Méthodes de séparation aveugle de sources et application à la télédétection spatiale

Cette thèse concerne la séparation aveugle de sources, qui consiste à estimer un ensemble de signaux sources inconnus à partir d'un ensemble de signaux observés qui sont des mélanges à paramètres inconnus de ces signaux sources. C'est dans ce cadre que le travail de recherche de cette thèse concerne le développement et l'utilisation de méthodes linéaires innovantes de séparation de sources pour des applications en imagerie de télédétection spatiale. Des méthodes de séparation de sources sont utilisées pour prétraiter une image multispectrale en vue d'une classification supervisée de ses pixels. Deux nouvelles méthodes hybrides non-supervisées, baptisées 2D-Corr-NLS et 2D-Corr-NMF, sont proposées pour l'extraction de cartes d'abondances à partir d'une image multispectrale contenant des pixels purs. Ces deux méthodes combinent l'analyse en composantes parcimonieuses, le clustering et les méthodes basées sur les contraintes de non-négativité. Une nouvelle méthode non-supervisée, baptisée 2D-VM, est proposée pour l'extraction de spectres à partir d'une image hyperspectrale contenant des pixels purs. Cette méthode est basée sur l'analyse en composantes parcimonieuses. Enfin, une nouvelle méthode est proposée pour l'extraction de spectres à partir d'une image hyperspectrale ne contenant pas de pixels purs, combinée avec une image multispectrale, de très haute résolution spatiale, contenant des pixels purs. Cette méthode est fondée sur la factorisation en matrices non-négatives couplée avec les moindres carrés non-négatifs. Comparées à des méthodes de la littérature, d'excellents résultats sont obtenus par les approches méthodologiques proposées.This thesis concerns the blind source separation problem, which consists in estimating a set of unknown source signals from a set of observed signals which are mixtures of these source signals, with unknown mixing coefficients. In this thesis, we develop and use innovative linear source separation methods for applications in remote sensing imagery. Source separation methods are used and applied in order to preprocess a multispectral image for a supervised classification of this image. Two new unsupervised methods, called 2D-Corr-NLS and 2D-Corr-NMF, are proposed in order to extract abundance maps from a multispectral image with pure pixels. These methods are based on sparse component analysis, clustering and non-negativity constraints. A new unsupervised method, called 2D-VM, is proposed in order to extract endmember spectra from a hyperspectral image with pure pixels. This method is based on sparse component analysis. Also, a new method is proposed for extracting endmember spectra from a hyperspectral image without pure pixels, combined with a very high spatial resolution multispectral image with pure pixels. This method is based on non-negative matrix factorization coupled with non-negative least squares. Compared to literature methods, excellent results are obtained by the proposed methodological approaches

Méthodes de séparation aveugle de sources et application à la télédétection spatiale

TOULOUSE3-BU Sciences (315552104) / SudocSudocFranceF

Extraction of a Specific Land-cover Class Using Deep Features and SVM One-class Classification

International audience    Land use and land cover maps are required for various purposes in scientific, administrative and commercial domains. Such maps can be efficiently derived by supervised classification. As the spatial resolution of remotely sensed images increased, geographic object based analysis (GEOBIA) approaches became more suitable concerning the demand for faster and more accurate classification. Multi-class supervised classification requires the collection of reference samples of all available classes in the study area, which is a costly and time consuming process. However in many case users may only be interested in a specific land class such as extracting urban areas, detection of cars, or retrieving trees. This could be referred to as a one-class classification problem, which allows for the learning of a classification model from labeled reference data for the class of interest only. In this case there is no need for a representative dataset for the counter-class which consists of all other classes. One common solution to deal with one-class classification problem is based on One-class SVM. This method has proved useful in document classification, texture segmentation, and image retrieval. Moreover, deep learning through convolutional neural networks (CNN) has been intensively used in remote sensing field. CNNs are now commonly used for land cover/land use classification and semantic labeling tasks in large image archives while achieving the state-of-the-art performances In this study, we propose to extract examples of classes of interest from high spatial resolution images by integrating one-class Support Vector Machines (SVMs), Deep features from pre-trained CNN and object-based image analysis approach. We also compared the performance from the proposed method with the supervised multi-class classification. The results indicate that the proposed method achieve similar performances the multi-class method, and could be a promising way to provide relatively quick and efficient way in extracting a specific land class from high spatial resolution images

Blind Unmixing of Hyperspectral Remote Sensing Data: A New Geometrical Method Based on a Two-Source Sparsity Constraint

Blind source separation (or unmixing) methods process a set of mixed signals, which are typically linear memoryless combinations of source signals, so as to estimate these unknown source signals and/or combination coefficients. These methods have been extensively applied to hyperspectral images in the field of remote sensing, because the reflectance spectrum of each image pixel is often a mixture of elementary contributions, due to the limited spatial resolution of hyperspectral remote sensing sensors. Each spatial source signal then corresponds to a pure material, and its value in each pixel is equal to the “abundance fraction” of the corresponding Earth surface covered by that pure material. The mixing coefficients then form the pure material spectra. Various unmixing methods have been designed for this data model and the majority of them are either geometrical or statistical, or even based on sparse regressions. Various such unmixing techniques mainly consider assumptions that are related to the presence or absence of pure pixels (i.e., pixels which contain only one pure material). The case when, for each pure material, the image includes at least one pixel or zone which only contains that material yielded attractive unmixing methods, but corresponds to a stringent sparsity condition. We here aim at relaxing that condition, by only requesting a few tiny pixel zones to contain two pure materials. The proposed linear and geometrical sparse-based, blind (or unsupervised) unmixing method first automatically detects these zones. Each such zone defines a line in the data representation space. These lines are then estimated and clustered. The pairs of cluster centers, corresponding to lines, which have an intersection, yield the spectra of pure materials, forming the columns of the mixing matrix. Finally, the proposed method derives all abundance fractions, i.e., source signals, by using a least squares method with a non-negativity constraint. This method is applied to realistic synthetic images and is shown to outperform various methods from the literature. Indeed, and for the conducted experiments, when considering the pure material spectra extraction, the obtained improvements, for the considered spectral angle mapper performance criterion, vary between 0.02∘ and 12.43∘. For the abundance fractions estimation, the proposed technique is able to achieve a normalized mean square error lower than 0.01%, while the tested literature methods yield errors greater than 0.1%

Hyperspectral Unmixing Based on Constrained Bilinear or Linear-Quadratic Matrix Factorization

Unsupervised hyperspectral unmixing methods aim to extract endmember spectra and infer the proportion of each of these spectra in each observed pixel when considering linear mixtures. However, the interaction between sunlight and the Earth’s surface is often very complex, so that observed spectra are then composed of nonlinear mixing terms. This nonlinearity is generally bilinear or linear quadratic. In this work, unsupervised hyperspectral unmixing methods, designed for the bilinear and linear-quadratic mixing models, are proposed. These methods are based on bilinear or linear-quadratic matrix factorization with non-negativity constraints. Two types of algorithms are considered. The first ones only use the projection of the gradient, and are therefore linked to an optimal manual choice of their learning rates, which remains the limitation of these algorithms. The second developed algorithms, which overcome the above drawback, employ multiplicative projective update rules with automatically chosen learning rates. In addition, the endmember proportions estimation, with three alternative approaches, constitutes another contribution of this work. Besides, the reduction of the number of manipulated variables in the optimization processes is also an originality of the proposed methods. Experiments based on realistic synthetic hyperspectral data, generated according to the two considered nonlinear mixing models, and also on two real hyperspectral images, are carried out to evaluate the performance of the proposed approaches. The obtained results show that the best proposed approaches yield a much better performance than various tested literature methods

Hyperspectral and multispectral data fusion based on linear-quadratic nonnegative matrix factorization

International audienceThis paper proposes three multisharpening approaches to enhance the spatial resolution of urban hyperspectral remote sensing images. These approaches, related to linear-quadratic spectral unmixing techniques, use a linear-quadratic nonnegative matrix factorization (NMF) multiplicative algorithm. These methods begin by unmixing the observable high-spectral/low-spatial resolution hyperspectral and high-spatial/low-spectral resolution multispectral images. The obtained high-spectral/high-spatial resolution features are then recombined, according to the linear-quadratic mixing model, to obtain an unobservable multisharpened high-spectral/high-spatial resolution hyperspectral image. In the first designed approach, hyperspectral and multispectral variables are independently optimized, once they have been coherently initialized. These variables are alternately updated in the second designed approach. In the third approach, the considered hyperspectral and multispectral variables are jointly updated. Experiments, using synthetic and real data, are conducted to assess the efficiency, in spatial and spectral domains, of the designed approaches and of linear NMF-based approaches from the literature. Experimental results show that the designed methods globally yield very satisfactory spectral and spatial fidelities for the multisharpened hyperspectral data. They also prove that these methods significantly outperform the used literature approaches

Contribution of non-negative matrix factorization to the classification of remote sensing images

Authors: M.S. Karoui, Y. Deville, S. Hosseini, A. Ouamri, D. DucrotInternational audienc

Spectral Unmixing Based Approach for Measuring Gas Flaring from VIIRS NTL Remote Sensing Data: Case of the Flare FIT-M8-101A-1U, Algeria

During the oil extraction procedure, natural gases escape from wells, and the process of recuperating such gases requires important investments from oil and gas companies. That is why, most often, they favor burning them with flares. This practice, which is frequently employed by oil-producing companies, is a major cause of greenhouse gas emissions. Under growing demands from the World Bank and environmental defenders, many producer countries are devoted to decreasing gas flaring. For this reason, several researchers in the oil and gas industry, academia, and governments are working to propose new methods for estimating flared gas volumes, and among the most used techniques are those that exploit remote sensing data, particularly Visible Infrared Imaging Radiometer Suite (VIIRS) Nighttime Light (NTL) ones. Indeed, it is possible to extract, from such data, some physical parameters of flames produced by gas flares. In this investigation, a linear spectral unmixing-based approach, which addresses the spectral variability phenomenon, was designed to estimate accurate physical parameters from VIIRS NTL data. Then, these parameters are used to derive flared gas volumes through intercepting zero polynomial regression models that exploit in situ measurements. Experiments based on synthetic data were first conducted to validate the proposed linear spectral unmixing-based approach. Second, experiments based on real VIIRS NTL data covering the flare, named FIT-M8-101A-1U and located in the Berkine basin (Hassi Messaoud) in Algeria, were carried out. Then, the obtained flared gas volumes were compared with in situ measurements

Unsupervised hyperspectral band selection by combination of unmixing and sequential clustering techniques

Selecting the decisive spectral bands is a key issue in unsupervised hyperspectral band selection techniques. These methods are the most popular ways for dimensionality reduction of original data. A compact data representation without compromising the physical information and optimizing the separation between different materials are the main objectives of such selection processes. In this work, a hyperspectral band selection approach is proposed based on linear spectral unmixing and sequential clustering techniques. The use of these two specific techniques constitutes the main novelty of this investigation. The proposed approach operates in different successive steps. It starts with extracting material spectra contained in the considered data using an unmixing method. Then, the variance of extracted spectra samples is calculated at each wavelength, which results in a variances vector. This one is segmented into a fixed number of clusters using a sequential clustering strategy. Finally, only one spectral band is selected for each segment. This band corresponds to the wavelength at which a maximum variance value is obtained. Experiments on three real hyperspectral data demonstrate the superiority of the proposed approach in comparison with four methods from the literature