Multispectral Imaging Instrument Research Articles

Hydrothermally altered mineral mapping using synthetic application of Sentinel-2A MSI, ASTER and Hyperion data in the Duolong area, Tibetan Plateau, China

The Duolong area is in a strong potential copper mineralization zone associated with felsic intrusions, and several porphyry copper deposits (PCDs) have been identified. The PCDs are characterized by hydrothermal alteration zones. In this study, Sentinel-2A MultiSpectral Instrument (MSI), Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER), and Hyperion data are combined to map hydrothermally altered minerals. Three temporal Sentinel-2A MSI images are processed by integrating the Crosta technique and the anomaly-overlaying selection method to map iron oxides and hydroxyl-bearing minerals. These approaches are implemented to omit interference-induced false anomalies. The six shortwave infrared (SWIR) bands of ASTER are used to extract information on Al-OH and Mg-OH group minerals. The distribution of Al-OH minerals in the ASTER image map corresponds to that of hydroxyl-bearing minerals in the MSI image map. A combination of minimum noise fraction (MNF), pixel purity index (PPI), n-dimensional visualizer (n-D Visualizer) and matched filtering (MF) is adopted to process the Hyperion image to obtain accurate hydrothermal alteration mapping results. The overall accuracy for mineral mapping is 92.75% and the kappa coefficient is 0.89. The results of the Hyperion image are spatially consistent with those of the Sentinel-2A MSI and ASTER images. The image processing results were validated by field investigations and spectral reflectance measurements. Hydrothermally altered rocks correspond well with the five PCDs in the Duolong area. Three mineralization prospects associated with felsic intrusions are discovered in accordance with the results. The methodologies and data are effective in detecting porphyry copper mineralization in other arid areas.

Non-destructive Determination of Shikimic Acid Concentration in Transgenic Maize Exhibiting Glyphosate Tolerance Using Chlorophyll Fluorescence and Hyperspectral Imaging.

The development of transgenic glyphosate-tolerant crops has revolutionized weed control in crops in many regions of the world. The early, non-destructive identification of superior plant phenotypes is an important stage in plant breeding programs. Here, glyphosate-tolerant transgenic maize and its parental wild-type control were studied at 2, 4, 6, and 8 days after glyphosate treatment. Visible and near-infrared hyperspectral imaging and chlorophyll fluorescence imaging techniques were applied to monitor the performance of plants. In our research, transgenic maize, which was highly tolerant to glyphosate, was phenotyped using these high-throughput non-destructive methods to validate low levels of shikimic acid accumulation and high photochemical efficiency of photosystem II as reflected by maximum quantum yield and non-photochemical quenching in response to glyphosate. For hyperspectral imaging analysis, the combination of spectroscopy and chemometric methods was used to predict shikimic acid concentration. Our results indicated that a partial least-squares regression model, built on optimal wavelengths, effectively predicted shikimic acid concentrations, with a coefficient of determination value of 0.79 for the calibration set, and 0.82 for the prediction set. Moreover, shikimic acid concentration estimates from hyperspectral images were visualized on the prediction maps by spectral features, which could help in developing a simple multispectral imaging instrument for non-destructive phenotyping. Specific physiological effects of glyphosate affected the photochemical processes of maize, which induced substantial changes in chlorophyll fluorescence characteristics. A new data-driven method, combining mean fluorescence parameters and featuring a screening approach, provided a satisfactory relationship between fluorescence parameters and shikimic acid content. The glyphosate-tolerant transgenic plants can be identified with the developed discrimination model established on important wavelengths or sensitive fluorescence parameters 6 days after glyphosate treatment. The overall results indicated that both hyperspectral imaging and chlorophyll fluorescence imaging techniques could provide useful tools for stress phenotyping in maize breeding programs and could enable the detection and evaluation of superior genotypes, such as glyphosate tolerance, with a non-destructive high-throughput technique.

Nondestructive and rapid determination of lignocellulose components of biofuel pellet using online hyperspectral imaging system

BackgroundIn the pursuit of sources of energy, biofuel pellet is emerging as a promising resource because of its easy storage and transport, and lower pollution to the environment. The composition of biomass has important implication for energy conversion processing strategies. Current standard chemical methods for biomass composition are laborious, time-consuming, and unsuitable for high-throughput analysis. Therefore, a reliable and efficient method is needed for determining lignocellulose composition in biomass and so to accelerate biomass utilization. Here, near-infrared hyperspectral imaging (900–1700 nm) together with chemometrics was used to determine the lignocellulose components in different types of biofuel pellets. Partial least-squares regression and principal component multiple linear regression models based on whole wavelengths and optimal wavelengths were employed and compared for predicting lignocellulose composition.ResultsOut of 216 wavelengths, 20, 10 and 17 were selected by the successive projections algorithm for cellulose, hemicellulose and lignin, respectively. Three simple and satisfactory prediction models were constructed, with coefficients of determination of 0.92, 0.84 and 0.71 for cellulose, hemicellulose and lignin, respectively. The relative parameter distributions were quantitatively visualized through prediction maps by transferring the optimal models to all pixels on the hyperspectral image.ConclusionsHence, the overall results indicated that hyperspectral imaging combined with chemometrics offers a non-destructive and low-cost method for determining biomass lignocellulose components, which would help in developing a simple multispectral imaging instrument for biofuel pellets online measurement and improving the production management.

Understanding the temporal dimension of the red-edge spectral region for forest decline detection using high-resolution hyperspectral and Sentinel-2a imagery

The operational monitoring of forest decline requires the development of remote sensing methods that are sensitive to the spatiotemporal variations of pigment degradation and canopy defoliation. In this context, the red-edge spectral region (RESR) was proposed in the past due to its combined sensitivity to chlorophyll content and leaf area variation. In this study, the temporal dimension of the RESR was evaluated as a function of forest decline using a radiative transfer method with the PROSPECT and 3D FLIGHT models. These models were used to generate synthetic pine stands simulating decline and recovery processes over time and explore the temporal rate of change of the red-edge chlorophyll index (CI) as compared to the trajectories obtained for the structure-related Normalized Difference Vegetation Index (NDVI). The temporal trend method proposed here consisted of using synthetic spectra to calculate the theoretical boundaries of the subspace for healthy and declining pine trees in the temporal domain, defined by CItime=n/CItime=n+1 vs. NDVItime=n/NDVItime=n+1. Within these boundaries, trees undergoing decline and recovery processes showed different trajectories through this subspace. The method was then validated using three high-resolution airborne hyperspectral images acquired at 40 cm resolution and 260 spectral bands of 6.5 nm full-width half-maximum (FWHM) over a forest with widespread tree decline, along with field-based monitoring of chlorosis and defoliation (i.e., ‘decline’ status) in 663 trees between the years 2015 and 2016. The temporal rate of change of chlorophyll vs. structural indices, based on reflectance spectra extracted from the hyperspectral images, was different for trees undergoing decline, and aligned towards the decline baseline established using the radiative transfer models. By contrast, healthy trees over time aligned towards the theoretically obtained healthy baseline. The applicability of this temporal trend method to the red-edge bands of the MultiSpectral Imager (MSI) instrument on board Sentinel-2a for operational forest status monitoring was also explored by comparing the temporal rate of change of the Sentinel-2-derived CI over areas with declining and healthy trees. Results demonstrated that the Sentinel-2a red-edge region was sensitive to the temporal dimension of forest condition, as the relationships obtained for pixels in healthy condition deviated from those of pixels undergoing decline.

Hue-Angle Product for Low to Medium Spatial Resolution Optical Satellite Sensors

In the European Citclops project, with a prime aim of developing new tools to involve citizens in the water quality monitoring of natural waters, colour was identified as a simple property that can be measured via a smartphone app and by dedicated low-cost instruments. In a recent paper, we demonstrated that colour, as expressed mainly by the hue angle (α), can also be derived accurately and consistently from the ocean colour satellite instruments that have observed the Earth since 1997. These instruments provide superior temporal coverage of natural waters, albeit at a reduced spatial resolution of 300 m at best. In this paper, the list of algorithms is extended to the very first ocean colour instrument, and the Moderate Resolution Imaging Spectroradiometer (MODIS) 500-m resolution product. In addition, we explore the potential of the hue angle derivation from multispectral imaging instruments with a higher spatial resolution but reduced spectral resolution: the European Space Agency (ESA) multispectral imager (MSI) on Sentinel-2 A,B, the Operational Land Imager (OLI) on the National Aeronautics and Space Administration (NASA) Landsat-8, and its precursor, the Enhanced Thematic Mapper Plus (ETM+) on Landsat-7. These medium-resolution imagers might play a role in an upscaling from point measurements to the typical 1-km pixel size from ocean colour instruments. As the parameter α (the colour hue angle) is fairly new to the community of water remote sensing scientists, we present examples of how colour can help in the image analysis in terms of water-quality products.

Multi-spectral misregistration of Sentinel-2A images: analysis and implications for potential applications.

This study aims at analyzing sub-pixel misregistration between multi-spectral images acquired by the Multi-Spectral Instrument (MSI) aboard Sentinel-2A remote sensing satellite, and exploring its potential for moving target and cloud detection. By virtue of its hardware design, MSI's detectors exhibit a parallax angle that leads to sub-pixel shifts that are corrected with special pre-processing routines. However, these routines do not correct shifts for moving and/or high altitude objects. In this letter, we apply a phase correlation approach to detect sub-pixel shifts between B2 (blue), B3 (green) and B4 (red) Sentinel-2A/MSI images. We show that shifts of more than 1.1 pixels can be observed for moving targets, such as airplanes and clouds, and can be used for cloud detection. We demonstrate that the proposed approach can detect clouds that are not identified in the built-in cloud mask provided within the Sentinel-2A Level-1C (L1C) product.

Classification of white maize defects with multispectral imaging

Multispectral imaging with object-wise multivariate image analysis was evaluated for its potential to grade whole white maize kernels. The types of defective materials regarded in grading legislation were divided into 13 classes, and were imaged with a multispectral imaging instrument spanning the UV, visible and NIR regions (19 wavelengths ranging from 375 to 970nm). Object-wise partial least squares discriminant analysis (PLS-DA) models were developed and validated with an independent data set. Results demonstrated good performance in distinguishing between sound maize and undesirable materials, with cross-validated coefficients of determination (Q2) and classification accuracies ranging from 0.35 to 0.99 and 83 to 100%, respectively. Wavelengths related to absorbance of green, yellow and orange colour indicated the presence of lycopene and anthocyanin (505, 525, 570 and 590 nm). NIR wavelengths 890, 940 nm (associated with fat) and 970 nm (associated with water) were generally identified as important features throughout the study.

ASSESSMENTS OF SENTINEL-2 VEGETATION RED-EDGE SPECTRAL BANDS FOR IMPROVING LAND COVER CLASSIFICATION

Abstract. The Multi Spectral Instrument (MSI) onboard Sentinel-2 can record the information in Vegetation Red-Edge (VRE) spectral domains. In this study, the performance of the VRE bands on improving land cover classification was evaluated based on a Sentinel-2A MSI image in East Texas, USA. Two classification scenarios were designed by excluding and including the VRE bands. A Random Forest (RF) classifier was used to generate land cover maps and evaluate the contributions of different spectral bands. The combination of VRE bands increased the overall classification accuracy by 1.40 %, which was statistically significant. Both confusion matrices and land cover maps indicated that the most beneficial increase was from vegetation-related land cover types, especially agriculture. Comparison of the relative importance of each band showed that the most beneficial VRE bands were Band 5 and Band 6. These results demonstrated the value of VRE bands for land cover classification.

Landsat 15-m Panchromatic-Assisted Downscaling (LPAD) of the 30-m Reflective Wavelength Bands to Sentinel-2 20-m Resolution

The Landsat 15-m Panchromatic-Assisted Downscaling (LPAD) method to downscale Landsat-8 Operational Land Imager (OLI) 30-m data to Sentinel-2 multi-spectral instrument (MSI) 20-m resolution is presented. The method first downscales the Landsat-8 30-m OLI bands to 15-m using the spatial detail provided by the Landsat-8 15-m panchromatic band and then reprojects and resamples the downscaled 15-m data into registration with Sentinel-2A 20-m data. The LPAD method is demonstrated using pairs of contemporaneous Landsat-8 OLI and Sentinel-2A MSI images sensed less than 19 min apart over diverse geographic environments. The LPAD method is shown to introduce less spectral and spatial distortion and to provide visually more coherent data than conventional bilinear and cubic convolution resampled 20-m Landsat OLI data. In addition, results for a pair of Landsat-8 and Sentinel-2A images sensed one day apart suggest that image fusion should be undertaken with caution when the images are acquired under different atmospheric conditions. The LPAD source code is available at GitHub for public use.

Assessing the suitability of data from Sentinel-1A and 2A for crop classification

Sentinel-1A C-SAR and Sentinel-2A MultiSpectral Instrument (MSI) provide data applicable to the remote identification of crop type. In this study, six crop types (beans, beetroot, grass, maize, potato, and winter wheat) were identified using five C-SAR images and one MSI image acquired during the 2016 growing season. To assess the potential for accurate crop classification with existing supervised learning models, the four different approaches namely kernel-based extreme learning machine (KELM), multilayer feedforward neural networks, random forests, and support vector machine were compared. Algorithm hyperparameters were tuned using Bayesian optimization. Overall, KELM yielded the highest performance, achieving an overall classification accuracy of 96.8%. Evaluation of the sensitivity of classification models and relative importance of data types using data-based sensitivity analysis showed that the set of VV polarization data acquired on 24 July (Sentinel-1A) and band 4 data (Sentinel-2A) had the greatest potential for use in crop classification.

The potential for integrating Sentinel 2 MSI with SPOT 5 HRG and Landsat 8 OLI imagery for monitoring semi-arid savannah woody cover

ABSTRACTMultitemporal archived imagery enables the monitoring of savannah woody cover, for ecological purposes. Compatibility in multitemporal, multiple sensor image data would facilitate the monitoring. The decommissioning of SPOT 5 (Système Pour l’Observation de la Terre 5) left a void in multispectral imagery at the 10 m spatial resolution of its high-resolution geometric (HRG) sensor. The subsequent launch of Sentinel 2 presented an opportunity for data continuity to monitor the savannah woody cover, using equivalent 10 m resolution multispectral instrument (MSI) bands. This study examined the integration potential of Sentinel 2 MSI with the longer archive HRG and Landsat 8 (Land Satellite 8) Operational Land Imager (OLI) imagery, in assessing savannah woody cover. Images of three semi-arid savannah sites acquired on same season dates that excluded herbaceous vegetation from the spectral signature were used: November 2014 (HRG) and December 2015 (MSI, OLI). Using equivalent green (G), red (R), and near infrared (NIR) bands at 10 m (MSI, HRG) and 30 m (OLI) resolution, the woody cover was mapped through subpixel classification. The mapped woody cover was compared for statistical differences using χ2 analysis at 10 m resolution (MSI, HRG) and at a degradation of the MSI and HRG images to the 30 m OLI pixel size. Conversion to top-of-atmosphere reflectance values facilitated inter-sensor correlation of G, R, and NIR reflectance for field sampling sites where woody cover was quantified. Inter-sensor regression functions in G, R, and NIR band MSI and HRG images were developed. The 10 m resolution classifications of woody cover were not statistically different. Due to spatial resolution similarity, SPOT 5 HRG multispectral imagery was established as suitable for integration with equivalent band MSI imagery in mapping the woody cover in a multitemporal analysis. For dense woody cover, Landsat 8 OLI imagery was more suitable for integration with MSI than HRG images due to higher radiometric sensitivity, which can permit monitoring physiology-related woody reflectance.

An automated ranking platform for machine learning regression models for meat spoilage prediction using multi-spectral imaging and metabolic profiling

Over the past decade, analytical approaches based on vibrational spectroscopy, hyperspectral/multispectral imagining and biomimetic sensors started gaining popularity as rapid and efficient methods for assessing food quality, safety and authentication; as a sensible alternative to the expensive and time-consuming conventional microbiological techniques. Due to the multi-dimensional nature of the data generated from such analyses, the output needs to be coupled with a suitable statistical approach or machine-learning algorithms before the results can be interpreted. Choosing the optimum pattern recognition or machine learning approach for a given analytical platform is often challenging and involves a comparative analysis between various algorithms in order to achieve the best possible prediction accuracy.In this work, “MeatReg”, a web-based application is presented, able to automate the procedure of identifying the best machine learning method for comparing data from several analytical techniques, to predict the counts of microorganisms responsible of meat spoilage regardless of the packaging system applied. In particularly up to 7 regression methods were applied and these are ordinary least squares regression, stepwise linear regression, partial least square regression, principal component regression, support vector regression, random forest and k-nearest neighbours.MeatReg” was tested with minced beef samples stored under aerobic and modified atmosphere packaging and analysed with electronic nose, HPLC, FT-IR, GC–MS and Multispectral imaging instrument. Population of total viable count, lactic acid bacteria, pseudomonads, Enterobacteriaceae and B. thermosphacta, were predicted. As a result, recommendations of which analytical platforms are suitable to predict each type of bacteria and which machine learning methods to use in each case were obtained. The developed system is accessible via the link: www.sorfml.com.

Assessment of Atmospheric Correction Methods for Sentinel-2 MSI Images Applied to Amazon Floodplain Lakes

Satellite data provide the only viable means for extensive monitoring of remote and large freshwater systems, such as the Amazon floodplain lakes. However, an accurate atmospheric correction is required to retrieve water constituents based on surface water reflectance ( R W ). In this paper, we assessed three atmospheric correction methods (Second Simulation of a Satellite Signal in the Solar Spectrum (6SV), ACOLITE and Sen2Cor) applied to an image acquired by the MultiSpectral Instrument (MSI) on-board of the European Space Agency’s Sentinel-2A platform using concurrent in-situ measurements over four Amazon floodplain lakes in Brazil. In addition, we evaluated the correction of forest adjacency effects based on the linear spectral unmixing model, and performed a temporal evaluation of atmospheric constituents from Multi-Angle Implementation of Atmospheric Correction (MAIAC) products. The validation of MAIAC aerosol optical depth (AOD) indicated satisfactory retrievals over the Amazon region, with a correlation coefficient (R) of ~0.7 and 0.85 for Terra and Aqua products, respectively. The seasonal distribution of the cloud cover and AOD revealed a contrast between the first and second half of the year in the study area. Furthermore, simulation of top-of-atmosphere (TOA) reflectance showed a critical contribution of atmospheric effects (>50%) to all spectral bands, especially the deep blue (92%–96%) and blue (84%–92%) bands. The atmospheric correction results of the visible bands illustrate the limitation of the methods over dark lakes ( R W < 1%), and better match of the R W shape compared with in-situ measurements over turbid lakes, although the accuracy varied depending on the spectral bands and methods. Particularly above 705 nm, R W was highly affected by Amazon forest adjacency, and the proposed adjacency effect correction minimized the spectral distortions in R W (RMSE < 0.006). Finally, an extensive validation of the methods is required for distinct inland water types and atmospheric conditions.

Chimeric Mouse model to track the migration of bone marrow derived cells in glioblastoma following anti-angiogenic treatments

ABSTRACTBone marrow derived cells (BMDCs) have been shown to contribute in the tumor development. In vivo animal models to investigate the role of BMDCs in tumor development are poorly explored. We established a novel chimeric mouse model using as low as 5 × 106 GFP+ BM cells in athymic nude mice, which resulted in >70% engraftment within 14 d. In addition, chimera was established in NOD-SCID mice, which displayed >70% with in 28 d. Since anti-angiogenic therapies (AAT) were used as an adjuvant against VEGF-VEGFR pathway to normalize blood vessels in glioblastoma (GBM), which resulted into marked hypoxia and recruited BMDCs to the tumor microenvironment (TME). We exploited chimeric mice in athymic nude background to develop orthotopic U251 tumor and tested receptor tyrosine kinase inhibitors and CXCR4 antagonist against GBM. We were able to track GFP+ BMDCs in the tumor brain using highly sensitive multispectral optical imaging instrument. Increased tumor growth associated with the infiltration of GFP+ BMDCs acquiring suppressive myeloid and endothelial phenotypes was seen in TME following treatments. Immunofluorescence study showed GFP+ cells accumulated at the site of VEGF, SDF1 and PDGF expression, and at the periphery of the tumors following treatments. In conclusion, we developed a preclinical chimeric model of GBM and phenotypes of tumor infiltrated BMDCs were investigated in context of AATs. Chimeric mouse model could be used to study detailed cellular and molecular mechanisms of interaction of BMDCs and TME in cancer.

Fluorescence Phenomena in Nerve-Labeling Styryl-Type Dyes.

Several classes of diversely substituted styryl type dyes have been synthesized with the goal of extending their expected fluorescent properties as much towards red as possible given the constraint that they maintain drug-like properties and retain high affinity binding to their biological target. We report on the synthesis, optical properties of a series of styryl dyes ( d1-d14 ), and the anomalous photophysical behavior of several of these Donor-Acceptor pairs separated by long conjugated π-systems ( d7-d10 ). We further describe an unusual dual emission behavior with two distinct ground state conformers which could be individually excited to locally excited (LE) and twisted intramolecular charge transfer (TICT) excited state in push-pull dye systems ( d7 , d9 and d10 ). Additionally, unexpected emission behavior in dye systems d7 and d8 wherein the amino- derivative d7 displayed a dual emission in polar medium while the N,N-dimethyl derivative d8 and other methylated derivatives d12-d14 showed only LE emission but did not show any TICT emission. Based on photophysical and nerve binding studies, we down selected compounds that exhibited the most robust fluorescent staining of nerve tissue sections. These dyes ( d7 , d9 , and d10 ) were subsequently selected for in-vivo fluorescence imaging studies in rodents using the small animal multispectral imaging instrument and the dual-mode laparoscopic instrument developed in-house.

Abstract P5-04-07: Multispectral imaging allows visualization and quantification of multiple immunologic cell types in breast tumor tissues

Abstract Background: Multiple studies have identified tumor infiltrating lymphocytes (TIL) in breast cancer and shown them to have prognostic and predictive significance. TIL are comprised of CD8+ and CD4+ T cells, regulatory T cells (Treg) and B cells. To date, the majority of studies have identified TIL using H&E staining. Studies utilizing IHC to stain for TIL generally have been limited to evaluating a single immune cell phenotype such as CD8+ T cells, while studies evaluating multiple immune cell types have generated single cell suspensions from tumors for analysis by flow cytometry to determine relative percentages of specific immune cell populations. Although these studies have confirmed that immune infiltrates in tumors are heterogeneous, they have not addressed the spatial relationship between tumor and immune cells, which could be critical to anti-tumor immune effects. This study was undertaken to demonstrate the feasibility of multispectral imaging and multiplexed immunofluorescence to visualize and quantify specific immune infiltrates in the tumor and surrounding stroma on single FFPE tissue sections. Methods: Single FFPE slides from 9 HER2+ breast cancer patients receiving neoadjuvant chemotherapy were stained with primary antibodies targeting cytokeratin, CD8, CD4, FoxP3 (Treg), CD20 (B-cells) and PD-L1 (T cell inhibitory molecule). Tyramide signal amplification was used to improve signal and specificity, as well as to reduce background. The Vectra multispectral imaging instrument was used for image acquisition and InForm software was used to quantitate the density (number of cells per square millimeter) of specific cell types in the tumor and in the surrounding stroma. Results: Multispectral imaging successfully captured and quantified multiple immune cell types in all tissues (images to be shown at time of presentation). CD8+ and CD4+ T cell densities as well as PD-L1 expression in the tumor and surrounding stroma are shown. Summary of immune cell infiltrate Within Tumor Within Stroma SampleCD8 densityCD4 densityPDL1 H-scoreCD8 densityCD4 densityPDL1 H-score1050631301207342523059189238312260022111331104305083833106345020808206131751011891591110700417754621858511431739186812198591617448342570196 For patients not achieving a pathologic complete response (pCR), the density of both the CD8 (p=.03) and CD4 (p=.05) infiltrates in the stroma were significantly greater than in the tumor. For patients achieving a pCR, there was no significant difference in the densities of stromal and intratumoral CD8 (p=.11) or CD4 (p=.75) infiltrates suggesting that T cell infiltration into the tumor from the stroma is critical. Conclusion: Multispectral imaging allows different immune cell phenotypes to be visualized and quantified simultaneously in the same tissue section enabling further study of the relationships and distribution of these cells within the tumor and tumor microenvironment, and their spatial distribution and proximity to the tumor cells. This technology will enable improved understanding of the immune infiltrate in breast tumors thereby facilitating the rational design and use of immunotherapeutic agents in combination with standard systemic therapies. Citation Format: Elizabeth A Mittendorf, Chichung Wang, Peng Yau, Kristin Roman, Yun Wu, Gheath Alatrash, Clifford Hoyt. Multispectral imaging allows visualization and quantification of multiple immunologic cell types in breast tumor tissues [abstract]. In: Proceedings of the Thirty-Seventh Annual CTRC-AACR San Antonio Breast Cancer Symposium: 2014 Dec 9-13; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2015;75(9 Suppl):Abstract nr P5-04-07.

Infrared optical coatings for the EarthCARE Multispectral Imager.

The Earth Cloud, Aerosol and Radiation Explorer mission (EarthCARE) Multispectral Imager (MSI) is a radiometric instrument designed to provide the imaging of the atmospheric cloud cover and the cloud top surface temperature from a sun-synchronous low Earth orbit. The MSI forms part of a suite of four instruments destined to support the European Space Agency Living Planet mission on-board the EarthCARE satellite payload to be launched in 2016, whose synergy will be used to construct three-dimensional scenes, textures, and temperatures of atmospheric clouds and aerosols. The MSI instrument contains seven channels: four solar channels to measure visible and short-wave infrared wavelengths, and three channels to measure infrared thermal emission. In this paper, we describe the optical layout of the infrared instrument channels, thin-film multilayer designs, the coating deposition method, and the spectral system throughput for the bandpass interference filters, dichroic beam splitters, lenses, and mirror coatings to discriminate wavelengths at 8.8, 10.8, and 12.0μm. The rationale for the selection of thin-film materials, spectral measurement technique, and environmental testing performance are also presented.

Instrument an Off-Shelf CCD Imaging Sensor Into a Handheld Multispectral Video Camera

In this letter, we report the feasibility study of converting an ordinary off-shelf charge-coupled device (CCD) imaging sensor into a multispectral imaging instrument, using the multispectral bruises detection as an example. The resulting handheld device is capable of producing in real-time multiple pixel-registered images centered at different wavelengths in a single exposure, thereby providing overwhelming operational convenience. The results are advantageous for the development of a handheld narrowband multispectral imaging video camera for image guided medical surgery.

Analysis of acousto-optic tunable filter performance for imaging applications

Acousto-optic tunable filters (AOTFs) can be used as spectral filters in multispectral imaging applications. Acousto-optic crystals diffract a single wavelength from a broadband light beam, depending on the applied radio frequency signal. However, experimental measurements show that the actual performance is far from the expected behavior. We present an experimental characterization of several commercial off-the-shelf AOTFs for the implementation of multispectral imaging instruments. The diffraction performance of three bare crystals is compared, while a fourth AOTF crystal is mounted on the optical path of a multispectral imager to evaluate its performance. The experiments show that the behavior of all the analyzed AOTFs differs from the theoretical expectations and presents uneven diffraction efficiency, a Gaussian dependency with the applied power, and a strong nonlinear relationship with the driving signal frequency. The different behavior of each AOTF in terms of all the analyzed parameters shows the necessity for an in-depth characterization of the AOTF performance once mounted on a multispectral imaging device if quantitative measurements are required. Finally, several recommendations for use are derived from these experimental results.

Improving the performance of acousto-optic tunable filters in imaging applications

Acousto-optic tunable filters (AOTFs) can be used as spectral filters for the implementation of multispectral imaging systems. However, obtaining quality images is challenging. In this work, we propose several improvements that enable the use of these systems in quantitative spectroscopic imaging applications. The improvements are based on three pillars: 1. a finer spectral bandpass shaping by dynamically optimizing the radio frequency (rf) driving signal, 2. an extensive calibration process, and 3. careful image preprocessing that uses calibration data to correct some well known AOTF issues in imaging applications. A novel multispectral imaging instrument is built using commercial off-the-shelf components. The instrument includes an Isomet (Springfield, New Jersey) AOTF working in the visible and near-infrared range, and a new concept of rf generator based on a high-speed digital-to-analog converter that allows the generation of multiband signals. The ancillary control software performs the main part of the image optimization process: an initial calibration, a dynamic adjustment of the rf driving signal power and exposure time, and finally the radiometric preprocessing of the acquired multispectral images. Finally, some results of the instrument performance are presented that show the achieved spectral and spatial resolution on different imaging scenarios.