Set Of Multispectral Images Research Articles

Using Remote Sensing Multispectral Imagery for Invasive Species Quantification: The Effect of Image Resolution on Area and Biomass Estimation

This study assesses the applicability of different-resolution multispectral remote sensing images for mapping and estimating the aboveground biomass (AGB) of Carpobrotus edulis, a prominent invasive species in European coastal areas. This study was carried out on the Cávado estuary sand spit (Portugal). The performance of three sets of multispectral images with different Ground Sample Distances (GSDs) were compared: 2.5 cm, 5 cm, and 10 cm. The images were classified using the supervised classification algorithm random forest and later improved by applying a sieve filter. Samples of C. edulis were also collected, dried, and weighed to estimate the AGB using the relationship between the dry weight (DW) and vegetation indices (VIs). The resulting regression models were evaluated based on their coefficient of determination (R2), Normalised Root Mean Square Error (NRMSE), p-value, Akaike information criterion (AIC), and the Bayesian information criterion (BIC). The results show that the three tested image resolutions allow for constructing reliable coverage maps of C. edulis, with overall accuracy values of 89%, 85%, and 88% for the classification of the 2.5 cm, 5 cm, and 10 cm GSD images, respectively. The best-performing VI-DW regression models achieved R2 = 0.87 and NRMSE = 0.09 for the 2.5 cm resolution; R2 = 0.77 and NRMSE = 0.12 for the 5 cm resolution; and R2 = 0.64 and NRMSE = 0.15 for the 10 cm resolution. The C. edulis area and total AGB were 3441.10 m2 and 28,327.1 kg (with an AGB relative error (RE) = 0.08) for the 2.5 cm resolution; 3070.04 m2 and 29,170.8 kg (AGB RE = 0.08) for the 5 cm resolution; and 2305.06 m2 and 22,135.7 kg (AGB RE = 0.11) for the 10 cm resolution. Spatial and model differences were analysed in detail to determine their causes. Final analyses suggest that multispectral imagery of up to 5 cm GSD is adequate for estimating C. edulis distribution and biomass.

ELSSIE: A compact stereo spectral imager for planetary surface morphology and composition

Here we describe the design, prototyping, testing, and simulations that were conducted to demonstrate the technology for a concept of the next generation landed planetary spectral imager, the Europa Lander Stereo Spectral Imaging Experiment (ELSSIE). The concept was developed originally for a Europa Lander mission, but the design is applicable, with simplifications, to any ocean world of the outer solar system or to non-icy bodies, including Enceladus, the Moon, Mars, or the surface of Ceres. ELSSIE's design consists of two subassemblies. A Sensor melds a high-resolution, 20-filter, 0.4–3.65 μm, adjustable-focus multispectral stereo imager with a 0.8–3.6 μm point spectrometer, sharing a radiation-shielded single Teledyne H2RG 2048 × 2048 pixel focal plane array (FPA). Each camera includes two 6-position filter wheels with 5 filters and a blank position, providing 10 bandpasses for each of the 2 stereo eyes, and uses 700 × 700 pixels of the FPA. The point spectrometer uses a 6 ×350 pixel strip of the FPA. The Sensor provides stereo and imaging/spectroscopic measurements of reflected light from visible to medium wave-infrared (MWIR) wavelengths to characterize surface morphology, search for pyroclastic plumes, search for organics, identify salts and possible biominerals, characterize crystalline vs. amorphous ice and ice grain sizes, and map the distributions of key phases. In addition to addressing important geologic questions, these measurements support selection of a site for in situ sampling and analysis. A Data Processing Unit (DPU) performs mitigation of radiation that penetrates the shielding using sets of same-filter image frames or spectra of a single spot by removing image spatial pixels with radiation hits, and coadding the remainder for the same spatial pixel, improving signal-to-noise ratio (SNR). The DPU also performs onboard calibration of imager and spectrometer data, co-registration of multispectral images, and calculation of spectral index (“summary parameter”) images for efficient use of lander downlink. Co-registered multispectral image sets and spectra are retained onboard and can be downlinked upon query.

The Mastcam‐Z Radiometric Calibration Targets on NASA's Perseverance Rover: Derived Irradiance Time‐Series, Dust Deposition, and Performance Over the First 350 Sols on Mars

AbstractThe Mastcam‐Z radiometric calibration targets mounted on the NASA's Perseverance rover proved to be effective in the calibration of Mastcam‐Z images to reflectance (I/F) over the first 350 sols on Mars. Mastcam‐Z imaged the calibration targets regularly to perform reflectance calibration on multispectral image sets of targets on the Martian surface. For each calibration target image, mean radiance values were extracted for 41 distinct regions of the targets, including patches of color and grayscale materials. Eight strong permanent magnets, placed under the primary target, attracted magnetic dust and repelled it from central surfaces, allowing the extraction of radiance values from eight regions relatively clean from dust. These radiances were combined with reflectances obtained from laboratory measurements, a one‐term linear fit model was applied, and the slopes of the fits were retrieved as estimates of the solar irradiance and used to convert Mastcam‐Z images from radiance to reflectance. Derived irradiance time series are smoothly varying in line with expectations based on the changing Mars‐Sun distance, being only perturbed by a few significant dust events. The deposition of dust on the calibration targets was largely concentrated on the magnets, ensuring a minimal influence of dust on the calibration process. The fraction of sunlight directly hitting the calibration targets was negatively correlated with the atmospheric optical depth, as expected. Further investigation will aim at explaining the origin of a small offset observed in the fit model employed for calibration, and the causes of a yellowing effect affecting one of the calibration targets materials.

An out-of-core method for GPU image mapping on large 3D scenarios of the real world

Image mapping on 3D huge scenarios of the real world is one of the most fundamental and computational expensive processes for the integration of multi-source sensing data. Recent studies focused on the observation and characterization of Earth have been enhanced by the proliferation of Unmanned Aerial Vehicle (UAV) and sensors able to capture massive datasets with a high spatial resolution. Despite the advances in manufacturing new cameras and versatile platforms, only a few methods have been developed to characterize the study area by fusing heterogeneous data such as thermal, multispectral or hyperspectral images with high-resolution 3D models. The main reason for this lack of solutions is the challenge to integrate multi-scale datasets and high computational efforts required for image mapping on dense and complex geometric models. In this paper, we propose an efficient pipeline for multi-source image mapping on huge 3D scenarios. Our GPU-based solution significantly reduces the run time and allows us to generate enriched 3D models on-site. The proposed method is out-of-core and it uses available resources of the GPU’s machine to perform two main tasks: (i) image mapping and (ii) occlusion testing. We deploy highly-optimized GPU-kernels for image mapping and detection of self-hidden geometry in the 3D model, as well as a GPU-based parallelization to manage the 3D model considering several spatial partitions according to the GPU capabilities. Our method has been tested on 3D scenarios with different point cloud densities (66M, 271M, 542M) and two sets of multispectral images collected by two drone flights. We focus on launching the proposed method on three platforms: (i) System on a Chip (SoC), (ii) a user-grade laptop and (iii) a PC. The results demonstrate the method’s capabilities in terms of performance and versatility to be computed by commodity hardware. Thus, taking advantage of GPUs, this method opens the door for embedded and edge computing devices for 3D image mapping on large-scale scenarios in near real-time.

Deep learning segmentation of gadolinium-enhancing lesions in multiple sclerosis

Objective: The aim of this study is to assess the performance of deep learning convolutional neural networks (CNNs) in segmenting gadolinium-enhancing lesions using a large cohort of multiple sclerosis (MS) patients. Methods: A three-dimensional (3D) CNN model was trained for segmentation of gadolinium-enhancing lesions using multispectral magnetic resonance imaging data (MRI) from 1006 relapsing–remitting MS patients. The network performance was evaluated for three combinations of multispectral MRI used as input: (U5) fluid-attenuated inversion recovery (FLAIR), T2-weighted, proton density-weighted, and pre- and post-contrast T1-weighted images; (U2) pre- and post-contrast T1-weighted images; and (U1) only post-contrast T1-weighted images. Segmentation performance was evaluated using the Dice similarity coefficient (DSC) and lesion-wise true-positive (TPR) and false-positive (FPR) rates. Performance was also evaluated as a function of enhancing lesion volume. Results: The DSC/TPR/FPR values averaged over all the enhancing lesion sizes were 0.77/0.90/0.23 using the U5 model. These values for the largest enhancement volumes (>500 mm3) were 0.81/0.97/0.04. For U2, the average DSC/TPR/FPR values were 0.72/0.86/0.31. Comparable performance was observed with U1. For all types of input, the network performance degraded with decreased enhancement size. Conclusion: Excellent segmentation of enhancing lesions was observed for enhancement volume ⩾70 mm3. The best performance was achieved when the input included all five multispectral image sets.

Beyond Color Correction : Skin Color Estimation In The Wild Through Deep Learning

In this paper we present a set of multispectral images covering the visible and near-infrared spectral range (400 nm to 1050 nm). This dataset intends to provide spectral reflectance images containing daily life objects, usable for silicon image sensor simulations. All images were taken with our acquisition bench and a particular attention was brought to processings in order to provide calibrated reflectance data. ReDFISh (Reflectance Dataset For Image sensor Simulation) is available at: http://dx.doi.org/10.18709/perscido.2020.01.ds289.

True Sensor Captured Radiance and Surface Reflectance from Landsat-8 Multispectral Image sets

Proper validation of multispectral image sets that are produced by Satellite Sensors, in this era of big-data computing, is an important aspect in signal processing of natural semantic scenes. In this research article we explore fundamental issues that are cropped up in radiance correction and surface reflectance measurements. We calibrate the raw Landsat-8 imagery digital numbers by using standard functional oriented modular programs in R software, for true sensor radiance and reflectance values. This important preprocessing step is essential for activities that use multispectral images for natural resources monitoring and change-detection studies. It is noted that the procedures for preprocessing can be applied in machine learning algorithms for scientific studies.

A Comparative Study of Supervised Classification Techniques for Multi-Spectral Images

Classification of satellite images is an important key for ground features extraction and thematic maps production. Satellite images with multi-spectral bands provide rich data which is useful for features extraction and description. Many supervised classification methods have been developed for classifying the multispectral images. Each method has its own advantages and disadvantages (limitations). In this paper the performance of four of the common used supervised classification techniques is compared. The techniques considered here are: Parallelepiped (PP), Minimum Distance (MD), Mahalanobies (MA), and Maximum Likelihood (ML). They are applied on a set of multispectral images acquired by Worldview-2 satellite. The classification results accuracy are analyzed and evaluated The research work flow is processed by using ENVI. The developed maps are then visually compared with each other and accuracy assessments utilizing ground-truths. The assessment of classification results is represented in confusion matrix format and determination of Kappa coefficients. The preliminary results show that Maximum Likelihood (ML) gives accurate classification result for the area of study with overall accuracy 91.5741% and it is evaluated by Kappa coefficient which is 0.8846:

Multispectral image enhancement based on Retinex by using structure extraction

Applications in industrial and scientific areas need high-quality multispectral images. However, because of various disadvantages, e.g., adverse environment of sensing, limited spectral bands, etc., multispectral images suffer low contrasts, low signal-noise-ratios, etc. As inputs of applications such as tracking, recognition and so on, multispectral images with low quality may cause those applications to fail. In this paper, aiming to meet practical requirements, we propose an algorithm of efficiently improving miultispectral images. The framework of the proposed method is as follows. According to Retinex theory, a multispectral image can be modeled as multiplicative combination of an illumination component and a reflection component. Typically, an illumination component is of low frequency and determines the dynamic range of pixel intensity, while a reflection component is of high frequency and determines the property of an image. Once we can successfully enhance both an illumination component and a reflection component, which is described later, respectively, we achieve the enhancement of a multispectral image by multiplying two enhanced components. First, we estimate the illumination component based on the principal structures extracted from the multispectral image on a multiple scale, i.e. on a low-level scale, middle-level scale and high-level scale, respectively. The mean value of the three principal structures is used as the estimation of an illumination component. Then the global structure contained in the illumination component, can be obtained by analyzing the corresponding information about its histogram, and is involved to enhance the global contrast and the edge details of the principal structure. Second, with the previously computed illumination component, we can easily derive the reflection component from the multispectral image in pixel-wise division operation. There are adequate image details as well as noise in the reflection component. We suppress the noise and keep the image details by using a non local mean filter. And then we enhance the image details by means of local variances. Finally, multiplying the enhanced illumination component by the filtered reflection component, we enhance the multispectral image. In order to verify the efficiency of our algorithm, experiments are conducted over multispectral image sets including X-ray images, ultraviolet images, well illuminated visible light images, poorly illuminated visible light image, and infrared images. The experimental results show that the proposed algorithm can efficiently remove halo artifacts, well suppress noise and obviously improve local details as well as global contrast. Compared with the state-of-the-art algorithms, the proposed method significantly enhances multispectral images both in vision and in objective analysis by means of information entropy and average gradient.

Detection and Compensation of Band-to-band Registration Error for Multi-spectral Imagery Caused By Satellite Jitter

Abstract. Satellite jitter has become a more and more important factor which affects the quality of imagery products with development of the high resolution satellite. This paper focused on analyzing the impact on multi-spectral image caused by satellite jitter and proposed a jitter detection and compensation method to improve the band-to-band registration efficiently when jitter exists without observation by attitude sensor. Firstly, the design of multi-spectral camera and the mainstream band-to-band registration method is introduced to explain factors influencing the registration accuracy. As one of factors, satellite jitter is an unexpected satellite movement and do have impact on registration on both across and along track but easy to be ignored for the lack of high frequency and accuracy attitude data. So next the jitter detection and compensation method is proposed, in which there are six main steps to achieve the analysis of registration accuracy with and without jitter and improvement of registration accuracy after compensation when the jitter cannot be ignored. Finally, three sets of multi-spectral images of ZY-3 were used to verify the proposed method. As a result, the error caused by satellite jitter was suppressed efficiently from 0.2pixels to 0.02pixels and registration accuracy (RMSE) was improved from 0.32 pixels to 0.11 pixels by the proposed method. The results indicate that the proposed method can detect and compensate the distortion of multi-spectral image caused by satellite jitter accurately and efficiently.

Unsupervised Multi-Spectral Satellite Image Segmentation Combining Modified Mean-Shift and a New Minimum Spanning Tree Based Clustering Technique

An unsupervised object based segmentation, combining a modified mean-shift (MS) and a novel minimum spanning tree (MST) based clustering approach of remotely sensed satellite images has been proposed in this correspondence. The image is first pre-processed by a modified version of the standard MS based segmentation which preserves the desirable discontinuities present in the image and guarantees oversegmentation in the output. A nearest neighbor based method for estimating the bandwidth of the kernel density estimator (KDE) and a novel termination condition have been incorporated into the standard MS. Considering the segmented regions as nodes in a low level feature space, an MST is constructed. An unsupervised technique to cluster a given MST has also been devised here. This type of hybrid segmentation technique which clusters the regions instead of image pixels reduces greatly the sensitivity to noise and enhances the overall segmentation performance. The superiority of the proposed method has been experimented on a large set of multi-spectral images and compared with some well-known hybrid segmentation models.

Modifications of iterative schemes used for curvature correction in noninvasive biomedical imaging

Iterative polynomial fitting along image rows and columns has recently been used to remove curvature bias in multispectral image sets of the human forearm and phantoms. However, this method is only applicable if foreground and background features satisfy strong separation conditions. In this method, we verify that the iterative polynomial approach converges toward bivariate polynomial fitting, and, hence, the resulting fit corresponds to low-pass filtering the image. In contrast to the iterative fitting, the bivariate polynomial fit can be performed on images with missing or excluded parts. Indeed, our observation enables us to modify the scheme and significantly weaken the required assumptions on foreground/background separation allowing a wider range of application.

Mitigating the effect of optical back-scatter in multispectral underwater imaging

Multispectral imaging is a very useful technique for extracting information from the underwater world. However, optical back-scatter changes the intensity value in each spectral band and this distorts the estimated spectrum. In this work, a filter is used to detect the level of optical back-scatter in each spectral band from a set of multispectral images. Extraction of underwater object spectra can be done by subtracting the estimated level of optical back-scatter and scaling the remainder in each spectral band from the captured image in the corresponding band. An experiment has been designed to show the performance of the proposed filter for correcting the set of multispectral underwater images and recovering the pixel spectra. The multispectral images are captured by a B/W CCD digital camera with a fast tunable liquid-crystal filter in 33 narrow spectral bands in clear and different levels of turbid water. Reference estimates for the optical back-scatter spectra are found by comparing a clear and a degraded set of multispectral images. The accuracy and consistency of the proposed method, the extended Oakley–Bu cost function, is examined by comparing the estimated values with the reference level of an optical back-scatter spectrum. The same comparison is made for the simple estimation approach. The results show that the simple method is not reliable and fail to estimate the level of optical back-scatter spectrum accurately. The results from processing experimental images in turbid water show that the effect of optical back-scatter can be mitigated in the image of each spectral band and, as a result, the spectra of the object can be recovered. However, for a very high level of turbid water the recovery is limited because of the effect of extinction.

Imaging the Syriac Galen Palimpsest: preliminary analysis and future prospects

The Syriac Galen Palimpsest is a remarkable, privately owned manuscript that contains the Syriac version of Galen’s Simple drugs by Sergius of Rēš ‘Aynā (d. 536). The anonymous owner commissioned a full set of multi-spectral images and made them freely available. The available combined and enhanced images of the undertext, however, leave many areas of poor readability. The present article reports on attempts to improve readability by using sophisticated algorithms (Canonical Variate Analysis). The initial results are encouraging, and additional image capture in the infrared area with wavelengths longer than 940 nm may also be useful.

Transillumination Imaging for Blood Oxygen Saturation Estimation of Skin Lesions

Detecting the early stages of melanoma can be greatly assisted by an accurate estimate of subsurface blood volume and blood oxygen saturation, indicative of angiogenesis. Visualization of this blood volume present beneath a skin lesion can be achieved through the transillumination of the skin. As the absorption of major chromophores in the skin is wavelength dependent, multispectral imaging can provide the needed information to separate out relative amounts of each chromophore. However, a critical challenge to this strategy is relating the pixel intensities observed in a given image to the wavelength-dependent total absorption existing at each spatial location. Consequently, in this paper, we develop an extension to Beer's law, estimated through a novel voxel-based, parallel processing Monte Carlo simulation of light propagation in skin which takes into account the specific geometry of our transillumination imaging apparatus. We then use this relation in a linear mixing model, solved using a multispectral image set, for chromophore separation and oxygen saturation estimation of an absorbing object located at a given depth within the medium. Validation is performed through the Monte Carlo simulation, as well as by imaging on a skin phantom. Results show that subsurface oxygen saturation can be reasonably estimated with good implications for the reconstruction of 3-D skin lesion volumes using transillumination toward early detection of malignancy.

Analysis and selection of pan-sharpening assessment measures

Pan-sharpening of remote sensing multispectral imagery directly influences the accuracy of interpretation, classification, and other data mining methods. Different tasks of multispectral image analysis and processing require specific properties of input pan-sharpened multispectral data such as spectral and spatial consistency, complexity of the pan-sharpening method, and other properties. The quality of a pan-sharpened image is assessed using quantitative measures. Generally, the quantitative measures for pan-sharpening assessment are taken from other topics of image processing (e.g., image similarity indexes), but the applicability basis of these measures (i.e., whether a measure provides correct and undistorted assessment of pan-sharpened imagery) is not checked and proven. For example, should (or should not) a quantitative measure be used for pan-sharpening assessment is still an open research topic. Also, there is a chance that some measures can provide distorted results of the quality assessment and the suitability of these quantitative measures as well as the application for pan-sharpened imagery assessment is under question. The aim of the authors is to perform statistical analysis of widely employed measures for remote sensing imagery pan-sharpening assessment and to show which of the measures are the most suitable for use. To find and prove which measures are the most suitable, sets of multispectral images are processed by the general fusion framework method (GFF) with varying parameters. The GFF is a type of general image fusion method. Variation of the method parameter set values allows one to produce imagery data with predefined quality (i.e., spatial and spectral consistency) for further statistical analysis of the assessment measures. The use of several main multispectral sensors (Landsat 7ETM+, IKONOS, and WorldView-2) imagery allows one to assess and compare available quality assessment measures and illustrate which of them are most suitable for each satellite.

Superresolution Reconstruction of Multispectral Data for Improved Image Classification

In this letter, the application of superresolution (SR) techniques to multispectral image clustering and classification is investigated and tested using satellite data. A set of multispectral images with better spatial resolution is obtained after an SR technique is applied to several data sets recorded within a short period over a study area. Improved clustering and classification performance is demonstrated visually and quantitatively by comparison with the original low-resolution data or enlarged images using a conventional interpolation method. This letter illustrates the possibility and feasibility of the use of SR reconstruction for the classification of remote sensing data, which is encouraging as a means of breaking through current satellite detectors' resolution limits.

English

This paper looks at two methods for visualizing and mapping vegetation change on a large surface mining complex in southern West Virginia. Two Quickbird image sets, acquired in 2003 and 2007, were used to identify vegetation trends and to map significant change events manifested during the four year interval. Vegetation trends were visualized using Normalized Difference Vegetation Index (NDVI) difference images, which proved to be a simple and effective means of identifying vegetation change events for further investigation. The study then evaluated Feature Analyst—a commercial analysis package—for its ability to map and quantify two of the most significant change events identified in the study area. A field investigation used handheld GPS receivers equipped with ArcPad software and GPS-equipped cameras to verify the cause of the two events—1) the defoliation of stands of black locust, and 2) a significant increase in area dominated by deciduous shrub vegetation, caused by rapid growth in autumn olive. Feature Analyst was able to delineate the extent of black locust defoliation, estimated at over 152 acres on several reclaimed permits. In the second case, the analysis estimated that deciduous shrub cover expanded from 32 acres to over 81 acres on one permit, representing an increase from 6.5% to 16.5% of the total permit area. Feature Analyst showed significant promise for extracting vegetation features from the source images, including individual trees. Feature Analyst's ability to effectively utilize panchromatic and multispectral image sets suggests it is an effective tool for use with increasingly high resolution data available from commercial vendors.

Digital terrain analysis using Landsat‐7 ETM+ imagery and SRTM DEM: a case study of Nevsehir province (Cappadocia), Turkey

A three‐dimensional (3D) model of land‐use/land‐cover (LULC) and a digital terrain model of Nevsehir province (Cappadocia), Turkey, were generated and analysed using a Landsat‐7 Enhanced Thematic Mapper Plus (ETM+) multispectral image set and a Shuttle Radar Topographic Mission (SRTM) digital elevation model (DEM). Stream drainage patterns, lineaments and structural‐geological features (landforms) were extracted and analysed. In the process of analysing and interpreting the multispectral images of geological features, criteria such as colour and colour tones, topography and stream drainage patterns were used to acquire information about the geological structures of the land, including as geomorphological, topographic and tectonic structures. Landsat‐7 ETM+ multispectral imagery and an SRTM DEM of the study region were used experimentally for classification and analysis of a digital terrain model. Using the multispectral image data, the LULC types were classified as: settlement (1.2%); agricultural land (70.1%); forest (scrubland, orchard and grassland) (2.9%); bare ground (25.5%); and water bodies (lakes and rivers) (0.3%) of the study area (5434 km2). The results of the DEM classification in the study area were: river flood plain (11.3%); plateau (52.3%); high plateau (28.4%); mountain (7.6%); and high mountain (0.3%). Lineament analysis revealed that the central Kizilirmak River divides the region into two nearly equal parts: the Kirsehir Plateau in the north and the Nevsehir Plateau in the south. In terms of the danger of catastrophe, the settlements of Kozakli, Hacibektas and Acigol were found to be at less risk of earthquake and/or flooding than those of Avanos, Gulsehir, Urgup, Nevsehir, Gumuskent and Derinkuyu, which are located on river flood plains and/or the main stream drainage channels, particularly stream beds, where the lineaments are deep valleys or fracture or fault‐line indicators.

Blue-green spectral minimum correlates with oxyhemoglobin saturation in vivo

An imaging multi-spectral retinal oximeter with intravitrial illumination is used to perform the first in vivo test of the blue-green minima shift oximetry method (BGO) in swine eyes [K. R. Dennighoff, R. A. Chipman, and L. W. Hillman, Opt. Lett. 31, 924-926 (2006); J. Biomed. Opt. 12, 034020 (2007).] A fiber optic intravitreal illuminator inserted through the pars plana was coupled to a monochromator and used to illuminate the retina from an angle. A camera viewing through the cornea recorded a series of images at each wavelength. This intravitreal light source moves the specular vessel glint away from the center of the vessel and directly illuminates the fundus behind most blood vessels. These two conditions combine to provide accurate measurements of vessel and perivascular reflectance. Equations describing these different light paths are solved, and BGO is used to evaluate large retinal vessels. In order to test BGO calibration in vivo, data were acquired from swine with varied retinal arterial oxyhemoglobin saturations (60-100% saturation.). The arterial saturations determined using BGO to analyze the multispectral image sets showed excellent correlation with co-oximeter data (r2=0.98, and residual error +/-3.4% saturation) and are similar to results when hemoglobin and blood were analyzed using this technique.