Visible Imaging Spectrometer Research Articles

Experimental Approach to the Selection of the Components in the Minimum Noise Fraction

An experimental method to select the number of principal components in minimum noise fraction (MNF) is proposed to process images measured by imagery sensors onboard aircraft or satellites. The method is based on an experimental measurement by spectrometers in dark conditions from which noise structure can be estimated. To represent typical land conditions and atmospheric variability, a significative data set of synthetic noise-free images based on real Multispectral Infrared and Visible Imaging Spectrometer images is built. To this purpose, a subset of spectra is selected within some public libraries that well represent the simulated images. By coupling these synthetic images and estimated noise, the optimal number of components in MNF can be obtained. In order to have an objective (fully data driven) procedure, some criteria are proposed, and the results are validated to estimate the number of components without relying on ancillary data. The whole procedure is made computationally feasible by some simplifications that are introduced. A comparison with a state-of-the-art algorithm for estimating the optimal number of components is also made.

OMEGA long wavelength channel: Data reduction during non-nominal stages

The Observatoire pour la Minéralogie, l’Eau, les Glaces et l’Activité (OMEGA) instrument is a visible and near-infrared imaging spectrometer on board the European Mars Express (MEx) mission. The on-board calibration (OBC) performed at the beginning of observations on each orbit reveals that the photometric response of the C channel (1.0–2.5 μm) has been very stable since orbit insertion in January 2004. On the contrary the L channel (2.5–5.1 μm) response has varied significantly during the mission, and only orbits for which the response is close to nominal could be used with confidence. The spatial coverage of ice-free surfaces in this wavelength range is consequently limited to only ∼30%, mainly during northern spring and summer. This paper presents the empirical method used to derive new instrumental transfer functions (ITF) for the non-nominal orbits. This method consists of analyzing the variation of the signal between several observations of a same region acquired at nominal and non-nominal calibration states. In the cases where the mineralogy and the atmospheric conditions between the two observations are the same, the variation in reflectance spectra is only due to the ITF variation, which provides a new ITF. We then associate these new ITFs with their corresponding OBCs to model a relationship between both. The resulting model enables us to provide a new ITF for each orbit for which the OBC is available. The new ITFs derived for the entire dataset have been validated (1) through a comparison of the C and L channel global albedo trends and (2) through a comparison of the surface temperatures derived from the L channel with those calculated from the General Circulation Model (GCM) numerical simulation of the LMD released in the Martian Climate Database. The non-nominal data processed with adapted ITFs for orbits up to 3050 increase the non-icy surface coverage of Mars to ∼70% including all seasons.

Hyperspectral Sensing for Assessing Nearshore Water Quality Conditions of Hudson/Raritan Estuary

This study addresses the use of remote sensing hyperspectral data acquired by NASA/Airborne Visible Infra Red Imaging Spectrometer (AVIRIS) to estimate concentrations of optical water quality parameters in the Hudson/Raritan Estuary of New York-New Jersey. Hudson/Raritan estuary is a complex estuarine system where tidal and wind-driven currents are modified by freshwater discharges from the Hudson, Raritan, Hackensack, and Passaic rivers. Hypersectral (high resolution) remote sensing data offers unique advantages for the study of recurrent hydrological phenomena on regional and local scales. The paper describes the bio-optical water quality model constructed linking the water constituent concentrations to (i) the inherent optical properties (IOPs), using the specific inherent optical properties (SIOP), and (ii) to the subsurface light levels. Generation of accurate reflecta nce R(0) from radiance recorded by the AVIRIS is the key parameter for input into inverse modeling for estimation of constituent concent rations. In conjunction with bio-optical model, the linear matrix inversion technique is used for retrieval of estuarine water constituents in terms of chlorophyll, colored dissolved organic matter and inorganic material. The long term goal is to establish a monitoring/management system for retrieval of estuarine water constituents using remotely sensed data. Such efforts are complementary to the development of spectral library for detec tion/identification of harmful algal blooms (HABs) causing eutrophication and pollutions in the world’s coastal and estuarine waters. Results of AVIRIS data analyses in forms of thematic maps of concentrations of water quality parameters can be integrated into the Geographic Information Systems (GIS) of the estuary. GIS is a computer-based geospatial database which can be used as a management tool for monitoring of water quality conditions of the estuary.

Road Asphalt Pavements Analyzed by Airborne Thermal Remote Sensing: Preliminary Results of the Venice Highway.

This paper describes a fast procedure for evaluating asphalt pavement surface defects using airborne emissivity data. To develop this procedure, we used airborne multispectral emissivity data covering an urban test area close to Venice (Italy).For this study, we first identify and select the roads' asphalt pavements on Multispectral Infrared Visible Imaging Spectrometer (MIVIS) imagery using a segmentation procedure. Next, since in asphalt pavements the surface defects are strictly related to the decrease of oily components that cause an increase of the abundance of surfacing limestone, the diagnostic absorption emissivity peak at 11.2μm of the limestone was used for retrieving from MIVIS emissivity data the areas exhibiting defects on asphalt pavements surface.The results showed that MIVIS emissivity allows establishing a threshold that points out those asphalt road sites on which a check for a maintenance intervention is required. Therefore, this technique can supply local government authorities an efficient, rapid and repeatable road mapping procedure providing the location of the asphalt pavements to be checked.

Recent changes in macrophyte colonisation patterns: an imaging spectrometry-based evaluation of southern Lake Garda (northern Italy)

Temporal variation in the extent of submerged macrophytes along the littoral zone of Sirmione Peninsula in the southern part of Lake Garda (Northern Italy) was investigated using imaging spectrometry. Two images, with a spatial resolution of 5 m were acquired by the Multispectral Infrared and Visible Imaging Spectrometer (MIVIS) in the summers of 1997 and 2005. Image data were first geocoded and then corrected for both atmospheric and skylight reflection effects at the water surface using the 6S radiative transfer code. The two images were inverted using a bio-optical model, which was parameterised with the inherent optical properties of the lake. The inversion utilized the spectral range from 0.48-0.60 µm because it simultaneously provided the lowest environmental noise and the best atmospheric correction performances for the two scenes and produced images of bottom depth and of two substrate classes: bare sand and submerged vegetation, representing a mixture of valuable freshwater species. The MIVIS-derived bottom depth ranges and patterns were comparable to a bathymetry chart with a deviation less than 5%. In 2005, the image was consistent with contemporaneous in-situ derived knowledge on macrophyte distribution. In 1997, the substrate image map was deemed reasonable with respect to the macrophyte distribution documented in 2000. The comparison of the substrate products for the two dates showed a marked decrease in macrophyte beds, with a concomitant increase in sandy substrates. In the 8-year interval the extent of submerged macrophyte decreased from 72% to 52%. We expect that this study will contribute to increased knowledge of macrophyte colonisation patterns of the Sirmione Peninsula, where, despite their ecological significance, changes have been poorly documented.

Estimation of leaf and canopy water content in poplar plantations by means of hyperspectral indices and inverse modeling

This study investigates the applicability of empirical and radiative transfer models to estimate water content at leaf and landscape level. The main goal is to evaluate and compare the accuracy of these two approaches for estimating leaf water content by means of laboratory reflectance/transmittance measurements and for mapping leaf and canopy water content by using airborne Multispectral Infrared and Visible Imaging Spectrometer (MIVIS) data acquired over intensive poplar plantations (Ticino, Italy). At leaf level, we tested the performance of different spectral indices to estimate leaf equivalent water thickness (EWT) and leaf gravimetric water content (GWC) by using inverse ordinary least squares (OLS) regression, and reduced major axis (RMA) regression. The analysis showed that leaf reflectance is related to changes in EWT rather than GWC, with best results obtained by using RMA regression by exploiting the spectral index related to the continuum removed area of the 1200 nm water absorption feature with an explained variance of 61% and prediction error of 6.6%. Moreover, we inverted the PROSPECT leaf radiative transfer model to estimate leaf EWT and GWC and compared the results with those obtained by means of empirical models. The inversion of this model showed that leaf EWT can be successfully estimated with no prior information with mean relative errors of 14% and determination coefficient of 0.65. Inversion of the PROSPECT model showed some difficulties in the simultaneous estimation of leaf EWT and dry matter content, which led to large errors in GWC estimation. At landscape level with MIVIS data, we tested the performance of different spectral indices to estimate canopy water per unit ground area (EWT canopy). We found a relative error of 20% using a continuum removed spectral index around 1200 nm. Furthermore, we used a model simulation to evaluate the possibility of applying empirical models based on appositely developed MIVIS double ratios to estimate mean leaf EWT at landscape level ( EWT ― ). It is shown that combined indices (double ratios) yielded significant results in estimating leaf EWT at landscape level by using MIVIS data (with errors around 2.6%), indicating their potential in reducing the effects of LAI on the recorded signal. The accuracy of the empirical estimation of EWT canopy and EWT ― was finally compared with that obtained from inversion of the PROSPECT + SAILH canopy reflectance model to evaluate the potential of both methods for practical applications. A relative error of 27% was found for EWT canopy and an overestimation of leaf EWT ― with relative errors around 19%. Results arising from this remote sensing application support the robustness of hyperspectral regression indices for estimating water content at both leaf and landscape level, with lower relative errors compared to those obtained from inversion of leaf and 1D canopy radiative transfer models.

Remote hyperspectral imagery as a support to archaeological prospection

Hyperspectral data were exploited to test their effectiveness as a tool for archaeological prospection, envisaging their potential for detecting spectral anomalies related to buried archaeological structures. For this purpose the airborne Multispectral Infrared and Visible Imaging Spectrometer images were analysed. Each single band of the entire data set and different processing technique products were interpreted to identify any tonal anomalies. Since every analysed image exhibited marks different in terms of size and intensity, two indexes were defined for assessing the potential of anomalies detection of each image. Such parameters were: the Detection Index, used for counting the number of pixels related in each image to marks, and the Separability Index, applied for measuring the tonal difference of the marks with respect to the background. These indexes were tested on two areas within the Selinunte Archaeological Park where the presence of remains, not yet excavated, was supposed by archaeologists. For the test sites any extracted anomalies were evaluated by an expert in order to determine their archaeological relevance. The comparison among the index values, derived from each single band of the spectrometer and from different image processing by-products, allowed to determine which spectral range and which processing method are the most valuable to quickly highlight the anomalies. The analysis pointed out that, where vegetation cover is dominant, the Visible near infrared is the spectral region more sensitive to variations of spectral properties related to buried structures, while, where soil cover becomes relevant, the Short-wave infrared and the Thermal-infrared regions resulted more sensitive. As far as the applied processing methods are concerned, the Spectral Angle Mapper classifier and, secondly, the Minimum Distance algorithm stressed the highest archaeological information content. The results of this work showed that the archaeological information content derived by analysing the outputs of the applied image processing techniques is more significant than the information obtained by interpreting each single band and the available historical aerial photos. As a final remark, the data processing flow chart, applied to the entire remote hyperspectral data set over Selinunte Archaeological Park, appeared encouraging for detection of anomalies related to the presence of the buried archaeological structures.

Tree species mapping with Airborne hyper‐spectral MIVIS data: the Ticino Park study case

The present work describes the procedure, which was studied for mapping the spatial distribution of tree forest communities in the Ticino Park located in Northern Italy. Ten overlapping airborne runs of the Multispectral Infrared Visible Imaging Spectrometer (MIVIS) were acquired to cover the entire park extent (920 km2). An integrated supervised classification procedure was developed using band ratios in the red edge portion (REP) of the spectrum and training collected by field survey and visual interpretation. Validation performed with a robust random stratified sampling scheme and taking into account the unequal distribution of the classes showed that, on large‐scale application, high‐resolution remotely sensed images can generate, in a cost‐effective manner, accurate (overall accuracy 75%) local‐scale thematic products.

Snapshot dual-band visible hyperspectral imaging spectrometer

We describe a proof of concept for a snapshot dual-band visible hyperspectral imaging spectrometer. A commercially available digital camera was integrated into a computed tomographic imaging spectrometer to provide a means for this proof of concept. Two spatially coregistered data cubes covering the blue (400 to 500 nm) and red (600 to 700 nm) spectral regions were reconstructed and the results analyzed. We found that the system accurately reconstructs the spectral content of the scene and that the two data cubes are automatically spatially coregistered by virtue of the system design.

Statistical Analysis of Hyper-Spectral Data: A Non-Gaussian Approach

We investigate the statistical modeling of hyper-spectral data. The accurate modeling of experimental data is critical in target detection and classification applications. In fact, having a statistical model that is capable of properly describing data variability leads to the derivation of the best decision strategies together with a reliable assessment of algorithm performance. Most existing classification and target detection algorithms are based on the multivariate Gaussian model which, in many cases, deviates from the true statistical behavior of hyper-spectral data. This motivated us to investigate the capability of non-Gaussian models to represent data variability in each background class. In particular, we refer to models based on elliptically contoured (EC) distributions. We consider multivariate EC-t distribution and two distinct mixture models based on EC distributions. We describe the methodology adopted for the statistical analysis and we propose a technique to automatically estimate the unknown parameters of statistical models. Finally, we discuss the results obtained by analyzing data gathered by the multispectral infrared and visible imaging spectrometer (MIVIS) sensor.

Characterization and Mapping of Fuel Types for the Mediterranean Ecosystems of Pollino National Park in Southern Italy by Using Hyperspectral MIVIS Data

Abstract The characterization and mapping of fuel types is one of the most important factors that should be taken into consideration for wildland fire prevention and prefire planning. This research aims to investigate the usefulness of hyperspectral data to recognize and map fuel types in order to ascertain how well remote sensing data can provide an exhaustive classification of fuel properties. For this purpose airborne hyperspectral Multispectral Infrared and Visible Imaging Spectrometer (MIVIS) data acquired in November 1998 have been analyzed for a test area of 60 km2 selected inside Pollino National Park in the south of Italy. Fieldwork fuel-type recognitions, performed at the same time as remote sensing data acquisition, were used as a ground-truth dataset to assess the results obtained for the considered test area. The method comprised the following three steps: 1) adaptation of Prometheus fuel types for obtaining a standardization system useful for remotely sensed classification of fuel types and properties in the considered Mediterranean ecosystems; 2) model construction for the spectral characterization and mapping of fuel types based on a maximum likelihood (ML) classification algorithm; and 3) accuracy assessment for the performance evaluation based on the comparison of MIVIS-based results with ground truth. Results from our analysis showed that the use of remotely sensed data at high spatial and spectral resolution provided a valuable characterization and mapping of fuel types being that the achieved classification accuracy was higher than 90%.

Multiscale fuel type mapping in fragmented ecosystems: preliminary results from hyperspectral MIVIS and multispectral Landsat TM data

This study aims to ascertain how well remote sensing data can characterize fuel type at different spatial scales in fragmented ecosystems. For this purpose, multisensor and multiscale remote sensing data such as hyperspectral Multispectral Infrared and Visible Imaging Spectrometer (MIVIS) and Landsat Thematic Mapper (TM) data acquired in 1998 were analysed for a test area in southern Italy characterized by mixed vegetation covers and complex topography. Fieldwork fuel type recognition, performed at the same time as remote sensing data acquisitions, was used to assess the results obtained for the considered test areas. Results from preliminary analysis showed that the use of unmixing techniques allows an increase in accuracy of around 7% compared with the accuracy level obtained by applying a widely used classification algorithm.

The development of Superspectral approaches for the improvement of land cover classification

This paper develops a critical review of the hyperspectral splitting of the solar reflected radiation acquired by hyperspectral imaging sensors. The bandwidth used in the range from 2.0-2.5 /spl mu/m by many hyperspectral sensors sometimes is too narrow for land cover classification. In fact, hyperspectral imagers often suffer from low signal-to-noise (SNR) in the shortwave infrared region of the electromagnetic spectrum, resulting in noisy image collection. This paper presents a new methodological approach to the splitting of the solar reflected radiation, called the It is based on the principle of increasing the channel bandwidth by increasing the number of wavelengths, to build synthetic spectral bands with higher SNR. The methodology has been applied to the Multispectral Infrared and Visible Imaging Spectrometer (MIVIS) sensor, an airborne hyperspectral scanner used for environmental remote sensing applications in Italy. Interesting results have been achieved in crop classification, processing the Cordenons survey carried out in August 2001 in the northeastern part of Italy. The Spectral Angle Mapper algorithm was used for classification because it is insensitive to shadows. For accuracy assessment, the overall accuracy (OA) and kappa coefficient (k) were calculated and used in the comparison. Using the superspectral approach, an increment in the overall accuracy of about 42% and an increment in the kappa coefficient of about 51% were obtained in comparison to the classification accuracy of unprocessed original MIVIS data (OA=41.21, k=0.35). A second case study is presented using the National Aeronautics and Space Administration's experimental hyperspectral imager HYPERION. Data acquired over the lake of Garda (Italy) in October 2002 was processed with the superspectral approach. Comparing the simulated HYPERION superspectral bands with the original data, SNR improvements are achieved in the shortwave infrared region (from 0.7-54.2 for 2.012-/spl mu/m wavelength and from 0.7-64.5 for the 2.365-/spl mu/m wavelength). The methodology proposed is sensor independent and can be applied to any of the hyperspectral sensors currently available.

Inspecting MIVIS capability to retrieve chemical–mineralogical information: evaluation and analysis of VNIR–SWIR data acquired on a volcanic area

This work is a contribution to the assessment of MIVIS (Multi-spectral Infrared and Visible Imaging Spectrometer) airborne imaging spectrometer capability in applications of surface characterization. The focus is on the visible and near-infrared–short wave infrared (VNIR–SWIR) spectral region, using a dataset acquired in 1994 on Vulcano Island (Italy), to retrieve chemical–mineralogical information on the altered deposits related to volcanic activity. The main processing steps include data quality evaluation in terms of signal-to-noise ratio, atmospheric and topographic corrections and spectral interpretation of the image. Estimation of surface reflectance is based on atmospheric modelling by MODTRAN3.5 and 6S radiative transfer codes. Representative MIVIS reflectance spectra of the main surface units are compared with spectra measured in the laboratory on field samples, and interpreted to characterize the mineralogy on the basis of their spectral features. A thematic map of the main alteration units is then produced by applying spectral mapping techniques to the surface reflectance image, using a set of channels selected on the basis of their data quality and image-derived end-member spectra.

A new algorithm for temperature and spectral emissivity retrieval over active fires in the TIR spectral range

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.

Effects of light pollution revealed during a nocturnal aerial survey by two hyperspectral imagers.

A remote-sensing campaign was performed in September 2001 at nighttime under clear-sky conditions before moonrise to assess the level of light pollution of urban and industrial origin. Two hyperspectral sensors, namely, the Multispectral Infrared and Visible Imaging Spectrometer and the Visible Infrared Scanner-200, which provide spectral coverage from the visible to the thermal infrared, were flown over the Tuscany coast (Italy) on board a Casa 212 airplane. The acquired images were processed to produce radiometrically calibrated data, which were then analyzed and compared with ground-based spectral measurements. Calibrated data acquired at high spectral resolution (approximately 2.5 nm) showed a maximum scene brightness almost of the same order of magnitude as that observed during similar daytime measurements, whereas their average luminosity was 3 orders of magnitude lower. The measurement analysis confirmed that artificial illumination hinders astronomical observations and produces noticeable effects even at great distances from the sources of the illumination.

Will ASTER see the Etna SO 2 plume?

The aim of this paper is to show the feasibility of evaluating the sulphur dioxide (SO 2 ) flux emitted by Mt Etna volcano, using remotely sensed data of Advanced Spaceborne Thermal Emission and Reflection radiometer (ASTER) in the thermal infrared (TIR). ASTER is one of the instruments on board the first Earth Observation System (EOS) satellite launched on 18 December 1999. To simulate the ASTER data, Multispectral Infrared and Visible Imaging Spectrometer (MIVIS) images, acquired during the 'Sicily '97' campaign, were used. The SO 2 column abundance maps shown in this paper have been obtained by means of a simple algorithm based on the split-window technique. The parameters of the algorithm were computed using Moderate Resolution Atmospheric Radiance and Transmittance Model (MODTRAN) simulations performed with atmospheric profiles acquired during MIVIS flights. The mean SO 2 flux on 12 and 16 June 1997, obtained using the ASTER simulated images, results in 42 and 33 kg s m 1 respectively; these values are in good agreement with the results obtained by the inversion procedure used by Teggi et al . (1999) applied to the MIVIS data and with the Correlation Spectrometer (COSPEC) measurements performed during the campaign by other teams.

Selected configuration tradeoffs of contour optical instruments

The Comet Nucleus Tour (CONTOUR) is a low-cost NASA Discovery mission designed to conduct three close flybys of comet nuclei. Selected configuration tradeoffs conducted to balance science requirements with low mission cost are reviewed. The tradeoffs discussed focus on the optical instruments and related spacecraft considerations. Two instruments are under development. The CONTOUR Forward Imager (CFI) is designed to perform optical navigation, moderate resolution nucleus/jet imaging, and imaging of faint molecular emission bands in the coma. The CONTOUR Remote Imager and Spectrometer (CRISP) is designed to obtain high-resolution multispectral images of the nucleus, conduct spectral mapping of the nucleus surface, and provide a backup optical navigation capability. Tradeoffs discussed are: (1) the impact on the optical instruments of not using reaction wheels on the spacecraft, (2) the improved performance and simplification gained by implementing a dedicated star tracker instead of including this function in CFI, (3) the improved flexibility and robustness of switching to a low frame rate tracker for CRISP, (4) the improved performance and simplification of replacing a visible imaging spectrometer by enhanced multispectral imaging in CRISP, and (5) the impact on spacecraft resources of these and other tradeoffs.

Estimation of SO2 abundance in the eruption plume of Mt. Etna using two MIVIS thermal infrared channels: a case study from the Sicily-1997 Campaign

In this paper, an algorithm is developed based on the split-window technique, to estimate the SO2 abundance in the plume of Mt. Etna volcano using the multispectral infrared and visible imaging spectrometer (MIVIS). The MIVIS data were remotely sensed in the thermal infrared (TIR) during the Sicily-1997 Campaign. In this study, the MODTRAN 3.5 code has been used to simulate the radiance at the sensor; the radiative transfer model was input along with the data of radio-sounding performed simultaneously with the MIVIS flight using a mobile radio-theodolite. From the SO2 map, derived from the MIVIS image, the SO2 flux along the axis of the plume was computed knowing the wind speed at the plume altitude. The SO2 flux is variable along the plume axis. The average SO2 flux (about 45 kg s–1 on 12 June and about 30 kg s–1 on 16 June) emitted from the vents is compared with the correlation spectrometer (COSPEC) measurements carried out by other teams (from the ground and from a light aircraft flying under the plume) during the MIVIS flight. Finally, by means of this algorithm it should be easier, with respect to the previously described procedure to monitor the SO2 flux of a specific volcano such as Mt. Etna.

Infrared detection of active fires and burnt areas: theory and observations

We have investigated the problem of fire recognition and management using hyperspectral images remotely sensed by the multispectral infrared and visible imaging spectrometer (MIVIS). Twenty near infrared bands (between 1.99 and 2.48 μm) and ten thermal infrared (between 8.2 and 12.7 μm) MIVIS bands have been utilised. The main problems addressed in this work are the fire-front and burned-area recognition and the extraction of information useful for the management of the burned area. The paper shows data gathered by the sensor MIVIS over the fire, the data processing result and is completed with a brief theoretical discussion of the involved topics. Some hints are given about the diagnostic capabilities of other hyperspectral devices.