High Resolution Imaging Spectrometer Research Articles

Real-time full-spectral imaging and affinity measurements from 50 microfluidic channels using nanohole surface plasmon resonance.

With recent advances in high-throughput proteomics and systems biology, there is a growing demand for new instruments that can precisely quantify a wide range of receptor-ligand binding kinetics in a high-throughput fashion. Here we demonstrate a surface plasmon resonance (SPR) imaging spectroscopy instrument capable of simultaneously extracting binding kinetics and affinities from 50 parallel microfluidic channels. The instrument utilizes large-area (~ cm(2)) metallic nanohole arrays as SPR sensing substrates and combines a broadband light source, a high-resolution imaging spectrometer and a low-noise CCD camera to extract spectral information from every channel in real time with a refractive index resolution of 7.7 × 10(-6) refractive index units. To demonstrate the utility of our instrument for quantifying a wide range of biomolecular interactions, each parallel microfluidic channel is coated with a biomimetic supported lipid membrane containing ganglioside (GM1) receptors. The binding kinetics of cholera toxin b (CTX-b) to GM1 are then measured in a single experiment from 50 channels. By combining the highly parallel microfluidic device with large-area periodic nanohole array chips, our SPR imaging spectrometer system enables high-throughput, label-free, real-time SPR biosensing, and its full-spectral imaging capability combined with nanohole arrays could enable integration of SPR imaging with concurrent surface-enhanced Raman spectroscopy.

Preliminary Results of Superresolution-Enhanced Angular Hyperspectral (CHRIS/Proba) Images for Land-Cover Classification

Superresolution (SR) image reconstruction is a technique to obtain a high-resolution (HR) image from a set of low-resolution images. Compact High Resolution Imaging Spectrometer (CHRIS)/Project for On-Board Autonomy (Proba) is a transitional hyperspectral-oriented satellite which acquires multiple angular images of the same scene. The angular images acquired within a very short period of time are ideal for SR operation. Recent developments point to the possibility of a SR-enhanced CHRIS data set at higher resolution. Apparently, the additional details are valuable information for many applications. This letter presents the preliminary evaluation of the SR CHRIS images for ecotope mapping and subpixel classification of sealed surface using two different scenes in Belgium. Accuracy obtained from SR CHRIS images is comparable to that of the original CHRIS, but with significantly more detail in the final classification map. In view of the demands for HR hyperspectral data sets, SR operation can be an interesting option to mitigate the lower spatial resolution of the current and future spaceborne hyperspectral images. Properties such as quick revisiting time and angular acquisition of a hyperspectral satellite are important for the success of SR operations.

Pixel Unmixing in Hyperspectral Data by Means of Neural Networks

Neural networks (NNs) are recognized as very effective techniques when facing complex retrieval tasks in remote sensing. In this paper, the potential of NNs has been applied in solving the unmixing problem in hyperspectral data. In its complete form, the processing scheme uses an NN architecture consisting of two stages: the first stage reduces the dimension of the input vector, while the second stage performs the mapping from the reduced input vector to the abundance percentages. The dimensionality reduction is performed by the so-called autoassociative NNs, which yield a nonlinear principal component analysis of the data. The evaluation of the whole performance is carried out for different sets of experimental data. The first one is provided by the Airborne Hyperspectral Scanner. The second set consists of images from the Compact High-Resolution Imaging Spectrometer on board the Project for On-Board Autonomy satellite, and it includes multiangle and multitemporal acquisitions. The third set is represented by Airborne Visible/InfraRed Imaging Spectrometer measurements. A quantitative performance analysis has been carried out in terms of effectiveness in the dimensionality reduction phase and in terms of the accuracy in the final estimation. The results obtained, when compared with those produced by appropriate benchmark techniques, show the advantages of this approach.

Multioutput Support Vector Regression for Remote Sensing Biophysical Parameter Estimation

This letter proposes a multioutput support vector regression (M-SVR) method for the simultaneous estimation of different biophysical parameters from remote sensing images. General retrieval problems require multioutput (and potentially nonlinear) regression methods. M-SVR extends the single-output SVR to multiple outputs maintaining the advantages of a sparse and compact solution by using an e-insensitive cost function. The proposed M-SVR is evaluated in the estimation of chlorophyll content, leaf area index and fractional vegetation cover from a hyperspectral compact high-resolution imaging spectrometer images. The achieved improvement with respect to the single-output regression approach suggests that M-SVR can be considered a convenient alternative for nonparametric biophysical parameter estimation and model inversion.

A very compact imaging spectrometer for the micro-satellite STSAT3

STSAT3, a ∼150-kg micro-satellite, is the third experimental micro-satellite of the STSAT (Science Technology Satellite) series designated for the Long-Term Plan for Korea's Space Development by the Ministry of Education, Science and Technology of Korea. A Compact Imaging Spectrometer (COMIS) for use in the STSAT3 micro-satellite is currently under construction. It is scheduled to be launched into a low sun-synchronous Earth orbit (∼700 km) by the end of 2012. COMIS was inspired by the success of Compact High Resolution Imaging Spectrometer (CHRIS), a small hyperspectral imager developed for the European Space Agency (ESA) micro-satellite Project for On Board Autonomy (PROBA). COMIS, as its name implies, is very compact in terms of volume, mass and power. The total mass including the optics, housing and electronics is approximately 4.3 kg, and the average operational power is less than 13 W. Its main operational goal will be the imaging of the Earth's surface and atmosphere with ground sampling distances of 27 m at the 18–62 spectral bands (0.4–1.05 μm). COMIS takes hyperspectral images in two different modes: strip imaging and stereo viewing observation. This imaging will be used for environmental monitoring, such as the inland water quality monitoring of Paldang Lake, which is located close to Seoul, South Korea.

Atmospheric correction of PROBA/CHRIS data in an urban environment

The Compact High Resolution Imaging Spectrometer (CHRIS) is an imaging spectrometer onboard the European Space Agency (ESA) Project for On-Board Autonomy (PROBA) satellite. However, it has been shown that CHRIS presents some miscalibration trends over the spectral region covered. This paper reports a practical procedure for the atmospheric correction of CHRIS images based on field recalibration in an urban environment. In the first stage, the spectra of surface targets are measured and used to simulate the spectral radiance at the top of the atmosphere (TOA) for each channel and to determine the recalibration coefficients of the CHRIS images. In the second stage, two methods for atmospheric correction are examined: the radiative transfer model (RTM) and the improved dark-object subtraction (IDOS) method. For comparison purposes, the empirical line method (ELM) is also evaluated. The accuracy assessment shows that the RTM with the Moderate Resolution Transmittance (MODTRAN) code provides the most accurate atmospheric correction for the multiangular CHRIS images when using the proposed procedure.

Discrimination of wheat crop stage using CHRIS/PROBA multi-angle narrowband data

Multi-angular narrowband compact high-resolution imaging spectrometer (CHRIS) on-board the project for on-board autonomy (PROBA) data of 18 March 2008 were used in this study to discriminate three different growth stages of wheat crop grown in the Central State Farm of Suratgarh, Rajasthan, India. Results showed that the off-nadir view angles performed better than nadir viewing for crop stage discrimination. Among all the off-nadir viewing angles, −55.37° view angle (in the backward-scattering direction) had the highest normalized distance between the crop stage classes. Based on the analysis, the five best bands were identified as 630, 660, 674, 705 and 712 nm for separating wheat at different stages.

星载成像光谱仪杂散光测量与修正

To reduce the influence of stray lights on the spectral measurement accuracy of a High Resolution Imaging Spectrometer(HRIS),sources and hazards of stray lights were analyzed.Based on the Point Spread Function(PSF) theory which is usually used to describe the stray lights of HRISs,the influence matrix and the correction matrix of stray lights were discussed.The measurements and specific correction programs were given,and the accuracies of measurement and correction were analysed.The results show that the method based on PSF can complete the measurement and correction of stray lights for the HRIS.For a pixel center wavelength incident light,the effective signal is only reduced no more than 1%,while the stray light signal is reduced at least by 99% as compared with the raw data.For both pixel center wavelength and non-pixel center wavelength incident lights,the corrected signals are reduced by about 7% at the pixel center wavelength and by nearly 25% at the non-pixel center wavelength,while the stray light signal is reduced by almost 50%.Finally,the theoretical reasons why the incident light wavelength affected the stray light correction were explained.

Spectrodirectional Minnaert-k retrieval using CHRIS-PROBA data

We report on a detailed analysis of hyperspectral and multidirectional remote sensing data acquired using the Compact High Resolution Imaging Spectrometer (CHRIS) mounted onboard the Project for On-Board Autonomy (PROBA) spacecraft. This instrument is capable of sampling reflected radiation over the visible and near-infrared (NIR) region of the solar spectrum at a spatial resolution (approx. 17 m) intermediary between sensors traditionally used in land applications (such as Landsat and Satellite Pour l'Observation de la Terre (SPOT), 30 m–50 m) and the latest instruments delivering a nominal resolution of 1 m or less. The spectral anisotropic signature of an Alpine coniferous forest during winter in relation to canopy cover was investigated using the Minnaert-k parameter obtained by inverting the Rahman–Pinty–Verstraete (RPV) model against CHRIS data. Although earlier studies have demonstrated that Minnaert-k can be used to characterize surface heterogeneity at subpixel scale, its spectral dependency has not yet been fully assessed in an imaging spectrometry context. Minnaert-k parameter retrievals across CHRIS bands revealed that a switch from bell-shaped to bowl-shaped anisotropic reflectance patterns occurs when comparing visible to NIR responses. Specifically, the degree of canopy cover and background brightness determine where in the spectral domain this switch in reflectance anisotropy occurs. For a bright snow cover background Minnaert-k values correlated best with canopy cover at the end of the red edge (e.g., around 735 nm). In this spectral region, pixels with medium canopy cover (40%–70%) typically produced bell-shaped anisotropy patterns, while pixels with sparse (<30%) or dense (>70%) canopy covers typically produced bowl-shaped reflectance anisotropy patterns.

Merging the Minnaert-$k$ Parameter With Spectral Unmixing to Map Forest Heterogeneity With CHRIS/PROBA Data

The Compact High Resolution Imaging Spectrometer (CHRIS) mounted onboard the Project for Onboard Autonomy (PROBA) spacecraft is capable of sampling reflected radiation at five viewing angles over the visible and near-infrared regions of the solar spectrum with high spatial resolution. We combined the spectral domain with the angular domain of CHRIS data in order to map the surface heterogeneity of an Alpine coniferous forest during winter. In the spectral domain, linear spectral unmixing of the nadir image resulted in a canopy cover map. In the angular domain, pixelwise inversion of the Rahman-Pinty-Verstraete (RPV) model at a single wavelength at the red edge (722 nm) yielded a map of the Minnaert-k parameter that provided information on surface heterogeneity at a subpixel scale. However, the interpretation of the Minnaert-k parameter is not always straightforward because fully vegetated targets typically produce the same type of reflectance anisotropy as non-vegetated targets. Merging both maps resulted in a forest cover heterogeneity map, which contains more detailed information on canopy heterogeneity at the CHRIS subpixel scale than is possible to realize from a single-source optical data set.

Impact of multi-angular CHRIS/PROBA data on their empirical relationships with tropical forest biomass

Images from the Compact High Resolution Imaging Spectrometer (CHRIS), on board the space platform Project for On Board Autonomy (PROBA), were acquired in the Brazilian Amazon region in 62 bands (410–1050 nm) at different view angles. They were evaluated for angular variations in Bidirectional Reflectance Factor (BRF), selected vegetation and anisotropic indices, and their relationship with the Above Ground Biomass (AGB) of some forest successional stages using an empirical approach. Results showed that correlations between AGB and reflectance were influenced by the vegetation anisotropy, which was stronger in the visible than in the near-infrared. The anisotropy increased from the forward (−36°) to the backward (+55°) scattering direction, was greater in the blue and green bands and decreased towards the near-infrared. As a result of this behaviour, several narrow and traditional vegetation indices showed correlation coefficients (R) that varied with view angle. The backscattering–forward scattering contrast, represented by anisotropic indices such as the Normalized Difference Anisotropic Index (NADX) and the Hot spot–Dark spot Normalized Difference Vegetation Index (NDVITD), presented only a small improvement of the relationships with AGB when compared with the performance of the other traditional vegetation indices at nadir viewing.

Use of remote sensing data to assist crop modeling

This paper presents results of a study investigating the potential for improvement of a physically-based model approach, when the static input data is enhanced by dynamic remote sensing information. The model PROMET (PROcess of Radiation Mass and Energy Transfer), which is normally used to simulate the water and energy fluxes at the landscape level, was applied on a field scale to simulate crop growth and yield. The remote sensing input data was derived from hyperspectral images of the CHRIS (Compact High Resolution Imaging Spectrometer) sensor, which is operated by ESA (European Space Agency). PROMET was set up for a field scale model run for two test fields grown with winter wheat (Triticum aestivum L.) mapping the crop development of the seasons 2004 and 2005. During the model runs, information on the absorptive capacity of the leaves for two canopy layers (sunlit and shaded layer) was updated using remotely sensed chlorophyll measurements. The chlorophyll contents of these two vegetation layers were assessed using angular CHRIS data. Control data were acquired through field measurements, which were conducted throughout the growing periods of both years and also accompanying the satellite overpasses. The stand-alone model was able to reproduce the average development of the crop and yield reasonably well, but the spatial heterogeneity was severely underestimated and yield was overestimated by approximately 20%. The combination of remote sensing data with the model led to an improvement of the spatial heterogeneity of the crop development and yield. The use of ground truth data to improve the modeling accuracy can be made possible.

Fully Automatic Subpixel Image Registration of Multiangle CHRIS/Proba Data

Subpixel image registration is the key to successful image fusion and superresolution enhancement of multiangle satellite data. Multiangle image registration poses two main challenges: 1) Images captured at large view angles are susceptible to resolution change and blurring, and 2) local geometric distortion caused by topographic effects and/or platform instability may be important. In this paper, we propose a two-step nonrigid automatic registration scheme for multiangle satellite images. In the first step, control points (CPs) are selected in a preregistration process based on the scale-invariant feature transform (SIFT). However, the number of CPs obtained in this first step may be too few and/or CPs may be unevenly distributed. To remediate these problems, in a second step, the preliminary registered image is subdivided into chips of 64 × 64 pixels, and each chip is matched with a corresponding chip in the reference image using normalized cross correlation (NCC). By doing so, more CPs with better spatial distribution are obtained. Two criteria are applied during the generation of CPs to identify outliers. Selected SIFT and NCC CPs are used for defining a nonrigid thin-plate-spline model. The proposed registration scheme has been tested using data from the Compact High Resolution Imaging Spectrometer (CHRIS) onboard the Project for On-Board Autonomy (Proba) satellite. Experimental results demonstrate that the proposed method works well in areas with little variation in topography. Application in areas with more pronounced relief would require the use of orthorectified image data in order to achieve subpixel registration accuracy.

星载成像光谱仪杂散光测量

In order to measure the stray light,which is one of the important influence factors in spectral accuracy,the definition,source and harmfulness of stray light are introduced.Cut-off filter method,spectrum method,spectral stray light factors method and so on are presented to discuss the advantages and disadvantages of normally used stray light measurement methods.Then,the feasibility and superiority of the stray light factor used to describe the stray light characteristics of the spectral instruments,is investigated.At last,the system components,measurement procedure and measurement results of the stray light measurement system,using narrow-band filter,are introduced,and the uncertainty of the results is analysed.Results show that the stray light factor indicates the stray light characteristics of the spectral instruments effectively without relationship to the light source,detector and other measurement conditions.The relative uncertainty of the measurement is 0.646% when the fiducial probability is about 95%,which can meet the needs of the stray light measurement.

Improvements in Aerosol Optical Depth Estimation Using Multiangle CHRIS/PROBA Images

A method has been developed to estimate aerosol optical depth (AOD) over land surfaces using high spatial resolution, hyperspectral, and multiangle Compact High Resolution Imaging Spectrometer (CHRIS)/Project for On Board Autonomy (PROBA) images. The CHRIS instrument is mounted aboard the PROBA satellite and provides up to 62 bands. The PROBA satellite allows pointing to obtain imagery from five different view angles within a short time interval. The method uses inversion of a coupled surface/atmosphere radiative transfer model and includes a general physical model of angular surface reflectance. An iterative process is used to determine the optimum value providing the best fit of the corrected reflectance values for a number of view angles and wavelengths with those provided by the physical model. This method has previously been demonstrated on data from the Advanced Along-Track Scanning Radiometer and is extended here to the spectral and angular sampling of CHRIS/PROBA. The values obtained from these observations are validated using ground-based sun-photometer measurements. Results from 22 image sets show an rms error of 0.11 in AOD at 550 nm, which is reduced to 0.06 after an automatic screening procedure.

Effects of woody elements on simulated canopy reflectance: Implications for forest chlorophyll content retrieval

An important bio-indicator of actual plant health status, the foliar content of chlorophyll a and b ( Cab), can be estimated using imaging spectroscopy. For forest canopies, however, the relationship between the spectral response and leaf chemistry is confounded by factors such as background (e.g. understory), canopy structure, and the presence of non-photosynthetic vegetation (NPV, e.g. woody elements)—particularly the appreciable amounts of standing and fallen dead wood found in older forests. We present a sensitivity analysis for the estimation of chlorophyll content in woody coniferous canopies using radiative transfer modeling, and use the modeled top-of-canopy reflectance data to analyze the contribution of woody elements, leaf area index (LAI), and crown cover (CC) to the retrieval of foliar Cab content. The radiative transfer model used comprises two linked submodels: one at leaf level (PROSPECT) and one at canopy level (FLIGHT). This generated bidirectional reflectance data according to the band settings of the Compact High Resolution Imaging Spectrometer (CHRIS) from which chlorophyll indices were calculated. Most of the chlorophyll indices outperformed single wavelengths in predicting Cab content at canopy level, with best results obtained by the Maccioni index ([ R 780 − R 710] / [ R 780 − R 680]). We demonstrate the performance of this index with respect to structural information on three distinct coniferous forest types (young, early mature and old-growth stands). The modeling results suggest that the spectral variation due to variation in canopy chlorophyll content is best captured for stands with medium dense canopies. However, the strength of the up-scaled Cab signal weakens with increasing crown NPV scattering elements, especially when crown cover exceeds 30%. LAI exerts the least perturbations. We conclude that the spectral influence of woody elements is an important variable that should be considered in radiative transfer approaches when retrieving foliar pigment estimates in heterogeneous stands, particularly if the stands are partly defoliated or long-lived.

Landscape‐level variation in forest structure and biogeochemistry across a substrate age gradient in Hawaii

We compared forest canopy heights and nitrogen concentrations in long-term research sites and in 2 x 2 km landscapes surrounding these sites along a substrate age gradient in the Hawaiian Islands. Both remote airborne and ground-based measurements were used to characterize processes that control landscape-level variation in canopy properties. We integrated a waveform light detection and ranging (LiDAR) system, a high-resolution imaging spectrometer, and a global positioning system/inertial measurement unit to provide highly resolved images of ground topography, canopy heights, and canopy nitrogen concentrations (1) within a circle 50 m in radius focused on a long-term study site in the center of each landscape; (2) for the entire 2 x 2 km landscape regardless of land cover; and (3) after stratification, for our target cover class, native-dominated vegetation on constructional geomorphic surfaces throughout each landscape. Remote measurements at all scales yielded the same overall patterns as did ground-based measurements in the long-term sites. The two younger landscapes supported taller trees than did older landscapes, while the two intermediate-aged landscapes had higher canopy nitrogen (N) concentrations than did either young or old landscapes. However, aircraft-based analyses detected substantial variability in canopy characteristics on the landscape level, even within the target cover class. Canopy heights were more heterogeneous on the older landscapes, with coefficients of variation increasing from 23-41% to 69-78% with increasing substrate age. This increasing heterogeneity was associated with a larger patch size of canopy turnover and with dominance of most secondary successional stands by the mat-forming fern Dicranopteris linearis in the older landscapes.

Scene-based spectral calibration assessment of high spectral resolution imaging spectrometers

An accurate knowledge of the spectral calibration of imaging spectrometers is required for optimum data processing and interpretation. The scene-based spectral characterization of imaging spectrometers is frequently necessary to update or replace the pre-flight laboratory-based spectral characterization supplied by the data provider. An automatic method for the estimation of spectral calibration parameters (channel position and bandwidth) at atmospheric absorption regions from high spectral resolution imaging spectrometers (spectral sampling interval below 5 nm) is presented in this contribution. The method has been tested on two commercial instruments with spectral sampling intervals below 2.5 nm. Optical aberrations such as smile, spectrometer shift and rotation and degradation of channel bandwidth have been detected and are discussed in terms of potential error sources at the instrument level.

Scene-based spectral calibration assessment of high spectral resolution imaging spectrometers

Scene-based spectral calibration assessment of high spectral resolution imaging spectrometers

Noise reduction of hyperspectral data using singular spectral analysis

In this study, a new noise reduction algorithm based on singular spectral analysis (SSA) was developed to reduce the noise in hyperspectral data. With this SSA‐based approach, the reflectance spectrum of a given pixel in a hyperspectral cube is transformed into its state space. The state space is dynamically constructed and characterized by irregular bases, which allows the proposed approach to reduce noises while keeping the absorption features of surface objects. The performance of the developed method was verified on three datasets: two simulated reflectance spectra with several narrow absorption features and a CHRIS (Compact High Resolution Imaging Spectrometer) data cube over agricultural fields. Our results demonstrated the effectiveness of the SSA‐based approach in improving the signal‐to‐noise ratio of hyperspectral data, while keeping the ‘sharp features’ in the reflectance spectra. The results also show that the proposed SSA method outperforms the commonly used MNF (minimum noise fraction) and wavelet‐based noise reduction methods and it improved vegetation cover classification accuracy by 6%.