Inertia-Constrained Pixel-by-Pixel Nonnegative Matrix Factorisation: a Hyperspectral Unmixing Method Dealing with Intra-class Variability

Blind source separation is a common processing tool to analyse the constitution of pixels of hyperspectral images. Such methods usually suppose that pure pixel spectra (endmembers) are the same in all the image for each class of materials. In the framework of remote sensing, such an assumption is no more valid in the presence of intra-class variabilities due to illumination conditions, weathering, slight variations of the pure materials, etc... In this paper, we first describe the results of investigations highlighting intra-class variability measured in real images. Considering these results, a new formulation of the linear mixing model is presented leading to two new methods. Unconstrained Pixel-by-pixel NMF (UP-NMF) is a new blind source separation method based on the assumption of a linear mixing model, which can deal with intra-class variability. To overcome UP-NMF limitations an extended method is proposed, named Inertia-constrained Pixel-by-pixel NMF (IP-NMF). For each sensed spectrum, these extended versions of NMF extract a corresponding set of source spectra. A constraint is set to limit the spreading of each source's estimates in IP-NMF. The methods are tested on a semi-synthetic data set built with spectra extracted from a real hyperspectral image and then numerically mixed. We thus demonstrate the interest of our methods for realistic source variabilities. Finally, IP-NMF is tested on a real data set and it is shown to yield better performance than state of the art methods

Blended learning of radiology improves medical students’ performance, satisfaction, and engagement

Purpose To evaluate the impact of blended learning using a combination of educational resources (flipped classroom and short videos) on medical students’ (MSs) for radiology learning. Material and methods A cohort of 353 MSs from 2015 to 2018 was prospectively evaluated. MSs were assigned to four groups (high, high-intermediate, low-intermediate, and low achievers) based on their results to a 20-MCQs performance evaluation referred to as the pretest. MSs had then free access to a self-paced course totalizing 61 videos based on abdominal imaging over a period of 3 months. Performance was evaluated using the change between posttest (the same 20 MCQs as pretest) and pretest results. Satisfaction was measured using a satisfaction survey with directed and spontaneous feedbacks. Engagement was graded according to audience retention and attendance on a web content management system. Results Performance change between pre and posttest was significantly different between the four categories (ANOVA, P = 10−9): low pretest achievers demonstrated the highest improvement (mean ± SD, + 11.3 ± 22.8 points) while high pretest achievers showed a decrease in their posttest score (mean ± SD, − 3.6 ± 19 points). Directed feedback collected from 73.3% of participants showed a 99% of overall satisfaction. Spontaneous feedback showed that the concept of “pleasure in learning” was the most cited advantage, followed by “flexibility.” Engagement increased over years and the number of views increased of 2.47-fold in 2 years. Conclusion Learning formats including new pedagogical concepts as blended learning, and current technologies allow improvement in medical student’s performance, satisfaction, and engagement

A manually annotated Actinidia chinensis var. chinensis (kiwifruit) genome highlights the challenges associated with draft genomes and gene prediction in plants

Most published genome sequences are drafts, and most are dominated by computational gene prediction. Draft genomes typically incorporate considerable sequence data that are not assigned to chromosomes, and predicted genes without quality confidence measures. The current Actinidia chinensis (kiwifruit) 'Hongyang' draft genome has 164\ua0Mb of sequences unassigned to pseudo-chromosomes, and omissions have been identified in the gene models

Apport de la prise en compte de la variabilité intra-classe dans les méthodes de démélange hyperspectral pour l'imagerie urbaine

This work is devoted to unmixing for urban areas. We particularly focused on the impact of intra-class variability on unmixing. We first described the results of a study highlighting intra-class variability assessed in real images. It appeared that this phenomenon was significant and had to be included in the mixing models. Based on the state of the art we developed 2 new mixing models dealing with intra-class variability. The first one is a linear one. The second one is a linear-quadratic one which allows to consider multiple scattering effects on buildings. First only the linear mixing model was considered. Currently it does not exist any unmixing method able to deal with this new model. So two methods were developed, UP-NMF and IP-NMF. UP-NMF is a new unmixing method based on an extension of the standard NMF. To overcome UP-NMF limitations an extended method is proposed, IP-NMF, which limit the spreading of each class by adding an inertia constraint in the cost function. These methods were firstly tested on a semi-synthetic data set. These tests allowed us to study the impact of the initialisation on our methods performance and also to fix the inertia parameter. We also compared the results of UP-NMF and IP-NMF to the results obtained with standard methods. The second tests were performed on an image taken above Toulouse. It appeared that IP-NMF is less sensitive to an error in the estimation of classes number than standard methods. Finally we developed a linear-quadratic method, LQIP-NMF, dealing with the non-linear mixing model previously described. In cases of high intra-class variability, the quadratic terms are drowned in the large variability of materials. So it seems that it is not relevant to taking into account these non-linearities.Au cours de cette thèse nous nous sommes intéressés à la problématique du démélange hyperspectral en milieux urbains. En particulier nous nous sommes penchés sur la prise en compte du phénomène de variabilité intra-classe dans les méthodes de démélange. La mise en évidence de la variabilité intra-classe a été le point de départ de cette étude. Nous avons ainsi constaté que ce phénomène était non-négligeable dans les milieux urbains et qu'il devait être pris en compte. En nous basant sur des modèles de mélange existants dans la littérature nous avons développé deux nouveaux modèles de mélange prenant en compte cette variabilité intra-classe. Le premier est un modèle de mélange linéaire. Le second est un modèle linéaire-quadratique qui permet de prendre en compte les réflexions multiples sur les bâtiments. Dans un premier temps nous ne nous sommes intéressés qu'au cas des modèles linéaires. Comme aucune méthode de la littérature ne permet d'effectuer le démélange à partir de nos modèles de mélange nous avons développé deux méthodes UP-NMF et IP-NMF. UP-NMF est une adaptation de la méthode NMF à notre modèle de mélange. Pour rendre compte de la notion de classes de matériaux purs une contrainte sur l'inertie des classes a été ajoutée à UP-NMF pour obtenir IP-NMF. Les premiers tests ont été effectués sur données semi-synthétiques et ont permis de déterminer l'impact de l'initialisation de ces méthodes sur leurs performances et de fixer le paramètre d'inertie. Les performances de UP-NMF et IP-NMF ont été comparées à celles des méthodes standards de démélange. Les seconds tests ont été effectués sur une portion d'image de Toulouse. Dans cette partie nous avons mis en évidence que, contrairement à des méthodes standards, les résultats de IP-NMF étaient peu sensibles à une erreur sur l'estimation du nombre de classes pures. Finalement nous avons développé une méthode de démélange linéaire-quadratique, LQIP-NMF, en nous basant sur le modèle que nous avons mis en place. Les tests de LQIP-NMF ont montré qu'en cas de trop forte variabilité intra-classe les effets de non-linéarité étaient de second ordre et qu'il ne semblait pas pertinent de les prendre en compte

Enhancing urban hyperspectral unmixing considering intra-class variability

Au cours de cette thèse nous nous sommes intéressés à la problématique du démélange hyperspectral en milieux urbains. En particulier nous nous sommes penchés sur la prise en compte du phénomène de variabilité intra-classe dans les méthodes de démélange. La mise en évidence de la variabilité intra-classe a été le point de départ de cette étude. Nous avons ainsi constaté que ce phénomène était non-négligeable dans les milieux urbains et qu'il devait être pris en compte. En nous basant sur des modèles de mélange existants dans la littérature nous avons développé deux nouveaux modèles de mélange prenant en compte cette variabilité intra-classe. Le premier est un modèle de mélange linéaire. Le second est un modèle linéaire-quadratique qui permet de prendre en compte les réflexions multiples sur les bâtiments. Dans un premier temps nous ne nous sommes intéressés qu'au cas des modèles linéaires. Comme aucune méthode de la littérature ne permet d'effectuer le démélange à partir de nos modèles de mélange nous avons développé deux méthodes UP-NMF et IP-NMF. UP-NMF est une adaptation de la méthode NMF à notre modèle de mélange. Pour rendre compte de la notion de classes de matériaux purs une contrainte sur l'inertie des classes a été ajoutée à UP-NMF pour obtenir IP-NMF. Les premiers tests ont été effectués sur données semi-synthétiques et ont permis de déterminer l'impact de l'initialisation de ces méthodes sur leurs performances et de fixer le paramètre d'inertie. Les performances de UP-NMF et IP-NMF ont été comparées à celles des méthodes standards de démélange. Les seconds tests ont été effectués sur une portion d'image de Toulouse. Dans cette partie nous avons mis en évidence que, contrairement à des méthodes standards, les résultats de IP-NMF étaient peu sensibles à une erreur sur l'estimation du nombre de classes pures. Finalement nous avons développé une méthode de démélange linéaire-quadratique, LQIP-NMF, en nous basant sur le modèle que nous avons mis en place. Les tests de LQIP-NMF ont montré qu'en cas de trop forte variabilité intra-classe les effets de non-linéarité étaient de second ordre et qu'il ne semblait pas pertinent de les prendre en compte.This work is devoted to unmixing for urban areas. We particularly focused on the impact of intra-class variability on unmixing. We first described the results of a study highlighting intra-class variability assessed in real images. It appeared that this phenomenon was significant and had to be included in the mixing models. Based on the state of the art we developed 2 new mixing models dealing with intra-class variability. The first one is a linear one. The second one is a linear-quadratic one which allows to consider multiple scattering effects on buildings. First only the linear mixing model was considered. Currently it does not exist any unmixing method able to deal with this new model. So two methods were developed, UP-NMF and IP-NMF. UP-NMF is a new unmixing method based on an extension of the standard NMF. To overcome UP-NMF limitations an extended method is proposed, IP-NMF, which limit the spreading of each class by adding an inertia constraint in the cost function. These methods were firstly tested on a semi-synthetic data set. These tests allowed us to study the impact of the initialisation on our methods performance and also to fix the inertia parameter. We also compared the results of UP-NMF and IP-NMF to the results obtained with standard methods. The second tests were performed on an image taken above Toulouse. It appeared that IP-NMF is less sensitive to an error in the estimation of classes number than standard methods. Finally we developed a linear-quadratic method, LQIP-NMF, dealing with the non-linear mixing model previously described. In cases of high intra-class variability, the quadratic terms are drowned in the large variability of materials. So it seems that it is not relevant to taking into account these non-linearities

The SSHR Solar Reference Spectrum

International audienceThe determination of many high-resolution solar reference spectra at high accuracy is crucial and represents a fundamental input for solar physics (Sun modeling), terrestrial atmospheric photochemistry and Earth's climate (climate's modeling). Thus, we present a new solar irradiance reference spectrum at high resolution representative of a solar minimum. The SOLAR Spectrum at High Resolution (SSHR) is developed by normalizing high spectral resolution solar line data to the absolute irradiance scale of the SOLAR-ISS reference spectrum. The resulting disk-integrated solar spectrum has at least 0.01 nm spectral resolution and spans 300-4400 nm. Below 1000 nm, the spectral resolution is less than 0.001 nm. One of our motivations is to develop a new radiometrically well calibrated solar spectrum with high spectral resolution for disk-integrated, but also for the first time for disk-center or intermediate cases. These spectra must meet the needs of the MicroCarb mission and the 4AOP radiative transfer software

A linear-quadratic unsupervised hyperspectral unmixing method dealing with intra-class variability

International audienceIn hyperspectral imagery, unmixing methods are often used to analyse the composition of the pixels. Such methods usually suppose that a unique spectral signature, called an endmember, can be associated with each pure material present in the scene. This assumption is no more valid for materials that exhibit spectral variability due to illumination conditions, weathering, slight variations of the composition, etc. Methods currently appear dealing with this spectral variability and based on linear mixing assumption. However, intra-class variability issues frequently appear in non-flat scenes, and particularly in urban scenes. For urban scenes, the linear-quadratic mixing models better depict the radiative transfer. In this paper, we propose a new unsupervised unmixing method based on the assumption of a linear-quadratic mixing model, that deals with intra-class spectral variability. A new formulation of linear-quadratic mixing is proposed. An unmixing method is presented to process this new model. The method is tested on a semi-synthetic data set built with spectra extracted from a real hyperspectral image and mixtures of these spectra. Based on the results of non-linear and linear unmixing, we discuss the interest of considering the non-linearity regarding the impact of intra-class variability

Impact of the initialisation of a blind unmixing method dealing with intra-class variability

International audienceIn hyperspectral imagery, unmixing methods are often used to analyse the composition of the pixels. Such methods usually suppose that a single spectral signature, called an endmember, can be associated with each pure material present in the scene. Such an assumption is no more valid for materials that exhibit spectral variability due to illumination conditions, weathering, slight variations of the composition, etc. In this paper, we investigate a new method based on the assumption of a linear mixing model, that deals with intra-class spectral variability. A new formulation of the linear mixing is provided. In our model a pure material cannot be described by a single spectrum in the image but it can in a pixel. An approach method is presented to handle this new model. It is based on pixel-by-pixel Nonnegative Matrix Factorization (NMF) methods. The methods are tested on a semi-synthetic data set built with spectra extracted from a real hyperspectral image and mixtures of these spectra. We particularly focused our tests to study the impact of the initialisation of our methods

Application and Extension of PCA Concepts to Blind Unmixing of Hyperspectral Data with Intra-class Variability

International audienceThe most standard blind source separation (BSS) methods address the situation when a set of signals are available, e.g. from measurements, and all of them are linear memoryless combinations, with unknown coefficient values, of the same limited set of unknown source signals. BSS methods aim at estimating these unknown source signals and/or coefficients. This generic problem is e.g. faced in the field of Earth observation (where it is also called “unsupervised unmixing”), when considering the commonly used (over)simplified model of hyperspectral images. Each pixel of such an image has an associated reflectance spectrum derived from measurements, which is defined by the fraction of sunlight power reflected by the corresponding Earth surface at each wavelength. Each source signal is then the single reflectance spectrum associated with one of the classes of pure materials which are present in the region of Earth corresponding to the overall considered hyperspectral image. Besides, the associated coefficients define the surfaces on Earth covered with each of these pure materials in each sub-region corresponding to one pixel of the considered image. However, real hyperspectral data e.g. obtained in urban areas have a much more complex structure than the above basic model: each class of pure materials (e.g. roof tiles, trees or asphalt) has so-called spectral or intra-class variability, i.e. it yields a somewhat different spectral component in each pixel of the image. In this complex framework, this chapter shows that Principal Component Analysis (PCA) and its proposed extension are of high interest at three stages of our investigation. First, PCA allows us to analyze the above-mentioned spectral variability of real high-dimensional hyperspectral data and to propose an extended data model which is suited to these complex data. We then develop a versatile extension of BSS methods based on Nonnegative Matrix Factorization, which adds the capability to handle arbitrary forms of intra-class variability by transposing PCA concepts to this original version of the BSS tramework. Finally, PCA again proves to be very well suited to analyzing the high-dimensional data obtained as the result of the proposed BSS method

A method based on NMF dealing with intra-class variability for unsupervised hyperspectral unmixing

International audienceIn hyperspectral imagery, unmixing methods are often used to analyse the composition of the pixels. Such methods usually supposed that a single spectral signature, called an endmember, can be associated with each pure material present in the scene. Such an assumption is no more valid for materials that exhibit spectral variability due to illumination conditions, weathering, slight variations of the composition, etc. In this paper, we proposed a new method based on the assumptions of a linear mixing model, that deals with within intra-class spectral variability. A new formulation of the linear mixing is proposed. It introduces not only a scaling factor but a complete representation of the spectral variability in the pure spectrum representation. In our model a pure material cannot be described by a single spectrum in the image but it can in a pixel. A method is presented to process this new model. It is based on a pixel-by-pixel Non-negative Matrix Factorization (NMF) method. The method is tested on a semi-synthetic set of data built with spectra extracted from a real hyperspectral image and mixtures of these spectra. Thus we demonstrate the interest of our method on realistic intra-class variabilities