Evaluating effects of spectral training data distribution on continuous field mapping performance

Continuous field mapping has to address two conflicting remote sensing requirements when collecting training data. On one hand, continuous field mapping trains fractional land cover and thus favours mixed training pixels. On the other hand, the spectral signature has to be preferably distinct and thus favours pure training pixels. The aim of this study was to evaluate the sensitivity of training data distribution along fractional and spectral gradients on the resulting mapping performance. We derived four continuous fields (tree, shrubherb, bare, water) from aerial photographs as response variables and processed corresponding spectral signatures from multitemporal Landsat 5 TM data as explanatory variables. Subsequent controlled experiments along fractional cover gradients were then based on generalised linear models. Resulting fractional and spectral distribution differed between single continuous fields, but could be satisfactorily trained and mapped. Pixels with fractional or without respective cover were much more critical than pure full cover pixels. Error distribution of continuous field models was non-uniform with respect to horizontal and vertical spatial distribution of target fields. We conclude that a sampling for continuous field training data should be based on extent and densities in the fractional and spectral, rather than the real spatial space. Consequently, adequate training plots are most probably not systematically distributed in the real spatial space, but cover the gradient and covariate structure of the fractional and spectral space well. (C) 2009 International Society for Photogrammetry and Remote Sensing, Inc. (ISPRS). Published by Elsevier B.V. All rights reserved.

A Fast Gradient Approximation for Nonlinear Blind Signal Processing

When dealing with nonlinear blind processing algorithms (deconvolution or post-nonlinear source separation), complex mathematical estimations must be done giving as a result very slow algorithms. This is the case, for example, in speech processing, spike signals deconvolution or microarray data analysis. In this paper, we propose a simple method to reduce computational time for the inversion of Wiener systems or the separation of post-nonlinear mixtures, by using a linear approximation in a minimum mutual information algorithm. Simulation results demonstrate that linear spline interpolation is fast and accurate, obtaining very good results (similar to those obtained without approximation) while computational time is dramatically decreased. On the other hand, cubic spline interpolation also obtains similar good results, but due to its intrinsic complexity, the global algorithm is much more slow and hence not useful for our purpose.

Ant species distribution along a topographic gradient in a "terra-firme" forest reserve in Central Amazonia

In Amazonia, topographical variations in soil and forest structure within "terra-firme" ecosystems are important factors correlated with terrestrial invertebrates' distribution. The objective of this work was to assess the effects of soil clay content and slope on ant species distribution over a 25 km² grid covering the natural topographic continuum. Using three complementary sampling methods (sardine baits, pitfall traps and litter samples extracted in Winkler sacks), 300 subsamples of each method were taken in 30 plots distributed over a wet tropical forest in the Ducke Reserve (Manaus, AM, Brazil). An amount of 26,814 individuals from 11 subfamilies, 54 genera, 85 species and 152 morphospecies was recorded (Pheidole represented 37% of all morphospecies). The genus Eurhopalothrix was registered for the first time for the reserve. Species number was not correlated with slope or clay content, except for the species sampled from litter. However, the Principal Coordinate Analysis indicated that the main pattern of species composition from pitfall and litter samples was related to clay content. Almost half of the species were found only in valleys or only on plateaus, which suggests that most of them are habitat specialists. In Central Amazonia, soil texture is usually correlated with vegetation structure and moisture content, creating different microhabitats, which probably account for the observed differences in ant community structure.

Estimating soybean crop areas using spectral-temporal surfaces derived from MODIS images in Mato Grosso, Brazil

The objective of this work was to evaluate the application of the spectral-temporal response surface (STRS) classification method on Moderate Resolution Imaging Spectroradiometer (MODIS, 250 m) sensor images in order to estimate soybean areas in Mato Grosso state, Brazil. The classification was carried out using the maximum likelihood algorithm (MLA) adapted to the STRS method. Thirty segments of 30x30 km were chosen along the main agricultural regions of Mato Grosso state, using data from the summer season of 2005/2006 (from October to March), and were mapped based on fieldwork data, TM/Landsat-5 and CCD/CBERS-2 images. Five thematic classes were considered: Soybean, Forest, Cerrado, Pasture and Bare Soil. The classification by the STRS method was done over an area intersected with a subset of 30x30-km segments. In regions with soybean predominance, STRS classification overestimated in 21.31% of the reference values. In regions where soybean fields were less prevalent, the classifier overestimated 132.37% in the acreage of the reference. The overall classification accuracy was 80%. MODIS sensor images and the STRS algorithm showed to be promising for the classification of soybean areas in regions with the predominance of large farms. However, the results for fragmented areas and smaller farms were less efficient, overestimating soybean areas.

Modeling morphogen gradient formation from arbitrary realistically shaped sources.

Much of the analytical modeling of morphogen profiles is based on simplistic scenarios, where the source is abstracted to be point-like and fixed in time, and where only the steady state solution of the morphogen gradient in one dimension is considered. Here we develop a general formalism allowing to model diffusive gradient formation from an arbitrary source. This mathematical framework, based on the Green's function method, applies to various diffusion problems. In this paper, we illustrate our theory with the explicit example of the Bicoid gradient establishment in Drosophila embryos. The gradient formation arises by protein translation from a mRNA distribution followed by morphogen diffusion with linear degradation. We investigate quantitatively the influence of spatial extension and time evolution of the source on the morphogen profile. For different biologically meaningful cases, we obtain explicit analytical expressions for both the steady state and time-dependent 1D problems. We show that extended sources, whether of finite size or normally distributed, give rise to more realistic gradients compared to a single point-source at the origin. Furthermore, the steady state solutions are fully compatible with a decreasing exponential behavior of the profile. We also consider the case of a dynamic source (e.g. bicoid mRNA diffusion) for which a protein profile similar to the ones obtained from static sources can be achieved.

Improved SNR efficiency in gradient echo coronary MRA with high temporal resolution using parallel imaging.

Contemporary coronary magnetic resonance angiography techniques suffer from signal-to-noise ratio (SNR) constraints. We propose a method to enhance SNR in gradient echo coronary magnetic resonance angiography by using sensitivity encoding (SENSE). While the use of sensitivity encoding to improve SNR seems counterintuitive, it can be exploited by reducing the number of radiofrequency excitations during the acquisition window while lowering the signal readout bandwidth, therefore improving the radiofrequency receive to radiofrequency transmit duty cycle. Under certain conditions, this leads to improved SNR. The use of sensitivity encoding for improved SNR in three-dimensional coronary magnetic resonance angiography is investigated using numerical simulations and an in vitro and an in vivo study. A maximum 55% SNR enhancement for coronary magnetic resonance angiography was found both in vitro and in vivo, which is well consistent with the numerical simulations. This method is most suitable for spoiled gradient echo coronary magnetic resonance angiography in which a high temporal and spatial resolution is required.

Mixed spectral-structural classification of very high resolution images with summation kernels

In this paper, mixed spectral-structural kernel machines are proposed for the classification of very-high resolution images. The simultaneous use of multispectral and structural features (computed using morphological filters) allows a significant increase in classification accuracy of remote sensing images. Subsequently, weighted summation kernel support vector machines are proposed and applied in order to take into account the multiscale nature of the scene considered. Such classifiers use the Mercer property of kernel matrices to compute a new kernel matrix accounting simultaneously for two scale parameters. Tests on a Zurich QuickBird image show the relevance of the proposed method : using the mixed spectral-structural features, the classification accuracy increases of about 5%, achieving a Kappa index of 0.97. The multikernel approach proposed provide an overall accuracy of 98.90% with related Kappa index of 0.985.

Highlight Removal in Spectral Images

Monissasovelluksissa on hyvin tärkeää vähentää valolähteen vaikutusta kohteen oikean värin havainnoimiseksi. Tämä on tarpeen mm. virtuaalisissa museoissa, telelääketieteessä, verkkokaupassa ja verkkorahassa. Tässä tutkielmassa on kehitetty tekniikkaa kirkkaiden heijastusten poistoon spektrikuvista. Työ sisältää katsauksen yleisen värillisen kuvan ymmärtämiseen, mihin perustuen analysoitiin erilaisia kirkkaiden heijastusten poistO'tekniikoita. Työssä kehitettiin uusi kirkkaiden heijastusten poistO'menetelmä, joka perustuu dikromaattiseen heijastus-malliin, joka kuvaa spektrisen datan objektin omaan väriin ja valaisevan valon väriin perustuen. Ehdotettu kirkkaiden heijastusten poistO'menetelmä hyödyntää erilaisia olemassaolevia menetelmiä, kuten pääkomponenttimenetelmää ja tiedon luokittelu-menetelmää. Yritys kehittää nopeasti toimiva algoritmi, joka myös suoriutuu tehtävästä hyvin, on onnistunut. Kokeet toteutettiin ehdotetun menetelmän mukaisesti ja toimivalla algoritmilla saatiin halutut lopputulokset. Edelleentyö sisältää ehdotuksia esitetyn algoritmin parantamiseksi.

Self-Organising Maps and Correlation Analysis as a Tool to Explore Patterns in Excitation-Emission Matrix Data Sets and to Discriminate Dissolved Organic Matter Fluorescence Components

Dissolved organic matter (DOM) is a complex mixture of organic compounds, ubiquitous in marine and freshwater systems. Fluorescence spectroscopy, by means of Excitation-Emission Matrices (EEM), has become an indispensable tool to study DOM sources, transport and fate in aquatic ecosystems. However the statistical treatment of large and heterogeneous EEM data sets still represents an important challenge for biogeochemists. Recently, Self-Organising Maps (SOM) has been proposed as a tool to explore patterns in large EEM data sets. SOM is a pattern recognition method which clusterizes and reduces the dimensionality of input EEMs without relying on any assumption about the data structure. In this paper, we show how SOM, coupled with a correlation analysis of the component planes, can be used both to explore patterns among samples, as well as to identify individual fluorescence components. We analysed a large and heterogeneous EEM data set, including samples from a river catchment collected under a range of hydrological conditions, along a 60-km downstream gradient, and under the influence of different degrees of anthropogenic impact. According to our results, chemical industry effluents appeared to have unique and distinctive spectral characteristics. On the other hand, river samples collected under flash flood conditions showed homogeneous EEM shapes. The correlation analysis of the component planes suggested the presence of four fluorescence components, consistent with DOM components previously described in the literature. A remarkable strength of this methodology was that outlier samples appeared naturally integrated in the analysis. We conclude that SOM coupled with a correlation analysis procedure is a promising tool for studying large and heterogeneous EEM data sets.

Use of the heat dissipation method for sap flow measurement in citrus nursery trees1

Sap flow could be used as physiological parameter to assist irrigation of screen house citrus nursery trees by continuous water consumption estimation. Herein we report a first set of results indicating the potential use of the heat dissipation method for sap flow measurement in containerized citrus nursery trees. 'Valencia' sweet orange [Citrus sinensis (L.) Osbeck] budded on 'Rangpur' lime (Citrus limonia Osbeck) was evaluated for 30 days during summer. Heat dissipation probes and thermocouple sensors were constructed with low-cost and easily available materials in order to improve accessibility of the method. Sap flow showed high correlation to air temperature inside the screen house. However, errors due to natural thermal gradient and plant tissue injuries affected measurement precision. Transpiration estimated by sap flow measurement was four times higher than gravimetric measurement. Improved micro-probes, adequate method calibration, and non-toxic insulating materials should be further investigated.

Self-Organising Maps and Correlation Analysis as a Tool to Explore Patterns in Excitation-Emission Matrix Data Sets and to Discriminate Dissolved Organic Matter Fluorescence Components

Dissolved organic matter (DOM) is a complex mixture of organic compounds, ubiquitous in marine and freshwater systems. Fluorescence spectroscopy, by means of Excitation-Emission Matrices (EEM), has become an indispensable tool to study DOM sources, transport and fate in aquatic ecosystems. However the statistical treatment of large and heterogeneous EEM data sets still represents an important challenge for biogeochemists. Recently, Self-Organising Maps (SOM) has been proposed as a tool to explore patterns in large EEM data sets. SOM is a pattern recognition method which clusterizes and reduces the dimensionality of input EEMs without relying on any assumption about the data structure. In this paper, we show how SOM, coupled with a correlation analysis of the component planes, can be used both to explore patterns among samples, as well as to identify individual fluorescence components. We analysed a large and heterogeneous EEM data set, including samples from a river catchment collected under a range of hydrological conditions, along a 60-km downstream gradient, and under the influence of different degrees of anthropogenic impact. According to our results, chemical industry effluents appeared to have unique and distinctive spectral characteristics. On the other hand, river samples collected under flash flood conditions showed homogeneous EEM shapes. The correlation analysis of the component planes suggested the presence of four fluorescence components, consistent with DOM components previously described in the literature. A remarkable strength of this methodology was that outlier samples appeared naturally integrated in the analysis. We conclude that SOM coupled with a correlation analysis procedure is a promising tool for studying large and heterogeneous EEM data sets.

Combining T2-preparation with spectral and spatial selectivity for improved coronary MRA

Cardiovascular disease is the leading cause of death worldwide. Within this subset, coronary artery disease (CAD) is the most prevalent. Magnetic resonance angiography (MRA) is an emerging technique that provides a safe, non-invasive way of assessing CAD progression. To generate contrast between tissues, MR images are weighted according to the magnetic properties of those tissues. In cardiac MRI, T2 contrast, which is governed by the rate of transverse signal loss, is often created through the use of a T2-Preparation module. T2-Preparation, or T2-Prep, is a magnetization preparation scheme used to improve blood/myocardium contrast in cardiac MRI. T2-Prep methods generally use a non-selective +90°, 180°, 180°, -90° train of radiofrequency (RF) pulses (or variant thereof), to tip magnetization into the transverse plane, allow it to evolve, and then to restore it to the longitudinal plane. A key feature in this process is the combination of a +90° and -90° RF pulse. By changing either one of these, a mismatch occurs between signal excitation and restoration. This feature can be exploited to provide additional spectral or spatial selectivity. In this work, both of these possibilities are explored. The first - spectral selectivity - has been examined as a method of improving fat saturation in coronary MRA. The second - spatial selectivity - has been examined as a means of reducing imaging time by decreasing the field of view, and as a method of reducing artefacts originating from the tissues surrounding the heart. Two additional applications, parallel imaging and self-navigation, are also presented. This thesis is thus composed of four sections. The first, "A Fat Signal Suppression for Coronary MRA at 3T using a Water-Selective Adiabatic T2-Preparation Technique", was originally published in the journal Magnetic Resonance in Medicine (MRM) with co-authors Ruud B. van Heeswijk and Matthias Stuber. The second, "Combined T2-Preparation and 2D Pencil Beam Inner Volume Selection", again with co-authors Ruud van Heeswijk and Matthias Stuber, was also published in the journal MRM. The third, "A cylindrical, inner volume selecting 2D-T2-Prep improves GRAPPA-accelerated image quality in MRA of the right coronary artery", written with co-authors Jerome Yerly and Matthias Stuber, has been submitted to the "Journal of Cardiovascular Magnetic Resonance", and the fourth, "Combined respiratory self-navigation and 'pencil-beam' 2D-T2 -Prep for free-breathing, whole-heart coronary MRA", with co¬authors Jerome Chaptinel, Giulia Ginami, Gabriele Bonanno, Simone Coppo, Ruud van Heeswijk, Davide Piccini, and Matthias Stuber, is undergoing internal review prior to submission to the journal MRM. -- Les maladies cardiovasculaires sont la cause principale de décès dans le monde : parmi celles-ci, les maladies coronariennes sont les plus répandues. L'angiographie par résonance magnétique (ARM) est une technique émergente qui fournit une manière sûre, non invasive d'évaluer la progression de la coronaropathie. Pour obtenir un contraste entre les tissus, les images d'IRM sont pondérées en fonction des propriétés magnétiques de ces tissus. En IRM cardiaque, le contraste en T2, qui est lié à la décroissance du signal transversal, est souvent créé grâce à l'utilisàtion d'un module de préparation T2. La préparation T2, ou T2-Prep, est un système de préparation de l'aimantation qui est utilisé pour améliorer le contraste entre le sang et le myocarde lors d'une IRM cardiaque. Les méthodes de T2-Prep utilisent généralement une série non-sélective d'impulsions de radiofréquence (RF), typiquement [+ 90°, 180°, 180°, -90°] ou une variante, qui bascule l'aimantation dans le plan transversal, lui permet d'évoluer, puis la restaure dans le plan longitudinal. Un élément clé de ce processus est la combinaison des impulsions RF de +90° et -90°. En changeant l'une ou l'autre des impulsions, un décalage se produit entre l'excitation du signal et de la restauration. Cette fonction peut être exploitée pour fournir une sélectivité spectrale ou spatiale. Dans cette thèse, les deux possibilités sont explorées. La première - la sélectivité spectrale - a été examinée comme une méthode d'améliorer la saturation de la graisse dans l'IRM coronarienne. La deuxième - la sélectivité spatiale - a été étudiée comme un moyen de réduire le temps d'imagerie en diminuant le champ de vue, et comme une méthode de réduction des artefacts provenant des tissus entourant le coeur. Deux applications supplémentaires, l'imagerie parallèle et la self-navigation, sont également présentées. Cette thèse est ainsi composée de quatre sections. La première, "A Fat Signal Suppression for Coronary MRA at 3T using a Water-Selective Adiabatic T2-Preparation Technique", a été publiée dans la revue médicale Magnetic Resonance .in Medicine (MRM) avec les co-auteurs Ruud B. van Heeswijk et Matthias Stuber. La deuxième, Combined T2-Preparation and 2D Pencil Beam Inner Volume Selection", encore une fois avec les co-auteurs Ruud van Heeswijk et Matthias Stuber, a également été publiée dans le journal MRM. La troisième, "A cylindrical, inner volume selecting 2D-T2-Prep improves GRAPPA- accelerated image quality in MRA of the right coronary artery", écrite avec les co-auteurs Jérôme Yerly et Matthias Stuber, a été présentée au "Journal of Cardiovascular Magnetic Resonance", et la quatrième, "Combined respiratory self-navigation and 'pencil-beam' 2D-T2 -Prep for free-breathing, whole-heart coronary MRA", avec les co-auteurs Jérôme Chaptinel, Giulia Ginami, Gabriele Bonanno , Simone Coppo, Ruud van Heeswijk, Davide Piccini, et Matthias Stuber, subit un examen interne avant la soumission à la revue MRM.

Reconstruction and analysis of surface variation using photometric stereo

In many industrial applications, accurate and fast surface reconstruction is essential for quality control. Variation in surface finishing parameters, such as surface roughness, can reflect defects in a manufacturing process, non-optimal product operational efficiency, and reduced life expectancy of the product. This thesis considers reconstruction and analysis of high-frequency variation, that is roughness, on planar surfaces. Standard roughness measures in industry are calculated from surface topography. A fast and non-contact method to obtain surface topography is to apply photometric stereo in the estimation of surface gradients and to reconstruct the surface by integrating the gradient fields. Alternatively, visual methods, such as statistical measures, fractal dimension and distance transforms, can be used to characterize surface roughness directly from gray-scale images. In this thesis, the accuracy of distance transforms, statistical measures, and fractal dimension are evaluated in the estimation of surface roughness from gray-scale images and topographies. The results are contrasted to standard industry roughness measures. In distance transforms, the key idea is that distance values calculated along a highly varying surface are greater than distances calculated along a smoother surface. Statistical measures and fractal dimension are common surface roughness measures. In the experiments, skewness and variance of brightness distribution, fractal dimension, and distance transforms exhibited strong linear correlations to standard industry roughness measures. One of the key strengths of photometric stereo method is the acquisition of higher frequency variation of surfaces. In this thesis, the reconstruction of planar high-frequency varying surfaces is studied in the presence of imaging noise and blur. Two Wiener filterbased methods are proposed of which one is optimal in the sense of surface power spectral density given the spectral properties of the imaging noise and blur. Experiments show that the proposed methods preserve the inherent high-frequency variation in the reconstructed surfaces, whereas traditional reconstruction methods typically handle incorrect measurements by smoothing, which dampens the high-frequency variation.

Computational Techniques for Spectral Image Analysis

Multispectral images are becoming more common in the field of remote sensing, computer vision, and industrial applications. Due to the high accuracy of the multispectral information, it can be used as an important quality factor in the inspection of industrial products. Recently, the development on multispectral imaging systems and the computational analysis on the multispectral images have been the focus of a growing interest. In this thesis, three areas of multispectral image analysis are considered. First, a method for analyzing multispectral textured images was developed. The method is based on a spectral cooccurrence matrix, which contains information of the joint distribution of spectral classes in a spectral domain. Next, a procedure for estimating the illumination spectrum of the color images was developed. Proposed method can be used, for example, in color constancy, color correction, and in the content based search from color image databases. Finally, color filters for the optical pattern recognition were designed, and a prototype of a spectral vision system was constructed. The spectral vision system can be used to acquire a low dimensional component image set for the two dimensional spectral image reconstruction. The data obtained by the spectral vision system is small and therefore convenient for storing and transmitting a spectral image.

A validated method using RP-HPLC for quantification of reserpine in the Brazilian tree Rauvolfia sellowii Müll. Arg. (Apocynaceae)

This study describes a simple, fast and reproducible method using RP-HPLC-UV, in a gradient system, for quantification of reserpine in Rauvolfia sellowii stem bark. The analysis were carried out on a C18 column; mobile phase was water and acetonitrile, and separations were carried out in 10 min, flow rate of 1.0 mL min-1, 25 ºC and 268 nm. The validation data showed that the method was specific, accurate, precise and robust. Results were linear over a range of 0.625-40.0 μg mL-1, and the mean recovery was 95.1%. The amount of reserpine found in the dried stem bark was 0.01% (m/m).