Hyperspectral images segmentation: a proposal

Hyper-Spectral Imaging (HIS) also known as chemical or spectroscopic imaging is an emerging technique that combines imaging and spectroscopy to capture both spectral and spatial information from an object. Hyperspectral images are made up of contiguous wavebands in a given spectral band. These images provide information on the chemical make-up profile of objects, thus allowing the differentiation of objects of the same colour but which possess make-up profile. Yet, whatever the application field, most of the methods devoted to HIS processing conduct data analysis without taking into account spatial information.Pixels are processed individually, as an array of spectral data without any spatial structure. Standard classification approaches are thus widely used (k-means, fuzzy-c-means hierarchical classification...). Linear modelling methods such as Partial Least Square analysis (PLS) or non linear approaches like support vector machine (SVM) are also used at different scales (remote sensing or laboratory applications). However, with the development of high resolution sensors, coupled exploitation of spectral and spatial information to process complex images, would appear to be a very relevant approach. However, few methods are proposed in the litterature. The most recent approaches can be broadly classified in two main categories. The first ones are related to a direct extension of individual pixel classification methods using just the spectral dimension (k-means, fuzzy-c-means or FCM, Support Vector Machine or SVM). Spatial dimension is integrated as an additionnal classification parameter (Markov fields with local homogeneity constrainst [5], Support Vector Machine or SVM with spectral and spatial kernels combination [2], geometrically guided fuzzy C-means [3]...). The second ones combine the two fields related to each dimension (spectral and spatial), namely chemometric and image analysis. Various strategies have been attempted. The first one is to rely on chemometrics methods (Principal Component Analysis or PCA, Independant Component Analysis or ICA, Curvilinear Component Analysis...) to reduce the spectral dimension and then to apply standard images processing technics on the resulting score images i.e. data projection on a subspace. Another approach is to extend the definition of basic image processing operators to this new dimensionality (morphological operators for example [1, 4]). However, the approaches mentioned above tend to favour only one description either directly or indirectly (spectral or spatial). The purpose of this paper is to propose a hyperspectral processing approach that strikes a better balance in the treatment of both kinds of information....Cet article présente une stratégie de segmentation d’images hyperspectrales liant de façon symétrique et conjointe les aspects spectraux et spatiaux. Pour cela, nous proposons de construire des variables latentes permettant de définir un sous-espace représentant au mieux la topologie de l’image. Dans cet article, nous limiterons cette notion de topologie à la seule appartenance aux régions. Pour ce faire, nous utilisons d’une part les notions de l’analyse discriminante (variance intra, inter) et les propriétés des algorithmes de segmentation en région liées à celles-ci. Le principe générique théorique est exposé puis décliné sous la forme d’un exemple d’implémentation optimisé utilisant un algorithme de segmentation en région type split and merge. Les résultats obtenus sur une image de synthèse puis réelle sont exposés et commentés

Hyperspectral image segmentation: the butterfly approach

International audienceFew methods are proposed in the litterature for coupling the spectral and the spatial dimension available on hyperspectral images. This paper proposes a generic segmentation scheme named butterfly based on an iterative process and a cross analysis of spectral and spatial information. Indeed, spatial and spatial structures are extracted in spatial and spectral space respectively both taking into account the other one. To apply this layout on hyperspectral imgages, we focus particulary on spatial and spectral structures i.e. topologic concepts and latent variable for the spatial and the spectral space respectively. Moreover, a cooperation scheme with these structures is proposed. Finally, results obtained on real hyperspectral images using this specific implementation of the butterfly approach are presented and discussed

Qu'attendre des matrices de détecteur à champ plan ?

International audienceFocal Plane Array Detectors have paved the way so numerous applications in optics and in particular in spectrometry. After having introduced how this component works, thes control with solid-state spectrometers, hyperspectral microscopy.Les matrices de détecteur à champ plan ont permis de développer de nombreuses applications en optique et en particulier en spectrométrie. Après avoir présenté le fonctionnement de ce composant, ces applications sont décrites : astronomie, contrôles industriels en ligne par spectrométrie, microscopie hyperspectrale

Etude de l'utilisation de la spectroscopie proche infrarouge pour la prédiction du potentiel méthane de déchets solides

La digestion anaérobie est un moyen de traitement des déchets solides produisant de l'énergie sous forme de biogaz (méthane et dioxyde de carbone). L'optimisation de la production de méthane passe par une sélection des déchets à fort potentiel méthane. Actuellement, la mesure du potentiel méthane est réalisée par le test BMP (Biochemical Methane Potential), qui repose sur une fermentation pouvant durer plus de 30 jours, ce qui est trop long pour une installation industrielle. Une méthode rapide de détermination du potentiel méthane est donc nécessaire. Le BMP est lié uniquement à la quantité et à la qualité de la matière organique. Cette méthode doit donc réaliser une analyse globale et rapide de la matière organique. L'objectif de la thèse a été d'identifier et d'étudier une méthode rapide d'analyse de la matière organique de déchets solides permettent de prédire le potentiel méthane. Suite au travail bibliographique, la spectroscopie proche infrarouge s'est révélée la méthode la plus appropriée: analyse globale et rapide, non destructive, préparation d'échantillon réduite, possibilité d'utiliser des fibres optiques pour déporter la mesure. Nous avons ensuite étudié des étalonnages pour prédire le potentiel méthane d'un ensemble homogène de 74 déchets. Un coefficient de corrélation de 0,76 et un écart standard de prédiction (RMSEP) de 28 ml CH4.g-1 MV ont été obtenus. Ensuite, les coefficients du modèle ont été analysés par rapport aux molécules présentes et rapprochés des variables sélectionnées par algorithme génétique afin de valider ce modèle d'un point de vue chimique. Enfin, la robustesse de ce modèle vis à vis de l'origine des échantillons et de l'humidité a été testée. Les résultats montrent clairement le fort potentiel de la spectroscopie proche infrarouge pour la prédiction du potentiel méthane. Pour une utilisation industrielle, il ressort qu'une attention particulière doit être portée sur l'ensemble d'étalonnage, qui doit être le plus exhaustif possible.Anaerobic digestion is a solution to process solid waste, while producing energy by biogas production (methane and carbon dioxide). Methane production could be optimized by selecting only wastes with high methane potential. Currently, the BMP (Biochemical Methane Potential) test is conducted to predict the methane potential. This test is based on a fermentation process. It is time consuming, sometimes, lasting over 30 days, which is too long from an industrial point of view. A rapid method for determining the methane potential is therefore urgently needed. The BMP value depends only on the quantity and the quality of the organic matter, so a method capable of determining the quality and quantity of organic matter is searched for. The objective of this thesis was to identify and study such a method. First, a bibliographic study led us to chose the near infrared (NIR) spectroscopy method: fast and global analysis of the organic matter, non-destructive method, few or no sample preparation, and remote monitoring by use of fiber optics. Second, a calibration for predicting the BMP of and homogenous sample set has been built based on a 74-waste sample set. A correlation coefficient of R = 0,76 and a standard error of prediction (RMSEP = 28 ml CH4.g-1 VS). Then, the regression coefficients (called b coefficients) were analysed with regard to the molecules in the waste and were compared to the variables selected from the spectrum, in order to validate the model from a chemical point of view. Finally, the robustness of the model, regarding the waste origins and the moisture was tested with heterogeneous samples set. Results show the potential of the near infrared spectroscopy to predict the methane potential quickly, but attention must be paid on the calibration data set when an industrial implementation is dealt with..MONTPELLIER-BU Sciences (341722106) / SudocSudocFranceF

Putting agricultural equipment and digital technologies at the cutting edge of agroecology

The agro-ecological transition is an ambitious challenge. It can be met by implementing the fundamentals of agroecology (use of biodiversity, integration of agriculture in landscapes, closure of flow loops) in the context of a broad and renewed offer of technologies: agro-equipment, biotechnology, digital technologies… This article explores the role that agro-equipment and digital services can play in this transition. These technologies contribute through various levers to the agro-ecological transition: by improving farming efficiency (more service rendered for the same environmental impact), by precision farming (adaptation of the operations to the needs of the plant or the animal based on a monitoring–diagnosis–recommendation cycle) and by the development of specialized machinery helping the farmer to achieve “flow loop-closing” (at the plot level, by maintaining the soil quality, or at the farm level, with the recycling of organic effluents) or to take advantage of biodiversity (e.g., with agro-equipment adapted to mixed crops). The technological bricks that are requested and for which advances are expected are: sensors (to measure plant or animal needs) and associated digital technologies (information transfer, data processing), precision technologies for input application, robotics, specialized machines to manage soil cover and weeds, or for agroforestry. The brakes and engines for innovation in agro-equipment are studied. The brakes are the generally small structure of the farm manufacturing companies, the deficit of the demand from farmers and the complexity − either real or perceived − of these equipments. To encourage innovation, several levers are to be used: involving users in the design of agro-equipments, creating financial incentives for innovative equipment purchase, and training end-users, prescribers and dealers to the high potential of these new technologies. In conclusion, putting agro-equipment and digital technology at the service of agroecology is not a straightforward route, but it is above all a real opportunity to produce better, technically and organizationally, with the emergence of new solidarities (sharing of data and knowledge). This is why it is absurd, as it is sometimes read, to oppose agroecology and technology. Agroecology is a set of practices to be built, while agro-equipment and digital technologies are a set of resources to be mobilized, with others, to achieve the objectives of sustainable agricultural production

Caractérisation expérimentale des émissions de pesticides vers l'air pendant les pulvérisations viticoles

MONTPELLIER-SupAgro La Gaillarde (341722306) / SudocSudocFranceF

Putting agricultural equipment and digital technologies at the cutting edge of agroecology

The agro-ecological transition is an ambitious challenge. It can be met by implementing the fundamentals of agroecology (use of biodiversity, integration of agriculture in landscapes, closure of flow loops) in the context of a broad and renewed offer of technologies: agro-equipment, biotechnology, digital technologies… This article explores the role that agro-equipment and digital services can play in this transition. These technologies contribute through various levers to the agro-ecological transition: by improving farming efficiency (more service rendered for the same environmental impact), by precision farming (adaptation of the operations to the needs of the plant or the animal based on a monitoring–diagnosis–recommendation cycle) and by the development of specialized machinery helping the farmer to achieve “flow loop-closing” (at the plot level, by maintaining the soil quality, or at the farm level, with the recycling of organic effluents) or to take advantage of biodiversity (e.g., with agro-equipment adapted to mixed crops). The technological bricks that are requested and for which advances are expected are: sensors (to measure plant or animal needs) and associated digital technologies (information transfer, data processing), precision technologies for input application, robotics, specialized machines to manage soil cover and weeds, or for agroforestry. The brakes and engines for innovation in agro-equipment are studied. The brakes are the generally small structure of the farm manufacturing companies, the deficit of the demand from farmers and the complexity − either real or perceived − of these equipments. To encourage innovation, several levers are to be used: involving users in the design of agro-equipments, creating financial incentives for innovative equipment purchase, and training end-users, prescribers and dealers to the high potential of these new technologies. In conclusion, putting agro-equipment and digital technology at the service of agroecology is not a straightforward route, but it is above all a real opportunity to produce better, technically and organizationally, with the emergence of new solidarities (sharing of data and knowledge). This is why it is absurd, as it is sometimes read, to oppose agroecology and technology. Agroecology is a set of practices to be built, while agro-equipment and digital technologies are a set of resources to be mobilized, with others, to achieve the objectives of sustainable agricultural production

Mesures robustes en ligne des solutés organiques par spectrométrie infrarouge et étalonnages multivariés

MONTPELLIER-BU Sciences (341722106) / SudocSudocFranceF

Agriculture et Agro-alimentaire : Technologies de l'information pour garantir la qualité des produits et le respect de l'environnement

National audiencePresently, due to the social demand, two trends weigh heavily on the agricultural and food production : foodstuff quality enhancement and the search for more environmental-friendly agricultural practices. Agriculture and the food industry are expected to take into account technical, social, economical and environmental factors. To do this means developing adaptabilty , and to adapt quickly one requires reliable and accurate information. This is why it is developed technologies and methodologies for acquiring and processing information in the agricultural fields and in the food processing plants. In agriculture, our main goals are to collect information from the field and to accurately model the cultivation practices that need to be improved. Embedded sensors are based on artificial vision and microwaves. In artificial vision, the main obstacles to be overcome are heterogenous lighting conditions, the great variability of biological products and the segmentation process (due to overlapping objects). Complex cultivation practices such as pesticide spraying or manuring are either optimised -using fluid mechanics for spraying, balistics for manuring- or replaced by robotic-based techniques. In food processing, research has been carried out on the modelling of the human perception and decision-making when assessing food quality. Powerful investigation techniques such as magnetic nuclear resonance are currently being studied to provide more in-depth knowledge of food products. Low-cost sensors (based on artificial vision, infrared spectroscopy) are developed to allow on-line, non destructive measurement of food characteristics (for instance sugar content in fruit). That implies technology studies (choice of the components, design), modelling (for instance, the light distribution in fruit), and chemometrics. Finally, the decisions of the human operator of food processing lines are modelled by artificial intelligence : fuzzy rules are created using either expert knowledge or fuzzy inference systems. These research methods are consistently applied to relevant and every-day problems. They engender multidisciplinary approaches carried out under heavy constraints (of cost, real-time, robustness) at the crossroads where Engineering, and Biology meet Social demands.Sous la pression sociétale, deux tendances dominent les productions agricoles et alimentaires : l'amélioration de la qualité des produits et la recherche de pratiques plus respectueuses de l'environnement. C'est pourquoi sont développées des technologies et des méthodologies d'acquisition et de traitement de l'information dans ce double objectif. En agriculture, les efforts se portent sur la collecte d'informations de terrain et sur la modélisation des procédés culturaux. Les capteurs développés sont basés sur la vision numérique, l'analyse d'images et les capteurs micro-ondes. Les opérations culturales sensibles telles que l'application d'intrants (pesticides, engrais) sont soit optimisées soit remplacées par des techniques alternatives basées sur la robotique. En agro-alimentaire, les recherches concernent le développement de capteurs spécifiques (vision, infrarouge, RMN_) et la modélisation de la décision des opérateurs qui gèrent les lignes de production. Cette recherche finalisée se situe donc dans un domaine très contraint à l'interface des Sciences pour l'Ingénieur, Sciences du Vivant et des Sciences Humaines et Sociales