Spectral clustering independent component analysis for tissue classification from brain MRI

A spectral angle based feature extraction method, Spectral Clustering Independent Component Analysis (SC-ICA), is proposed in this work to improve the brain tissue classification from Magnetic Resonance Images (MRI). SC-ICA provides equal priority to global and local features; thereby it tries to resolve the inefficiency of conventional approaches in abnormal tissue extraction. First, input multispectral MRI is divided into different clusters by a spectral distance based clustering. Then, Independent Component Analysis (ICA) is applied on the clustered data, in conjunction with Support Vector Machines (SVM) for brain tissue analysis. Normal and abnormal datasets, consisting of real and synthetic T1-weighted, T2-weighted and proton density/fluid-attenuated inversion recovery images, were used to evaluate the performance of the new method. Comparative analysis with ICA based SVM and other conventional classifiers established the stability and efficiency of SC-ICA based classification, especially in reproduction of small abnormalities. Clinical abnormal case analysis demonstrated it through the highest Tanimoto Index/accuracy values, 0.75/98.8%, observed against ICA based SVM results, 0.17/96.1%, for reproduced lesions. Experimental results recommend the proposed method as a promising approach in clinical and pathological studies of brain diseases

Estudo da influência da coluna d’água na resposta espectral da vegetação aquática submersa em condições controladas

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Signal classification of submerged aquatic vegetation based on the hemispherical-conical reflectance factor spectrum shape in the yellow and red regions

The water column overlying the submerged aquatic vegetation (SAV) canopy presents difficulties when using remote sensing images for mapping such vegetation. Inherent and apparent water optical properties and its optically active components, which are commonly present in natural waters, in addition to the water column height over the canopy, and plant characteristics are some of the factors that affect the signal from SAV mainly due to its strong energy absorption in the near-infrared. By considering these interferences, a hypothesis was developed that the vegetation signal is better conserved and less absorbed by the water column in certain intervals of the visible region of the spectrum; as a consequence, it is possible to distinguish the SAV signal. To distinguish the signal from SAV, two types of classification approaches were selected. Both of these methods consider the hemispherical-conical reflectance factor (HCRF) spectrum shape, although one type was supervised and the other one was not. The first method adopts cluster analysis and uses the parameters of the band (absorption, asymmetry, height and width) obtained by continuum removal as the input of the classification. The spectral angle mapper (SAM) was adopted as the supervised classification approach. Both approaches tested different wavelength intervals in the visible and near-infrared spectra. It was demonstrated that the 585 to 685-nm interval, corresponding to the green, yellow and red wavelength bands, offered the best results in both classification approaches. However, SAM classification showed better results relative to cluster analysis and correctly separated all spectral curves with or without SAV. Based on this research, it can be concluded that it is possible to discriminate areas with and without SAV using remote sensing. © 2013 by the authors; licensee MDPI, Basel, Switzerland.

Potencialidade do sensor Worldview II na detecção de hematita e goethita em solos tropicais

Dissertação (mestrado)—Universidade de Brasília, Instituto de Geociências, Programa de Pós-Graduação Stricto Sensu em Geociências Aplicadas, 2016.

Comparison of multitemporal MODIS-EVI smoothing algorithms and its contribution to crop monitoring.

Crop monitoring and more generally land use change detection are of primary importance in order to analyze spatio-temporal dynamics and its impacts on environment. This aspect is especially true in such a region as the State of Mato Grosso (south of the Brazilian Amazon Basin) which hosts an intensive pioneer front. Deforestation in this region as often been explained by soybean expansion in the last three decades. Remote sensing techniques may now represent an efficient and objective manner to quantify how crops expansion really represents a factor of deforestation through crop mapping studies. Due to the special characteristics of the soybean productions' farms in Mato Grosso (area varying between 1000 hectares and 40000 hectares and individual fields often bigger than 100 hectares), the Moderate Resolution Imaging Spectroradiometer (MODIS) data with a near daily temporal resolution and 250 m spatial resolution can be considered as adequate resources to crop mapping. Especially, multitemporal vegetation indices (VI) studies have been currently used to realize this task [1] [2]. In this study, 16-days compositions of EVI (MODQ13 product) data are used. However, although these data are already processed, multitemporal VI profiles still remain noisy due to cloudiness (which is extremely frequent in a tropical region such as south Amazon Basin), sensor problems, errors in atmospheric corrections or BRDF effect. Thus, many works tried to develop algorithms that could smooth the multitemporal VI profiles in order to improve further classification. The goal of this study is to compare and test different smoothing algorithms in order to select the one which satisfies better to the demand which is classifying crop classes. Those classes correspond to 6 different agricultural managements observed in Mato Grosso through an intensive field work which resulted in mapping more than 1000 individual fields. The agricultural managements above mentioned are based on combination of soy, cotton, corn, millet and sorghum crops sowed in single or double crop systems. Due to the difficulty in separating certain classes because of too similar agricultural calendars, the classification will be reduced to 3 classes : Cotton (single crop), Soy and cotton (double crop), soy (single or double crop with corn, millet or sorghum). The classification will use training data obtained in the 2005-2006 harvest and then be tested on the 2006-2007 harvest. In a first step, four smoothing techniques are presented and criticized. Those techniques are Best Index Slope Extraction (BISE) [3], Mean Value Iteration (MVI) [4], Weighted Least Squares (WLS) [5] and Savitzky-Golay Filter (SG) [6] [7]. These techniques are then implemented and visually compared on a few individual pixels so that it allows doing a first selection between the five studied techniques. The WLS and SG techniques are selected according to criteria proposed by [8]. Those criteria are: ability in eliminating frequent noises, conserving the upper values of the VI profiles and keeping the temporality of the profiles. Those selected algorithms are then programmed and applied to the MODIS/TERRA EVI data (16-days composition periods). Tests of separability are realized based on the Jeffries-Matusita distance in order to see if the algorithms managed in improving the potential of differentiation between the classes. Those tests are realized on the overall profile (comprising 23 MODIS images) as well as on each MODIS sub-period of the profile [1]. This last test is a double interest process because it allows comparing the smoothing techniques and also enables to select a set of images which carries more information on the separability between the classes. Those selected dates can then be used to realize a supervised classification. Here three different classifiers are tested to evaluate if the smoothing techniques as a particular effect on the classification depending on the classifiers used. Those classifiers are Maximum Likelihood classifier, Spectral Angle Mapper (SAM) classifier and CHAID Improved Decision tree. It appears through the separability tests on the overall process that the smoothed profiles don't improve efficiently the potential of discrimination between classes when compared with the original data. However, the same tests realized on the MODIS sub-periods show better results obtained with the smoothed algorithms. The results of the classification confirm this first analyze. The Kappa coefficients are always better with the smoothing techniques and the results obtained with the WLS and SG smoothed profiles are nearly equal. However, the results are different depending on the classifier used. The impact of the smoothing algorithms is much better while using the decision tree model. Indeed, it allows a gain of 0.1 in the Kappa coefficient. While using the Maximum Likelihood end SAM models, the gain remains positive but is much lower (Kappa improved of 0.02 only). Thus, this work's aim is to prove the utility in smoothing the VI profiles in order to improve the final results. However, the choice of the smoothing algorithm has to be made considering the original data used and the classifier models used. In that case the Savitzky-Golay filter gave the better results.

The distribution and ages of regional lithologies in the lunar maria

A research program was designed (1) to map regional lithological units of the lunar surface based on measurements of spatial variations in spectral reflectance, and, (2) to establish the sequence of the formation of such lithological units from measurements of the accumulated affects of impacting bodies.

Spectral reflectance data were obtained by scanning luminance variations over the lunar surface at three wavelengths (0.4µ, 0.52µ, and 0.7µ). These luminance measurements were reduced to normalized spectral reflectance values relative to a standard area in More Serenitotis. The spectral type of each lunar area was identified from the shape of its reflectance spectrum. From these data lithological units or regions of constant color were identified. The maria fall into two major spectral classes: circular moria like More Serenitotis contain S-type or red material and thin, irregular, expansive maria like Mare Tranquillitatis contain T-type or blue material. Four distinct subtypes of S-type reflectances and two of T-type reflectances exist. As these six subtypes occur in a number of lunar regions, it is concluded that they represent specific types of material rather than some homologous set of a few end members.

The relative ages or sequence of formation of these more units were established from measurements of the accumulated impacts which have occurred since more formation. A model was developed which relates the integrated flux of particles which hove impacted a surface to the distribution of craters as functions of size and shape. Erosion of craters is caused chiefly by small bodies which produce negligible individual changes in crater shape. Hence the shape of a crater can be used to estimate the total number of small impacts that have occurred since the crater was formed. Relative ages of a surface can then be obtained from measurements of the slopes of the walls of the oldest craters formed on the surface. The results show that different maria and regions within them were emplaced at different times. An approximate absolute time scale was derived from Apollo 11 crystallization ages under an assumption of a constant rote of impacting for the last 4 x 10^9 yrs. Assuming, constant flux, the period of mare formation lasted from over 4 x 10^9 yrs to about 1.5 x 10^9 yrs ago.

A synthesis of the results of relative age measurements and of spectral reflectance mapping shows that (1) the formation of the lunar maria occurred in three stages; material of only one spectral type was deposited in each stage, (2) two distinct kinds of maria exist, each type distinguished by morphology, structure, gravity anomalies, time of formation, and spectral reflectance type, and (3) individual maria have complicated histories; they contain a variety of lithic units emplaced at different times.

Uso do sensoriamento remoto orbital no monitoramento da dispersão de macrófitas nos reservatórios do complexo Tietê

A presença de algumas substâncias na água, como pigmentos fotossintetizantes, particulados, etc., afeta sua cor, provocando mudanças na radiância da água registrada por sensores orbitais. Nesse sentido, o sensoriamento remoto pode se constituir em uma fonte complementar de dados para o monitoramento da qualidade da água em grandes reservatórios (Novo et al., 1994). No contexto de um projeto de pesquisa realizado na AES Tietê S.A., com o objetivo de desenvolver técnicas para a avaliação da área com infestação de plantas aquáticas, imagens orbitais multiespectrais foram usadas tanto para mapear a dispersão espacial e estimar a área de ocorrência de macrófitas aquáticas, em duas épocas distintas, quanto para orientar a definição de pontos de amostragem in loco, visando a coleta e posterior análise da água e de sedimentos nos reservatórios. Este trabalho apresenta uma descrição do procedimento metodológico adotado na análise das imagens multiespectrais, bem como os resultados obtidos na caracterização dos reservatórios de Barra Bonita, Bariri, Ibitinga, Promissão e Nova Avanhandava, em termos de seu dimensionamento, variabilidade espectral da água e presença de macrófitas emersas, nas duas épocas do ano consideradas.

Examination of the quality of spinach leaves using hyperspectral imaging

The present research is focused on the application of hyperspectral images for the supervision of quality deterioration in ready to use leafy spinach during storage (Spinacia oleracea). Two sets of samples of packed leafy spinach were considered: (a) a first set of samples was stored at 20 °C (E-20) in order to accelerate the degradation process, and these samples were measured the day of reception in the laboratory and after 2 days of storage; (b) a second set of samples was kept at 10 °C (E-10), and the measurements were taken throughout storage, beginning the day of reception and repeating the acquisition of Images 3, 6 and 9 days later. Twenty leaves per test were analyzed. Hyperspectral images were acquired with a push-broom CCD camera equipped with a spectrograph VNIR (400–1000 nm). Calibration set of spectra was extracted from E-20 samples, containing three classes of degradation: class A (optimal quality), class B and class C (maximum deterioration). Reference average spectra were defined for each class. Three models, computed on the calibration set, with a decreasing degree of complexity were compared, according to their ability for segregating leaves at different quality stages (fresh, with incipient and non-visible symptoms of degradation, and degraded): spectral angle mapper distance (SAM), partial least squares discriminant analysis models (PLS-DA), and a non linear index (Leafy Vegetable Evolution, LEVE) combining five wavelengths were included among the previously selected by CovSel procedure. In sets E-10 and E-20, artificial images of the membership degree according to the distance of each pixel to the reference classes, were computed assigning each pixel to the closest reference class. The three methods were able to show the degradation of the leaves with storage time.

Aplicación de la imagen hiperespectral para la detección de contaminación microbiana en espinaca fresca envasada.

La seguridad alimentaria de hortalizas de hoja puede verse comprometida por contaminaciones microbianas, lo que ha motivado la promoción mundial de normas de seguridad en la producción, el manejo poscosecha y el procesado. Sin embargo, en los últimos años las hortalizas han estado implicadas en brotes de Escherichia coli, Salmonella spp y Listeria monocytogenes, lo que obliga a plantear investigaciones que posibiliten la detección de contaminación por microorganismos con técnicas rápidas, no destructivas y precisas. La imagen hiperespectral integra espectroscopia e imagen para proporcionar información espectral y espacial de la distribución de los componentes químicos de una muestra. Los objetivos de este estudio fueron optimizar un sistema de visión hiperespectral y establecer los procedimientos de análisis multivariante de imágenes para la detección temprana de contaminación microbiana en espinacas envasadas (Spinacia oleracea). Las muestras de espinacas fueron adquiridas en un mercado local. Se sometieron a la inoculación mediante la inmersión en suspensiones con poblaciones iniciales de 5 ó 7 unidades logarítmicas de ufc de una cepa no patógena de Listeria innocua. Después de 15 minutos de inoculación por inmersión, las hojas fueron envasadas asépticamente. Se consideró un tratamiento Control-1 no inoculado, sumergiendo las espinacas en una solución tampón (agua de peptona) en lugar de en solución inoculante, y un tratamiento Control-2 en el que las hojas no se sometieron a ninguna inmersión. Las muestras se almacenaron a 8ºC y las mediciones se realizaron 0, 1, 3, 6 y 9 días después de inocular. Cada día se determinaron los recuentos microbianos y se adquirieron las imágenes con una cámara VIS-NIR (400-1000 nm). Sobre los gráficos de dispersión de los scores de PC1 y PC2 (análisis de componentes principales computados sobre las imágenes hiperespectrales considerando el rango de 500-940 nm) se reconocieron diferentes patrones que se identificaron con niveles de degradación; así se seleccionaron píxeles que conformaron las clases de no degradados, semi-degradados y degradados. Se consideraron los espectros promedio de cada clase para asignar los píxeles anónimos a una de las tres clases. Se calculó la distancia SAM (Spectral Angle Mapper) entre el espectro promedio de cada categoría y cada espectro anónimo de las imágenes. Cada píxel se asignó a la clase a la que se calcula la distancia mínima. En general, las imágenes con los porcentajes más altos de píxeles levemente degradados y degradados correspondieron a contenidos microbiológicos superiores a 5 unidades logarítmicas de ufc.

Linear Hyperspectral Unmixing Using L0-norm Approximations and Nonnegative Matrix Factorization

Spectral unmixing (SU) is a technique to characterize mixed pixels of the hyperspectral images measured by remote sensors. Most of the existing spectral unmixing algorithms are developed using the linear mixing models. Since the number of endmembers/materials present at each mixed pixel is normally scanty compared with the number of total endmembers (the dimension of spectral library), the problem becomes sparse. This thesis introduces sparse hyperspectral unmixing methods for the linear mixing model through two different scenarios. In the first scenario, the library of spectral signatures is assumed to be known and the main problem is to find the minimum number of endmembers under a reasonable small approximation error. Mathematically, the corresponding problem is called the $\ell_0$-norm problem which is NP-hard problem. Our main study for the first part of thesis is to find more accurate and reliable approximations of $\ell_0$-norm term and propose sparse unmixing methods via such approximations. The resulting methods are shown considerable improvements to reconstruct the fractional abundances of endmembers in comparison with state-of-the-art methods such as having lower reconstruction errors. In the second part of the thesis, the first scenario (i.e., dictionary-aided semiblind unmixing scheme) will be generalized as the blind unmixing scenario that the library of spectral signatures is also estimated. We apply the nonnegative matrix factorization (NMF) method for proposing new unmixing methods due to its noticeable supports such as considering the nonnegativity constraints of two decomposed matrices. Furthermore, we introduce new cost functions through some statistical and physical features of spectral signatures of materials (SSoM) and hyperspectral pixels such as the collaborative property of hyperspectral pixels and the mathematical representation of the concentrated energy of SSoM for the first few subbands. Finally, we introduce sparse unmixing methods for the blind scenario and evaluate the efficiency of the proposed methods via simulations over synthetic and real hyperspectral data sets. The results illustrate considerable enhancements to estimate the spectral library of materials and their fractional abundances such as smaller values of spectral angle distance (SAD) and abundance angle distance (AAD) as well.

Caractérisation de minéraux indicateurs par imagerie hyperspectrale et traitement de l'image dans l'infrarouge proche et l'infrarouge lointain

L’imagerie hyperspectrale (HSI) fournit de l’information spatiale et spectrale concernant l’émissivité de la surface des matériaux, ce qui peut être utilisée pour l’identification des minéraux. Pour cela, un matériel de référence ou endmember, qui en minéralogie est la forme la plus pure d’un minéral, est nécessaire. L’objectif principal de ce projet est l’identification des minéraux par imagerie hyperspectrale. Les informations de l’imagerie hyperspectrale ont été enregistrées à partir de l’énergie réfléchie de la surface du minéral. L’énergie solaire est la source d’énergie dans l’imagerie hyperspectrale de télédétection, alors qu’un élément chauffant est la source d’énergie utilisée dans les expériences de laboratoire. Dans la première étape de ce travail, les signatures spectrales des minéraux purs sont obtenues avec la caméra hyperspectrale, qui mesure le rayonnement réfléchi par la surface des minéraux. Dans ce projet, deux séries d’expériences ont été menées dans différentes plages de longueurs d’onde (0,4 à 1 µm et 7,7 à 11,8 µm). Dans la deuxième partie de ce projet, les signatures spectrales obtenues des échantillons individuels sont comparées avec des signatures spectrales de la bibliothèque hyperspectrale de l’ASTER. Dans la troisième partie, trois méthodes différentes de classification hyperspectrale sont considérées pour la classification. Spectral Angle Mapper (SAM), Spectral Information Divergence (SID), et Intercorrélation normalisée (NCC). Enfin, un système d’apprentissage automatique, Extreme Learning Machine (ELM), est utilisé pour identifier les minéraux. Deux types d’échantillons ont été utilisés dans ce projet. Le système d’ELM est divisé en deux parties, la phase d’entraînement et la phase de test du système. Dans la phase d’entraînement, la signature d’un seul échantillon minéral est entrée dans le système, et dans la phase du test, les signatures spectrales des différents minéraux, qui sont entrées dans la phase d’entraînement, sont comparées par rapport à des échantillons de minéraux mixtes afin de les identifier.

Detecting outliers and asserting consistency in agriculture ground truth information by using temporal VI data from modis.

Collecting ground truth data is an important step to be accomplished before performing a supervised classification. However, its quality depends on human, financial and time ressources. It is then important to apply a validation process to assess the reliability of the acquired data. In this study, agricultural infomation was collected in the Brazilian Amazonian State of Mato Grosso in order to map crop expansion based on MODIS EVI temporal profiles. The field work was carried out through interviews for the years 2005-2006 and 2006-2007. This work presents a methodology to validate the training data quality and determine the optimal sample to be used according to the classifier employed. The technique is based on the detection of outlier pixels for each class and is carried out by computing Mahalanobis distances for each pixel. The higher the distance, the further the pixel is from the class centre. Preliminary observations through variation coefficent validate the efficiency of the technique to detect outliers. Then, various subsamples are defined by applying different thresholds to exclude outlier pixels from the classification process. The classification results prove the robustness of the Maximum Likelihood and Spectral Angle Mapper classifiers. Indeed, those classifiers were insensitive to outlier exclusion. On the contrary, the decision tree classifier showed better results when deleting 7.5% of pixels in the training data. The technique managed to detect outliers for all classes. In this study, few outliers were present in the training data, so that the classification quality was not deeply affected by the outliers.

Small angle X-ray scattering mapping and kinetics study of sub-critical CO2 sorption by two Australian coals

Time- and position-resolved synchrotron small angle X-ray scattering data were acquired from samples of two Australian coal seams: Bulli seam (Bulli 4, Ro=1.42%, Sydney Basin), which naturally contains CO2 and Baralaba seam (Ro=0.67%, Bowen Basin), a potential candidate for sequestering CO2. This experimental approach has provided unique, pore-size-specific insights into the kinetics of CO2 sorption in the micro- and small mesopores (diameter 5 to 175 Å) and the density of the sorbed CO2 at reservoir-like conditions of temperature and hydrostatic pressure. For both samples, at pressures above 5 bar, the density of CO2 confined in pores was found to be uniform, with no densification in near-wall regions. In the Bulli 4 sample, CO2 first flooded the slit pores between polyaromatic sheets. In the pore-size range analysed, the confined CO2 density was close to that of the free CO2. The kinetics data are too noisy for reliable quantitative analysis, but qualitatively indicate faster kinetics in mineral-matter-rich regions. In the Baralaba sample, CO2 preferentially invaded the smallest micropores and the confined CO2 density was up to five times that of the free CO2. Faster CO2 sorption kinetics was found to be correlated with higher mineral matter content but, the mineral-matter-rich regions had lower-density CO2 confined in their pores. Remarkably, the kinetics was pore-size dependent, being faster for smaller pores. These results suggest that injection into the permeable section of an interbedded coal-clastic sequence could provide a viable combination of reasonable injectivity and high sorption capacity.

Constrained Linear Spectral Unmixing Technique for Regional Land Cover Mapping Using MODIS Data

Over the last few decades, there has been a significant land cover (LC) change across the globe due to the increasing demand of the burgeoning population and urban sprawl. In order to take account of the change, there is a need for accurate and up-to-date LC maps. Mapping and monitoring of LC in India is being carried out at national level using multi-temporal IRS AWiFS data. Multispectral data such as IKONOS, Landsat-TM/ETM+, IRS-ICID LISS-III/IV, AWiFS and SPOT-5, etc. have adequate spatial resolution (similar to 1m to 56m) for LC mapping to generate 1:50,000 maps. However, for developing countries and those with large geographical extent, seasonal LC mapping is prohibitive with data from commercial sensors of limited spatial coverage. Superspectral data from the MODIS sensor are freely available, have better temporal (8 day composites) and spectral information. MODIS pixels typically contain a mixture of various LC types (due to coarse spatial resolution of 250, 500 and 1000 in), especially in more fragmented landscapes. In this context, linear spectral unmixing would be useful for mapping patchy land covers, such as those that characterise much of the Indian subcontinent. This work evaluates the existing unmixing technique for LC mapping using MODIS data, using end-members that are extracted through Pixel Purity Index (PPI), Scatter plot and N-dimensional visualisation. The abundance maps were generated for agriculture, built up, forest, plantations, waste land/others and water bodies. The assessment of the results using ground truth and a LISS-III classified map shows 86% overall accuracy, suggesting the potential for broad-scale applicability of the technique with superspectral data for natural resource planning and inventory applications. Index Terms-Remote sensing, digital

Contrained linear spectral unmixing technique for regional land cover mapping using MODIS data

Over the last few decades, there has been a significant land cover (LC) change across the globe due to the increasing demand of the burgeoning population and urban sprawl. In order to take account of the change, there is a need for accurate and up- to-date LC maps. Mapping and monitoring of LC in India is being carried out at national level using multi-temporal IRS AWiFS data. Multispectral data such as IKONOS, Landsat- TM/ETM+, IRS-1C/D LISS-III/IV, AWiFS and SPOT-5, etc. have adequate spatial resolution (~ 1m to 56m) for LC mapping to generate 1:50,000 maps. However, for developing countries and those with large geographical extent, seasonal LC mapping is prohibitive with data from commercial sensors of limited spatial coverage. Superspectral data from the MODIS sensor are freely available, have better temporal (8 day composites) and spectral information. MODIS pixels typically contain a mixture of various LC types (due to coarse spatial resolution of 250, 500 and 1000 m), especially in more fragmented landscapes. In this context, linear spectral unmixing would be useful for mapping patchy land covers, such as those that characterise much of the Indian subcontinent. This work evaluates the existing unmixing technique for LC mapping using MODIS data, using end- members that are extracted through Pixel Purity Index (PPI), Scatter plot and N-dimensional visualisation. The abundance maps were generated for agriculture, built up, forest, plantations, waste land/others and water bodies. The assessment of the results using ground truth and a LISS-III classified map shows 86% overall accuracy, suggesting the potential for broad-scale applicability of the technique with superspectral data for natural resource planning and inventory applications.