Alessandro Barducci - Academia.edu (original) (raw)

Papers by Alessandro Barducci

Research paper thumbnail of Detection of linear geological features (jointing) by Hough Transform of multispectral remotely sensed images

Proceedings of SPIE, Mar 14, 2003

This paper discusses the recognition of geological lineaments seen in multispectral images of the... more This paper discusses the recognition of geological lineaments seen in multispectral images of the Earth collected at optical wavelengths. The possibility of discerning jointing of rocks from natural and artificial image objects is thoroughly discussed. This problem is addressed by using an original image classification algorithm that enables us to detect urban areas and rivers. Once that a reliable sub image freed from possible artefact causes is isolated the true analysis of linear features is performed. This analysis takes advantage from the use of the Hough transform, in order to carry out the automatic identification of linear features and their analysis. The performance of the algorithm has been investigated by processing high resolution aerial photogrammetry and Thematic Mapper images. Tests as far executed have shown a good ability of the algorithm to accurately map image spatial features with linear morphology, and very rare occurrence of mix-up with different image objects.

Research paper thumbnail of Developing a new hyperspectral imaging interferometer for earth observation

Optical Engineering, Jun 14, 2012

Research paper thumbnail of ESA Sen3Exp Campaign: San Rossore Coastal Zone Monitoring by CHRIS/PROBA-1

Research paper thumbnail of CHRIS Performance Evaluation: Signal-To-Noise Ratio, Instrument Efficiency and Data Quality from Acquisitions over San Rossore (Italy) Test Site

ESA Special Publication, Jun 1, 2005

Research paper thumbnail of Optical design constrains in triangular Sagnac imaging interferometers for earth observation

Research paper thumbnail of Striping noise mitigation: performance evaluation on real and simulated hyperspectral images

Proceedings of SPIE, Oct 15, 2015

Striping noise is a phenomenon intrinsic to the process of image acquisition by means of scanning... more 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.

Research paper thumbnail of The OPTIMA project: data simulation and correction procedures for PRISMA mission products

Proceedings of SPIE, Oct 7, 2014

OPTIMA (“Advanced methods for the analysis, integration and optimization of PRISMA mission level ... more 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.

Research paper thumbnail of High spectral resolution imager for solar induced fluorescence observation

Proceedings of SPIE, Oct 6, 2011

The use of high-resolution imagers for determination of solar-induced fluorescence of natural bod... more 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

Research paper thumbnail of McCART: Monte Carlo Code for Atmospheric Radiative Transfer

IEEE Transactions on Geoscience and Remote Sensing, 2008

Research paper thumbnail of Emissivity spectra estimated with the MaxEnTES algorithm

Proceedings of SPIE, Nov 11, 2014

Temperature and Emissivity Separation (TES) applied to multispectral or hyperspectral Thermal Inf... more 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.

Research paper thumbnail of Assessment of quality parameters for a new-generation hyperspectral imager

Proceedings of SPIE, Oct 5, 2007

This work focuses on an assessment of quality parameters characterizing a hyperspectral image col... more This work focuses on an assessment of quality parameters characterizing a hyperspectral image collected by a new-generation high-resolution sensor named Hyper-SIMGA, which is a spectrometer operating in the push-broom configuration. By resorting to Shannon's information theory, the concept of quality is related to the information conveyed to a user by the hyperspectral data, which can be objectively defined from both

Research paper thumbnail of Blind atmospheric correction of hyperspectral images gathered by high spectral resolution sensors

ABSTRACT In this paper we discuss the structure and the performance of a blind (autonomous) atmos... more ABSTRACT In this paper we discuss the structure and the performance of a blind (autonomous) atmospheric correction procedure that can be adopted for compensating the radiative effects originated by the transfer of radiation throughout the atmosphere. The algorithm is tested with simulated hyperspectral images for the sensors HyperSymga and Prisma.

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

Proceedings of SPIE, Oct 6, 2005

ABSTRACT Using several hyperspectral images acquired by various aerospace sensors over San Rossor... more 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.

Research paper thumbnail of An atmospheric correction iterative method for very high resolution aerospace imaging spectrometers

Proceedings of SPIE, Oct 5, 2007

Increasing the radiometric accuracy and spectral resolution of aerospace optical imagers for Eart... more Increasing the radiometric accuracy and spectral resolution of aerospace optical imagers for Earth observation may allow enhanced results many remote sensing applications. This demand for accurate radiometric calibration and requests that atmospheric effects are carefully accounted for. Obtaining surface reflectance maps from the at-sensor radiance images requires improved atmospheric correction procedures. Based on the availability of data acquired at so high spectral resolution to allow the detection of different spectral features of some atmospheric constituents, an iterative estimation algorithm has been developed.. The default atmospheric profiles available in MODTRAN 4 have been firstly refined through at-ground level measurements of some parameters, like temperature, pressure, humidity. The algorithm uses the results of MODTRAN 4 simulations to calculate the apparent reflectance of several image pixels for various abundances of atmospheric constituents. The retrieved reflectance spectra are analysed in order to detect the presence of residual atmospheric absorption features, the amplitude of which is adopted as a score of sub-optimal atmospheric correction. A numerical minimization algorithm then finds the optima atmospheric parameters for the processed scene. Five parameters are estimated using this inversion procedure: visibility, H2O vapour, CO2, CO, and O3. To test and validate the method some images acquired by the new airborne sensor HYPER / SIM-GA on 15th December 2005 during a coastal zone remote sensing campaign have been utilized. Synthetic dataset simulating the above sensor have been employed too. First results are presented and discussed taking into account the feasibility of avoiding in-field measurements.

Research paper thumbnail of The aerospace imaging interferometer ALISEO: further improvements of calibration methods and assessment of interferometer response

Proceedings of SPIE, Oct 5, 2007

Research paper thumbnail of Methodology for at-surface BRDF retrieval from CHRIS-PROBA observations of the San Rossore (Italy) forestry

Proceedings of SPIE, Oct 15, 2004

Bi-Directional Reflectance Distribution Function (BRDF) of natural targets is a relevant topic to... more Bi-Directional Reflectance Distribution Function (BRDF) of natural targets is a relevant topic to many remote sensing applications. Recent satellite sensors such as the Multi-angle Imaging SpectroRadiometer (MISR) and the Compact High Resolution Imaging Spectrometer (CHRIS) supply experimental data to improve the current understanding of directional properties of reflection from natural surfaces. As a technology demonstrator to evaluate the performance of

Research paper thumbnail of Atmospheric effects on hyperspectral data acquired with aerospace imaging spectrometers

Proceedings of SPIE, Mar 21, 2003

Retrieval of reflectance spectra as well as of other level 2 products from hyperspectral remotely... more Retrieval of reflectance spectra as well as of other level 2 products from hyperspectral remotely sensed data demands an accurate analysis of the attenuation and scattering effects due to aerosols and gases distributed in the atmospheric path. Starting from radiometrically corrected data, target reflectance spectra were obtained by solving the radiative transfer equation using a rather simple physical model, which

Research paper thumbnail of ALISEO on MIOSat: an imaging interferometer for earth observation

Research paper thumbnail of Aliseo: An Imaging Interferometer on Micro-Satellite for Earth Observation

Research paper thumbnail of Technological issues in compressive sensing

Compressive sensing (aka compressive sampling or CS) is a new technology field that is characteri... more Compressive sensing (aka compressive sampling or CS) is a new technology field that is characterized by the possibility to sample radiometric and spectroscopic signals at a lower rate without losing significant source / target information. This option is made possible by a specific signal feature that is called sparsity. A sparse signal does not convey the whole information predicted by the traditional sampling theory, irrespective of the maximum frequency contained in its spectrum. The sparse mathematical representation admitted by the signal can be made accessible to an instrument throughout a dedicated integral transformation that would be performed by a specific optical subsystem. This technology belongs to the signal compression domain, and its main advantage is that compression takes place before signal registration, and during the sampling phase. Due to this feature, compressive sensing promises outstanding savings in terms of the ADC specs, required memory for temporary data storage, bandwidth necessary for down-link, and electrical power consumption. The above lesser requirements would originate supplementary reduction of the mass, volume, and cost budgets. The possible impact of these expectations on future space missions could be remarkable, motivating new investigations and research programs concerning this technology. In the paper we review the architecture and possible implementations of CS. The CS application to instruments devoted to Earth observation, and measurement of planetary surfaces will be discussed. The remotely sensed data will be 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. We will examine the main bottlenecks affecting the utilization of CS for Earth observation, and describe a forthcoming ESA ITI-B Project focusing on these topics. We show that a practical implementation of CS demands for optical light modulators and 2dim detector arrays of high frame rate. We will further show that CS necessarily employs a signal multiplexing architecture, which in spite of traditional expectations does not reach the projected SNR advantage.

Research paper thumbnail of Detection of linear geological features (jointing) by Hough Transform of multispectral remotely sensed images

Proceedings of SPIE, Mar 14, 2003

This paper discusses the recognition of geological lineaments seen in multispectral images of the... more This paper discusses the recognition of geological lineaments seen in multispectral images of the Earth collected at optical wavelengths. The possibility of discerning jointing of rocks from natural and artificial image objects is thoroughly discussed. This problem is addressed by using an original image classification algorithm that enables us to detect urban areas and rivers. Once that a reliable sub image freed from possible artefact causes is isolated the true analysis of linear features is performed. This analysis takes advantage from the use of the Hough transform, in order to carry out the automatic identification of linear features and their analysis. The performance of the algorithm has been investigated by processing high resolution aerial photogrammetry and Thematic Mapper images. Tests as far executed have shown a good ability of the algorithm to accurately map image spatial features with linear morphology, and very rare occurrence of mix-up with different image objects.

Research paper thumbnail of Developing a new hyperspectral imaging interferometer for earth observation

Optical Engineering, Jun 14, 2012

Research paper thumbnail of ESA Sen3Exp Campaign: San Rossore Coastal Zone Monitoring by CHRIS/PROBA-1

Research paper thumbnail of CHRIS Performance Evaluation: Signal-To-Noise Ratio, Instrument Efficiency and Data Quality from Acquisitions over San Rossore (Italy) Test Site

ESA Special Publication, Jun 1, 2005

Research paper thumbnail of Optical design constrains in triangular Sagnac imaging interferometers for earth observation

Research paper thumbnail of Striping noise mitigation: performance evaluation on real and simulated hyperspectral images

Proceedings of SPIE, Oct 15, 2015

Striping noise is a phenomenon intrinsic to the process of image acquisition by means of scanning... more 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.

Research paper thumbnail of The OPTIMA project: data simulation and correction procedures for PRISMA mission products

Proceedings of SPIE, Oct 7, 2014

OPTIMA (“Advanced methods for the analysis, integration and optimization of PRISMA mission level ... more 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.

Research paper thumbnail of High spectral resolution imager for solar induced fluorescence observation

Proceedings of SPIE, Oct 6, 2011

The use of high-resolution imagers for determination of solar-induced fluorescence of natural bod... more 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

Research paper thumbnail of McCART: Monte Carlo Code for Atmospheric Radiative Transfer

IEEE Transactions on Geoscience and Remote Sensing, 2008

Research paper thumbnail of Emissivity spectra estimated with the MaxEnTES algorithm

Proceedings of SPIE, Nov 11, 2014

Temperature and Emissivity Separation (TES) applied to multispectral or hyperspectral Thermal Inf... more 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.

Research paper thumbnail of Assessment of quality parameters for a new-generation hyperspectral imager

Proceedings of SPIE, Oct 5, 2007

This work focuses on an assessment of quality parameters characterizing a hyperspectral image col... more This work focuses on an assessment of quality parameters characterizing a hyperspectral image collected by a new-generation high-resolution sensor named Hyper-SIMGA, which is a spectrometer operating in the push-broom configuration. By resorting to Shannon's information theory, the concept of quality is related to the information conveyed to a user by the hyperspectral data, which can be objectively defined from both

Research paper thumbnail of Blind atmospheric correction of hyperspectral images gathered by high spectral resolution sensors

ABSTRACT In this paper we discuss the structure and the performance of a blind (autonomous) atmos... more ABSTRACT In this paper we discuss the structure and the performance of a blind (autonomous) atmospheric correction procedure that can be adopted for compensating the radiative effects originated by the transfer of radiation throughout the atmosphere. The algorithm is tested with simulated hyperspectral images for the sensors HyperSymga and Prisma.

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

Proceedings of SPIE, Oct 6, 2005

ABSTRACT Using several hyperspectral images acquired by various aerospace sensors over San Rossor... more 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.

Research paper thumbnail of An atmospheric correction iterative method for very high resolution aerospace imaging spectrometers

Proceedings of SPIE, Oct 5, 2007

Increasing the radiometric accuracy and spectral resolution of aerospace optical imagers for Eart... more Increasing the radiometric accuracy and spectral resolution of aerospace optical imagers for Earth observation may allow enhanced results many remote sensing applications. This demand for accurate radiometric calibration and requests that atmospheric effects are carefully accounted for. Obtaining surface reflectance maps from the at-sensor radiance images requires improved atmospheric correction procedures. Based on the availability of data acquired at so high spectral resolution to allow the detection of different spectral features of some atmospheric constituents, an iterative estimation algorithm has been developed.. The default atmospheric profiles available in MODTRAN 4 have been firstly refined through at-ground level measurements of some parameters, like temperature, pressure, humidity. The algorithm uses the results of MODTRAN 4 simulations to calculate the apparent reflectance of several image pixels for various abundances of atmospheric constituents. The retrieved reflectance spectra are analysed in order to detect the presence of residual atmospheric absorption features, the amplitude of which is adopted as a score of sub-optimal atmospheric correction. A numerical minimization algorithm then finds the optima atmospheric parameters for the processed scene. Five parameters are estimated using this inversion procedure: visibility, H2O vapour, CO2, CO, and O3. To test and validate the method some images acquired by the new airborne sensor HYPER / SIM-GA on 15th December 2005 during a coastal zone remote sensing campaign have been utilized. Synthetic dataset simulating the above sensor have been employed too. First results are presented and discussed taking into account the feasibility of avoiding in-field measurements.

Research paper thumbnail of The aerospace imaging interferometer ALISEO: further improvements of calibration methods and assessment of interferometer response

Proceedings of SPIE, Oct 5, 2007

Research paper thumbnail of Methodology for at-surface BRDF retrieval from CHRIS-PROBA observations of the San Rossore (Italy) forestry

Proceedings of SPIE, Oct 15, 2004

Bi-Directional Reflectance Distribution Function (BRDF) of natural targets is a relevant topic to... more Bi-Directional Reflectance Distribution Function (BRDF) of natural targets is a relevant topic to many remote sensing applications. Recent satellite sensors such as the Multi-angle Imaging SpectroRadiometer (MISR) and the Compact High Resolution Imaging Spectrometer (CHRIS) supply experimental data to improve the current understanding of directional properties of reflection from natural surfaces. As a technology demonstrator to evaluate the performance of

Research paper thumbnail of Atmospheric effects on hyperspectral data acquired with aerospace imaging spectrometers

Proceedings of SPIE, Mar 21, 2003

Retrieval of reflectance spectra as well as of other level 2 products from hyperspectral remotely... more Retrieval of reflectance spectra as well as of other level 2 products from hyperspectral remotely sensed data demands an accurate analysis of the attenuation and scattering effects due to aerosols and gases distributed in the atmospheric path. Starting from radiometrically corrected data, target reflectance spectra were obtained by solving the radiative transfer equation using a rather simple physical model, which

Research paper thumbnail of ALISEO on MIOSat: an imaging interferometer for earth observation

Research paper thumbnail of Aliseo: An Imaging Interferometer on Micro-Satellite for Earth Observation

Research paper thumbnail of Technological issues in compressive sensing

Compressive sensing (aka compressive sampling or CS) is a new technology field that is characteri... more Compressive sensing (aka compressive sampling or CS) is a new technology field that is characterized by the possibility to sample radiometric and spectroscopic signals at a lower rate without losing significant source / target information. This option is made possible by a specific signal feature that is called sparsity. A sparse signal does not convey the whole information predicted by the traditional sampling theory, irrespective of the maximum frequency contained in its spectrum. The sparse mathematical representation admitted by the signal can be made accessible to an instrument throughout a dedicated integral transformation that would be performed by a specific optical subsystem. This technology belongs to the signal compression domain, and its main advantage is that compression takes place before signal registration, and during the sampling phase. Due to this feature, compressive sensing promises outstanding savings in terms of the ADC specs, required memory for temporary data storage, bandwidth necessary for down-link, and electrical power consumption. The above lesser requirements would originate supplementary reduction of the mass, volume, and cost budgets. The possible impact of these expectations on future space missions could be remarkable, motivating new investigations and research programs concerning this technology. In the paper we review the architecture and possible implementations of CS. The CS application to instruments devoted to Earth observation, and measurement of planetary surfaces will be discussed. The remotely sensed data will be 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. We will examine the main bottlenecks affecting the utilization of CS for Earth observation, and describe a forthcoming ESA ITI-B Project focusing on these topics. We show that a practical implementation of CS demands for optical light modulators and 2dim detector arrays of high frame rate. We will further show that CS necessarily employs a signal multiplexing architecture, which in spite of traditional expectations does not reach the projected SNR advantage.