of compressive sampling in instruments devoted to Earth observation is discussed. The remotely sensed data is assumed to be constituted by sampled images collected by a passive device in the optical spectral range from the visible up to the thermal infrared, with possible spectral discrimination ability, e.g. hyperspectral imaging. According to recent investigations, compressive sensing necessarily employs a signal multiplexing architecture, which in spite of traditional expectations originates a significant SNR disadvantage.
Topical studies [1-5] demonstrate that signal acquisition can be performed at sampling frequencies far below the minimal frequency dictated by the ideal sampling theorem, a concept called "compressive sampling" (CS). This technique can be applied to signals that don't convey the entire information amount predicted by the traditional sampling theory, regardless of the maximum frequency contained in their spectrum. Signals with this intriguing characteristic are called sparse.
Striping noise is a phenomenon intrinsic to the process of image acquisition by means of scanning or pushbroom systems, caused by a poor radiometric calibration of the sensor. Although in-flight calibration has been performed, residual spatially and spectrally coherent noise may perturb the quantitative analysis of images and the extraction of physical parameters.
Destriping methods can be classified in three main groups: statistical-based methods, digital-filtering methods and radiometric-equalisation methods. Their performances depend both on the scene under investigation and on the type and intensity of noise to be treated. Availability of simulated data at each step of the digital image formation process, including that one before the introduction of the striping effect, is particularly useful since it offers the opportunity to test and adjust a variety of image processing and calibration algorithms.
This paper presents the performance of a statistical-based destriping method applied to a set of simulated and to images acquired by the EO-1 Hyperion hyperspectral sensor. The set of simulated data with different intensities of coherent and random noise was generated using an image simulator implemented for the PRISMA mission.
Algorithm’s performance was tested by evaluating most commonly used quality indexes. For the same purpose, a statistical evaluation based on image correlation and image differences between the corrected and ideal images was carried out. Results of the statistical analysis were compared with the outcome of the quality indexes-based analysis.
PRISMA is an Earth observation system that combines a hyperspectral sensor with a panchromatic, medium-resolution camera. OPTIMA is one of the five independent scientific research projects funded by the Italian Space Agency in the framework of PRISMA mission for the development of added-value algorithms and advanced applications. The main goal of OPTIMA is to increase and to strengthen the applications of PRISMA through the implementation of advanced methodologies for the analysis, integration and optimization of level 1 and 2 products. The project is comprehensive of several working packages: data simulation, data quality, data optimization, data processing and integration and, finally, evaluation of some applications related to natural hazards. Several algorithms implemented during the project employ high-speed autonomous procedures for the elaboration of the upcoming images acquired by PRISMA. To assess the performances of the developed algorithms and products, an end-to-end simulator of the instrument has been implemented. Data quality analysis has been completed by introducing noise modeling. Stand-alone procedures of radiometric and atmospheric corrections have been developed, allowing the retrieval of at-ground spectral reflectance maps. Specific studies about image enhancement, restoration and pan-sharpening have been carried out for providing added-value data. Regarding the mission capability of monitoring environmental processes and disasters, different techniques for estimating surface humidity and for analyzing burned areas have been investigated. Finally, calibration and validation activities utilizing the CAL/VAL test site managed by CNR-IFAC and located inside the Regional Park of San Rossore (Pisa), Italy have been considered.
This paper presents an analysis of the main artifacts introduced by the non- uniformity of the instrumental characteristics in an image dataset simulated by taking into account the main technical features of the FLORIS sensor. The dataset was produced by using a hyperspectral image simulation tool – named FLISM (Fluorescence Image Simulator for space Missions) – specifically implemented to produce images of fluorescent and non-fluorescent targets acquired by a pushbroom hyperspectral instrument. In this specific case, the available technical specifications of the FLORIS sensor were taken into account to investigate some critical issues concerning Solar Induced Fluorescence (SIF) retrieval in vegetated areas by means of the FLD (Fraunhofer Line Discriminator) method, which relies on the telluric O2-A and O2-B lines to decouple the weak SIF signal of vegetation from the backscattered radiance.
OPTIMA (“Advanced methods for the analysis, integration and optimization of PRISMA mission level 1 and 2 products”) is one of the five independent scientific research projects funded by the Italian Space Agency to study the applications and performances of the imaging spectrometer and the panchromatic camera of the PRISMA mission. One of the main tasks of the project is the implementation of advanced autonomous techniques for radiometric calibration and atmospheric corrections. Besides, in the framework of the project, a sensor data simulator has been developed to test data processing algorithms. In this paper we discuss the optimized destriping procedure and the autonomous algorithm developed for the correction of the atmospheric effects. The developed procedures provides refined at sensor radiance and at-ground spectral reflectance images. Results from simulated images are presented and discussed.
Temperature and Emissivity Separation (TES) applied to multispectral or hyperspectral Thermal Infrared (TIR) images of the Earth is a relevant issue for many remote sensing applications. The TIR spectral radiance can be modeled by means of the well-known Planck’s law, as a function of the target temperature and emissivity. The estimation of these target's parameters (i.e. the Temperature Emissivity Separation, aka TES) is hindered by the circumstance that the number of measurements is less than the unknown number. Existing TES algorithms implement a temperature estimator in which the uncertainty is removed by adopting some a priori assumption that conditions the retrieved temperature and emissivity. Due to its mathematical structure, the Maximum Entropy formalism (MaxEnt) seems to be well suited for carrying out this complex TES operation. The main advantage of the MaxEnt statistical inference is the absence of any external hypothesis, which is instead characterizes most of the existing the TES algorithms. In this paper we describe the performance of the MaxEnTES (Maximum Entropy Temperature Emissivity Separation) algorithm as applied to ten TIR spectral channels of a MIVIS dataset collected over Italy. We compare the temperature and emissivity spectra estimated by this algorithm with independent estimations achieved with two previous TES methods (the Grey Body Emissivity (GBE), and the Model Emittance Calculation (MEC)). We show that MaxEnTES is a reliable algorithm in terms of its higher output Signal-to-Noise Ratio and the negligibility of systematic errors that bias the estimated temperature in other TES procedures.
This study evaluates the performances of different algorithms for the retrieval of solar induced fluorescence of vegetation
in both the telluric O2-A and O2-B bands of the atmospheric molecular oxygen, respectively at 760 nm and 687 nm. In particular, we evaluated the performances of three algorithms amongst those already applied by the scientific
community: two of them are based on the use of two or three spectral bands (sFLD and 3FLD methods), while the third
one exploits the information content of all the spectral channels in certain bands by applying a polynomial model for
fluorescence and reflectance (SFM method). These were applied to a synthetic set of fluorescence data corresponding to
different types of vegetation. The main technical specifications of the spectroradiometer have been outlined in terms of
three different airborne operating scenarios, addressing different flight altitudes and speeds chosen on the basis of typical
platforms suitable for operation from low-medium altitudes. The results underline that the high spectral resolution of the
instrument plays a fundamental role for the determination of the value of fluorescence with a good precision and
accuracy, as expected. Nevertheless, the extraction of the value of fluorescence in the O2-A band is less critical than in
the O2-B band and, specifically, it is less sensitive to the spectral resolution of the spectroradiometer. Even at low
spectral resolutions, however, the retrieval algorithms based on polynomial fitting provided better results than methods
based on the use of spectral bands.
Compressive sensing (CS) is a new technology that investigates the chance to sample signals at a lower rate than the
traditional sampling theory. The main advantage of CS is that compression takes place during the sampling phase,
making possible significant savings in terms of the ADC, data storage memory, down-link bandwidth, and electrical
power absorption. The CS technology could have primary importance for spaceborne missions and technology, paving
the way to noteworthy reductions of payload mass, volume, and cost. On the contrary, the main CS disadvantage is made
by the intensive off-line data processing necessary to obtain the desired source estimation. In this paper we summarize
the CS architecture and its possible implementations for Earth observation, giving evidence of possible bottlenecks
hindering this technology. CS necessarily employs a multiplexing scheme, which should produce some SNR
disadvantage. Moreover, this approach would necessitate optical light modulators and 2-dim detector arrays of high
frame rate. This paper describes the development of a sensor prototype at laboratory level that will be utilized for the
experimental assessment of CS performance and the related reconstruction errors. The experimental test-bed adopts a
push-broom imaging spectrometer, a liquid crystal plate, a standard CCD camera and a Silicon PhotoMultiplier (SiPM)
matrix. The prototype is being developed within the framework of the ESA ITI-B Project titled “Hyperspectral Passive
Satellite Imaging via Compressive Sensing”.
In this paper we present the spectral behavior of the Solar Induced Fluorescence (SIF) of a crude oil, retrieved from its
radiance spectrum acquired in eight selected spectral windows of the visible spectrum from about 389 nm to 659 nm.
Each spectral window was chosen to cover one or more solar Fraunhofer Lines (FL) so as to retrieve the in-filling due to
the oil fluorescence contribution induced by the solar irradiance. The selected Fraunhofer lines were chosen within the
solar lines rather than the telluric ones since the former ones offer several advantages for the application from air- or
space-borne platform. Solar FL, compared with telluric ones, require a simpler atmospheric model to evaluate ground
solar irradiance. Besides, the signal measured at the sensor is not affected by re-absorption effects. For each spectral
window, oil fluorescence contribution and reflectance were evaluated by comparing the measured total radiance of the
oil with the incident sun irradiance spectrum measured in the same conditions. Fluorescence and reflectance spectral
shapes were evaluated within each measured spectral window by applying a spectral fitting method (SFM) and
polynomial modeling. Solar-induced fluorescence data were then used to evaluate the fluorescence spectrum of the oil.
The SIF spectrum of the same oil was also simulated by using emission-excitation fluorescence data and a simulated
solar irradiance. The measured and simulated spectra were then compared.
The Aerospace Leap-frog Imaging Stationary interferometer for Earth Observation (ALISEO) is a hyperspectral imaging interferometer for Earth remote sensing. The instrument belongs to the class of Sagnac stationary interferometers and acquires the image of the target superimposed to the pattern of autocorrelation functions of the electromagnetic field coming from each pixel. The ALISEO sensor together with the data processing algorithms that retrieve the at-sensor spectral radiance are discussed. A model describing the instrument OPD and interferogram center is also discussed, improving the procedures for phase retrieval and spectral estimation. Images acquired by ALISEO are shown, and examples of retrieved reflectance spectra are presented.
The use of high-resolution imagers for determination of solar-induced fluorescence of natural bodies by observing the infilling
of Fraunhofer lines has been frequently adopted as a tool for vegetation characterization. The option to perform
those measurements from airborne platforms was addressed in the past. In-field observations gave evidence of the main
requirements for an imaging spectrometer to be used for Sun-induced fluorescence measurements such as high spectral
resolution and fine radiometric accuracy needed to resolve the shape of observed Fraunhofer lines with a high level of
accuracy. In this paper, some solutions for the design of a high spectral resolution push-broom imaging spectrometer for
Sun-induced fluorescence measurements are analysed. The main constraints for the optical design are a spectral
resolution better than 0.01 nm and a wide field of view. Due to the fine instrumental spectral resolution, bidimensional
focal plane arrays characterized by high quantum efficiency, low read-out noise, and high sensitivity are requested. The
development of a lightweight instrument is a benefit for aerospace implementations of this technology. First results
coming from laboratory measurements and optical simulations are presented and discussed taking into account their
feasibility.
McCART is a numerical procedure to solve the radiative transfer equation for light propagation through the atmosphere especially developed to study the effect of the atmosphere on the response of hyperspectral sensors for remote sensing of the earth's surface. McCART is based on a single Monte Carlo simulation run for a reference layered plane non-absorbing atmosphere and a plane ground with uniform reflectance. The spectral response of the sensor for a given distribution of ground reflectance and for a specific profile of scattering and absorption properties of the atmosphere is obtained in a short time from the results of the Monte Carlo simulation, making use of scaling relationships and of symmetry properties. The response includes the effects of adjacent pixels. The results can be used to establish the limits of applicability of approximate algorithms for the processing and analysis of hyperspectral images. The algorithm can be also used to develop procedures of atmospheric compensation.
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