Multispectral Signals Research Articles

Identification of tissular origin of particles based on autofluorescence multispectral image analysis at the macroscopic scale

Powders produced from plant materials are heterogeneous in relation to native plant heterogeneity, and during grinding, dissociation often occurred at the tissue scale. The tissue composition of powdery samples could be modified through dry fractionation diagrams and impact their end-uses properties. If tissue identification is often made on native plant structure, this characterization is not straightforward in destructured samples such powders. Taking advantage of the autofluorescence properties of cell wall components, multispectral image acquisition is envisioned to identify the tissular origin of particles. Images were acquired on maize stem sections and ground tissues isolated from the same stem by hand dissection. The variability in fluorescence intensity profiles was analysed using principal component analysis. The correspondence between fluorescence profiles and the different tissues observed in maize sections was assessed based on histology or known compositional heterogeneity. Similar variability was encountered in fluorescence profiles extracted from powder leading to the potential ability to predict tissular origin based on this autofluorescence multispectral signal.

Characterization of grounds, pigments, binding media, and varnish coating of the Angel Michael' icon, 18th century, Egypt

Samples from canvas, grounds, pigments, and varnish coating from the icon of Angel Michael came back to the 18th century from the church of Samanod, Gharbeia Governorate, Egypt, were investigated using multispectral signals of SEM-EDX, FT-IR, and Mass spectrometry. The SEM data from the canvas indicated that it is of linen. EDX of the grounds indicated that they are of gypsum with natural impurities of halite, quartz, and clay minerals. FT-IR spectra of the grounds indicated that the binding medium was of animal glue where gypsum was mixed with animal glue. The SEM-EDX pattern of the orange pigment showed the presence of lead, minium (Pb3O4) is the most probable, the blue color was Egyptian blue, whereas tetrahyrate [(Cu, Fe, Zn, Ag)12Sb4S18)] may be used as a source of copper and crushed limestone was used as a source of flux. The gold color is composed red lead mixed with titanium oxide. The icon paintings were coated with a resinous layer of dammar, which had darkened into a brown color by photoxidation.

Colour-agnostic shape-based 3D fruit detection for crop harvesting robots

Most agricultural robots, fruit harvesting systems in particular, use computer vision to detect their fruit targets. Exploiting the uniqueness of fruit colour amidst the foliage, almost all of these computer vision systems rely on colour features to identify the fruit in the image. However, often the colour of fruit cannot be discriminated from its background, especially under unstable illumination conditions, thus rendering the detection and segmentation of the target highly sensitive or unfeasible in colour space. While multispectral signals, especially those outside the visible spectrum, may alleviate this difficulty, simpler, cheaper, and more accessible solutions are desired. Here exploiting both RGB and range data to analyse shape-related features of objects both in the image plane and 3D space is proposed. In particular, 3D surface normal features, 3D plane-reflective symmetry, and image plane highlights from elliptic surface points are combined to provide shape-based detection of fruits in 3D space regardless of their colour. Results are shown using a particularly challenging sweet pepper dataset with a significant degree of occlusions.

Spectral Unmixing of Multispectral Lidar Signals

In this paper, we present a Bayesian approach for spectral unmixing of multispectral Lidar (MSL) data associated with surface reflection from targeted surfaces composed of several known materials. The problem addressed is the estimation of the positions and area distribution of each material. In the Bayesian framework, appropriate prior distributions are assigned to the unknown model parameters and a Markov chain Monte Carlo method is used to sample the resulting posterior distribution. The performance of the proposed algorithm is evaluated using synthetic MSL signals, for which single and multi-layered models are derived. To evaluate the expected estimation performance associated with MSL signal analysis, a Cramer-Rao lower bound associated with model considered is also derived, and compared with the experimental data. Both the theoretical lower bound and the experimental analysis will be of primary assistance in future instrument design.

采用时序多谱段信号分析的空间目标运动状态辨识

采用时序多谱段信号分析的空间目标运动状态辨识

Multitemporal Fusion for the Detection of Static Spatial Patterns in Multispectral Satellite Images—With Application to Archaeological Survey

We evaluate and further develop a multitemporal fusion strategy that we use to detect the location of ancient settlement sites in the Near East and to map their distribution, a spatial pattern that remains static over time. For each ASTER images that has been acquired in our survey area in northeastern Syria, we use a pattern classification strategy to map locations with a multispectral signal similar to the one from (few) known archaeological sites nearby. We obtain maps indicating the presence of anthrosol-soils that formed in the location of ancient settlements and that have a distinct spectral pattern under certain environmental conditions-and find that pooling the probability maps from all available time points reduces the variance of the spatial anthrosol pattern significantly. Removing biased classification maps-i.e., those that rank last when comparing the probability maps with the (limited) ground truth we have- reduces the overall prediction error even further, and we estimate optimal weights for each image using a nonnegative least squares regression strategy. The ranking and pooling strategy approach we propose in this study shows a significant improvement over the plain averaging of anthrosol probability maps that we used in an earlier attempt to map archaeological sites in a 20 000-km 2 area in northern Mesopotamia, and we expect it to work well in other surveying tasks that aim in mapping static surface patterns with limited ground truth in long series of multispectral images.

Within-Species Benefits of Back-projecting Airborne Laser Scanner and Multispectral Sensors in Monospecific Pinus sylvestris Forests

Back-projecting is an alternative to orthorectification for ALS-imagery fusion. It usually assists in improving forest estimations in mixed forests, by adding species information from optical sensors. In this study, we focused on the within-species advantages obtained. Results showed that estimating relative stem density improved significantly (from R2=0.76 to R2=0.81), as the multispectral signal may incorporate canopy closure-related shadowing conditions at plot-level. As a result, volume prediction also improved (from R2=0.65 to R2=0.69), even though Lorey's height and basal area did not. Hence, monospecific conifer forests assessment may also benefit from ALS-imagery fusion.

Wavelength-Resolved Flow Cytometer Under a Dark-Field Illumination Configuration

This paper presents a novel yet high-performance diascopic illumination configuration for simultaneously detecting cells of different sizes and different fluorescence labeling in a microfluidic chip. An objective-type dark-field condenser equipped with a low-cost tungsten bulb light source with continuous wavelengths (400 to 900 nm) and an UV-Vis-NIR spectrometer is used for detecting the multispectral signals from various particles and cells. With this approach, continuous cell counting and spectra analyzing are realized in a single channel without using any spatial filter and delicate optical detectors. Results show that the developed system is capable of identifying different labeled particle and cell samples by extracting the information side-scatter, absorption, and fluorescence from the information-rich spectra. This proposed system provides an efficient multicolor detection for identification and classification of a microflow cytometer chip.

Detection of bacterial wilt infection caused by Ralstonia solanacearum in potato (Solanum tuberosum L.) through multifractal analysis applied to remotely sensed data

Potato bacterial wilt, caused by the bacterium Ralstonia solanacearum race 3 biovar 2 (R3bv2), affects potato production in several regions in the world. The disease becomes visually detectable when extensive damage to the crop has already occurred. Two greenhouse experiments were conducted to test the capability of a remote sensing diagnostic method supported by multispectral and multifractal analyses of the light reflectance signal, to detect physiological and morphological changes in plants caused by the infection. The analysis was carried out using the Wavelet Transform Modulus Maxima (WTMM) combined with the Multifractal (MF) analysis to assess the variability of high-resolution temporal and spatial signals and the conservative properties of the processes across temporal and spatial scales. The multispectral signal, enhanced by multifractal analysis, detected both symptomatic and latently infected plants, matching the results of ELISA laboratory assessment in 100 and 82%, respectively. Although the multispectral method provided no earlier detection than the visual assessment on symptomatic plants, the former was able to detect asymptomatic latent infection, showing a great potential as a monitoring tool for the control of bacterial wilt in potato crops. Applied to precision agriculture, this capability of the remote sensing diagnostic methodology would provide a more efficient control of the disease through an early and full spatial assessment of the health status of the crop and the prevention of spreading the disease.

Digital spectral separation methods and systems for bioluminescence imaging

We propose a digital spectral separation (DSS) system and methods to extract spectral information optimally from a weak multi-spectral signal such as in the bioluminescent imaging (BLI) studies. This system utilizes our newly invented spatially-translated spectral-image mixer (SSM), which consists of dichroic beam splitters, a mirror, and a DSS algorithm. The DSS approach overcomes the shortcomings of the data acquisition scheme used for the current BLI systems. Primarily, using our DSS scheme, spectral information will not be filtered out. Accordingly, truly parallel multi-spectral multi-view acquisition is enabled for the first time to minimize experimental time and optimize data quality. This approach also permits recovery of the bioluminescent signal time course, which is useful to study the kinetics of multiple bioluminescent probes using multi-spectral bioluminescence tomography (MSBT).

Performance evaluation of dimensionality reduction techniques for multispectral images

We consider several collections of multispectral color signals and describe how linear and nonlinear methods can be used to investigate their internal structure. We use databases consisting of blac...

Recent Development in Bioluminescence Tomography

Bioluminescence tomography (BLT) is a new molecular imaging tool. Using a modality fusion approach, we built the first BLT prototype that combines BLI data and micro-CT and micro-MRI images for proof of concept, established a theoretical framework for BLT, and reported encouraging preliminary results. In this overview, we highlight our key results and discuss further directions. Keywords: Bioluminescence imaging, Bioluminescence tomography, Finite Element Method, Monte Carlo approach, Molecular optical simulation environment, Multi-spectral signal separation

Annotated regions of significance of SELDI-TOF-MS spectra for detecting protein biomarkers

Peak detection is a key step in the analysis of SELDI-TOF-MS spectra, but the current default method has low specificity and poor peak annotation. To improve data quality, scientists still have to validate the identified peaks visually, a tedious and time-consuming process, especially for large data sets. Hence, there is a genuine need for methods that minimize manual validation. We have previously reported a multi-spectral signal detection method, called RS for 'region of significance', with improved specificity. Here we extend it to include a peak quantification algorithm based on annotated regions of significance (ARS). For each spectral region flagged as significant by RS, we first identify a dominant spectrum for determining the number of peaks and the m/z region of these peaks. From each m/z region of peaks, a peak template is extracted from all spectra via the principal component analysis. Finally, with the template, we estimate the amplitude and location of the peak in each spectrum with the least-squares method and refine the estimation of the amplitude via the mixture model. We have evaluated the ARS algorithm on patient samples from a clinical study. Comparison with the standard method shows that ARS (i) inherits the superior specificity of RS, and (ii) gives more accurate peak annotations than the standard method. In conclusion, we find that ARS alleviates the main problems in the preprocessing of SELDI-TOF spectra. The R-package ProSpect that implements ARS is freely available for academic use at http://www.meb.ki.se/ yudpaw.

Forest change detection by statistical object-based method

Forest monitoring requires more automated systems to analyse the large amount of remote sensing data. A new method of change detection is proposed for identifying forest land cover change using high spatial resolution satellite images. Combining the advantages of image segmentation, image differencing and stochastic analysis of the multispectral signal, this OB-Reflectance method is object-based and statistically driven. From a multidate image, a single segmentation using region-merging technique delineates multidate objects characterised by their reflectance differences statistics. Objects considered as outliers from multitemporal point of view are successfully discriminated thanks to a statistical procedure, i.e., the iterative trimming. Based on a chi-square test of hypothesis, abnormal values of reflectance differences statistics are identified and the corresponding objects are labelled as change. The object-based method performances were assessed using two sources of reference data, including one independent forest inventory, and were compared to a pixel-based method using the RGB-NDVI technique. High detection accuracy (> 90%) and overall Kappa (> 0.80) were achieved by OB-Reflectance method in temperate forests using three SPOT-HRV images covering a 10-year period.

On the residual term in the linear mixture model and its dependence on the point spread function

The linear mixture model has been extensively used in the analysis of satellite data, especially for characterization of surface cover at subpixel scales. In the model, a multispectral signal is assumed to comprise a weighted sum of characteristic spectra, the weights corresponding to fractional coverage. The residual is almost invariably assumed to be independent of the weights, and usually taken to arise from Gaussian noise; the maximum-likelihood estimate of the abundances is then found by minimizing a quadratic objective function. Nonuniform sampling of the radiance distribution within the field of view means, however, that there is some dependence of the residual on the true surface abundances, when it is understood that these are simple area averages. We account for this signal-dependent noise by incorporating the modified variance-covariance matrix of the residual into the quadratic objective function. It is shown that, despite the increased complexity of the new objective function, it is minimized by the traditional estimator. This is true whether or not we require the estimated abundances to sum to unity, i.e., whether the minimization is constrained or unconstrained. The constrained and unconstrained estimators are here treated together by deploying a "sum-to-one" parameter.

Vector FIR-median hybrid filters for multispectral signals

The concept of a vector median (VM) using linear substructures is introduced for multispectral signals. It is shown that the VM filter utilises the correlation between the spectral components, which is an important property when the edge shape and location should be preserved for each signal component simultaneously. The vector median approach is applied to FIR-median hybrid (FMH) filters. These filters are shown to have good noise attenuation and a low bias error near edges. Vector median filters can be applied, for example, to standard colour images in TV systems and to multispectral satellite images.