4 research outputs found

    Multi-Classifiers And Decision Fusion For Robust Statistical Pattern Recognition With Applications To Hyperspectral Classification

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    In this dissertation, a multi-classifier, decision fusion framework is proposed for robust classification of high dimensional data in small-sample-size conditions. Such datasets present two key challenges. (1) The high dimensional feature spaces compromise the classifiers’ generalization ability in that the classifier tends to overit decision boundaries to the training data. This phenomenon is commonly known as the Hughes phenomenon in the pattern classification community. (2) The small-sample-size of the training data results in ill-conditioned estimates of its statistics. Most classifiers rely on accurate estimation of these statistics for modeling training data and labeling test data, and hence ill-conditioned statistical estimates result in poorer classification performance. This dissertation tests the efficacy of the proposed algorithms to classify primarily remotely sensed hyperspectral data and secondarily diagnostic digital mammograms, since these applications naturally result in very high dimensional feature spaces and often do not have sufficiently large training datasets to support the dimensionality of the feature space. Conventional approaches, such as Stepwise LDA (S-LDA) are sub-optimal, in that they utilize a small subset of the rich spectral information provided by hyperspectral data for classification. In contrast, the approach proposed in this dissertation utilizes the entire high dimensional feature space for classification by identifying a suitable partition of this space, employing a bank-of-classifiers to perform “local” classification over this partition, and then merging these local decisions using an appropriate decision fusion mechanism. Adaptive classifier weight assignment and nonlinear pre-processing (in kernel induced spaces) are also proposed within this framework to improve its robustness over a wide range of fidelity conditions. Experimental results demonstrate that the proposed framework results in significant improvements in classification accuracies (as high as a 12% increase) over conventional approaches

    Multi-Classifiers And Decision Fusion For Robust Statistical Pattern Recognition With Applications To Hyperspectral Classification

    Get PDF
    In this dissertation, a multi-classifier, decision fusion framework is proposed for robust classification of high dimensional data in small-sample-size conditions. Such datasets present two key challenges. (1) The high dimensional feature spaces compromise the classifiers’ generalization ability in that the classifier tends to overit decision boundaries to the training data. This phenomenon is commonly known as the Hughes phenomenon in the pattern classification community. (2) The small-sample-size of the training data results in ill-conditioned estimates of its statistics. Most classifiers rely on accurate estimation of these statistics for modeling training data and labeling test data, and hence ill-conditioned statistical estimates result in poorer classification performance. This dissertation tests the efficacy of the proposed algorithms to classify primarily remotely sensed hyperspectral data and secondarily diagnostic digital mammograms, since these applications naturally result in very high dimensional feature spaces and often do not have sufficiently large training datasets to support the dimensionality of the feature space. Conventional approaches, such as Stepwise LDA (S-LDA) are sub-optimal, in that they utilize a small subset of the rich spectral information provided by hyperspectral data for classification. In contrast, the approach proposed in this dissertation utilizes the entire high dimensional feature space for classification by identifying a suitable partition of this space, employing a bank-of-classifiers to perform “local” classification over this partition, and then merging these local decisions using an appropriate decision fusion mechanism. Adaptive classifier weight assignment and nonlinear pre-processing (in kernel induced spaces) are also proposed within this framework to improve its robustness over a wide range of fidelity conditions. Experimental results demonstrate that the proposed framework results in significant improvements in classification accuracies (as high as a 12% increase) over conventional approaches

    Band Selection from Hyperspectral Data for Conifer Species Identification

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    Hyperspectral data compression and dimension reducing are very important to computer processing and data transmission. A small number of bands, containing relatively large amount of spectral information, are usually sufficient to many application purposes. Therefore, how to select a small number of bands without loss of much information from all the bands is a critical issue. In this paper, a method of band selection using band prioritization with peak values of sum of 30-eigenvector pertinent to principal component analysis (PCA) was developed. An error back-propagation neural network (NN) algorithm was applied to evaluate the effectiveness of the band selection method in forest species recognition. The results show that, when entering NN with 6–20 bands selected from a total of 161 bands of hyperspectral data for identifying six conifer species, the average recognition accuracy improvement of 11.20% can be obtained using the new band selection method over the method of equal-interval band selection

    Développement et automatisation de méthodes de classification à partir de séries temporelles d'images de télédétection : application aux changements d'occupation des sols et à l'estimation du bilan carbone

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    La quantité de données de télédétection archivées est de plus en plus importante et grâce aux nouveaux et futurs satellites, ces données offriront une plus grande diversité de caractéristiques : spectrale, temporelle, résolution spatiale et superficie de l'emprise du satellite. Cependant, il n'existe pas de méthode universelle qui maximise la performance des traitements pour tous les types de caractéristiques citées précédemment; chaque méthode ayant ses avantages et ses inconvénients. Les travaux de cette thèse se sont articulés autour de deux grands axes que sont l'amélioration et l'automatisation de la classification d'images de télédétection, dans le but d'obtenir une carte d'occupation des sols la plus fiable possible. En particulier, les travaux ont portés sur la la sélection automatique de données pour la classification supervisée, la fusion automatique d'images issues de classifications supervisées afin de tirer avantage de la complémentarité des données multi-sources et multi-temporelles et la classification automatique basée sur des séries temporelles et spectrales de référence, ce qui permettra la classification de larges zones sans référence spatiale. Les méthodes ont été testées et validées sur un panel de données très variées de : capteurs : optique (Formosat-2, Spot 2/4/5, Landsat 5/7, Worldview-2, Pleiades) et radar (Radarsat,Terrasar-X), résolutions spatiales : de haute à très haute résolution (de 30 mètres à 0.5 mètre), répétitivités temporelles (jusqu'à 46 images par an) et zones d'étude : agricoles (Toulouse, Marne), montagneuses (Pyrénées), arides (Maroc, Algérie). Deux applications majeures ont été possibles grâce à ces nouveaux outils : l'obtention d'un bilan carbone à partir des rotations culturales obtenues sur plusieurs années et la cartographie de la trame verte (espaces écologiques) dans le but d'étudier l'impact du choix du capteur sur la détection de ces élémentsAs acquisition technology progresses, remote sensing data contains an ever increasing amount of information. Future projects in remote sensing like Copernicus will give a high temporal repeatability of acquisitions and will cover large geographical areas. As part of the Copernicus project, Sentinel-2 combines a large swath, frequent revisit (5 days), and systematic acquisition of all land surfaces at high-spatial resolution and with a large number of spectral bands.The context of my research activities has involved the automation and improvement of classification processes for land use and land cover mapping in application with new satellite characteristics. This research has been focused on four main axes: selection of the input data for the classification processes, improvement of classification systems with introduction of ancillary data, fusion of multi-sensors, multi-temporal and multi-spectral classification image results and classification without ground truth data. These new methodologies have been validated on a wide range of images available: various sensors (optical: Landsat 5/7, Worldview-2, Formosat-2, Spot 2/4/5, Pleiades; and radar: Radarsat, Terrasar-X), various spatial resolutions (30 meters to 0.5 meters), various time repeatability (up to 46 images per year) and various geographical areas (agricultural area in Toulouse, France, Pyrenean mountains and arid areas in Morocco and Algeria). These methodologies are applicable to a wide range of thematic applications like Land Cover mapping, carbon flux estimation and greenbelt mappin
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