6 research outputs found
Spectral and spatial methods for the classification of urban remote sensing data
Lors de ces travaux, nous nous sommes intéressés au problème de la classification supervisée d'images satellitaires de
zones urbaines. Les données traitées sont des images optiques à très hautes résolutions spatiales: données panchromatiques à très haute résolution spatiale (IKONOS, QUICKBIRD, simulations PLEIADES) et des images hyperspectrales (DAIS, ROSIS).
Deux stratégies ont été proposées.
La première stratégie consiste en une phase d'extraction de caractéristiques spatiales et spectrales suivie d'une phase de classification. Ces caractéristiques sont extraites par filtrages morphologiques : ouvertures et fermetures géodésiques et filtrages surfaciques auto-complémentaires. La classification est réalisée avec les machines à vecteurs supports (SVM)
non linéaires. Nous proposons la définition d'un noyau spatio-spectral utilisant de manière conjointe l'information spatiale
et l'information spectrale extraites lors de la première phase.
La seconde stratégie consiste en une phase de fusion de données pre- ou post-classification. Lors de la fusion postclassification,
divers classifieurs sont appliqués, éventuellement sur plusieurs données issues d'une même scène (image panchromat
ique, image multi-spectrale). Pour chaque pixel, l'appartenance à chaque classe est estimée à l'aide des classifieurs. Un schéma de fusion adaptatif permettant d'utiliser l'information sur la fiabilité locale de chaque classifieur, mais aussi l'information globale disponible a priori sur les performances de chaque algorithme pour les différentes classes, est proposé.
Les différents résultats sont fusionnés à l'aide d'opérateurs flous.
Les méthodes ont été validées sur des images réelles. Des
améliorations significatives sont obtenues par rapport aux méthodes publiées dans la litterature
Multiple Spectral-Spatial Classification Approach for Hyperspectral Data
A .new multiple classifier approach for spectral-spatial classification of hyperspectral images is proposed. Several classifiers are used independently to classify an image. For every pixel, if all the classifiers have assigned this pixel to the same class, the pixel is kept as a marker, i.e., a seed of the spatial region, with the corresponding class label. We propose to use spectral-spatial classifiers at the preliminary step of the marker selection procedure, each of them combining the results of a pixel-wise classification and a segmentation map. Different segmentation methods based on dissimilar principles lead to different classification results. Furthermore, a minimum spanning forest is built, where each tree is rooted on a classification -driven marker and forms a region in the spectral -spatial classification: map. Experimental results are presented for two hyperspectral airborne images. The proposed method significantly improves classification accuracies, when compared to previously proposed classification techniques
Analysis of high dimensional remote sensing images using sparse learning models
In the current context of sustainable development, the study of the state and evolution of landscapes is a major challenge to understand and solve environmental problems. The research on the relationship between ecological processes and spatial patterns aims to understand how the configuration and composition of the landscape affect biodiversity. To address these questions, it is necessary to have a detailed mapping of the elements that make up the landscape. "Detailed" means that it is important to get a map at several levels : for a forest, it is necessary to map the tree species and dead timber on the ground. Another example can be given for the meadows (see figure 1), where the type (permanent, temporary ...) and the composition are spatial variables to extract.../..
Classification d’images hyperspectrales pour la caractérisation du milieu urbain par une approche multirésolution
Projecte final de carrera fet en col.laboració amb Ecole Nationale Supérieure d'Electronique et de
Radioélectricité de Grenoble i Télécom - ENSIMAGThe classification of optical urban remote‐sensing images is addressed. Support Vector
Machines (SVM) are proposed to classify hyperspectral images. An introduction to SVM is
given in this report in order to help understand how they classify data according to the spectral
information. Some kernel functions which are used to improve classification accuracy are
presented as well. Then the use of spatial information through multiresolution decomposition
is detailed.
The objective of this report is to propose a methodology including the spatial information in
the classification process and trying to evaluate and improve the accuracy of this classification.
Spatial information is extracted from a wavelet analysis of the image.
Finally experimental results are presented for each classification method: spectral, spatial and
combining both spatial and spectral, and kernel parameters are selected in order to optimize
the classification. After including the spatial information, classification accuracy has been
improved
Hyperspectral image representation and processing with binary partition trees
The optimal exploitation of the information provided by hyperspectral images requires the development of advanced image processing tools. Therefore, under the title Hyperspectral image representation and Processing with Binary Partition Trees, this PhD thesis proposes the construction and the processing of a new region-based hierarchical hyperspectral image representation: the Binary Partition Tree (BPT). This hierarchical region-based representation can be interpreted as a set of hierarchical regions stored in a tree structure. Hence, the Binary Partition Tree succeeds in presenting: (i) the decomposition of the image in terms of coherent regions and (ii) the inclusion relations of the regions in the scene. Based on region-merging techniques, the construction of BPT is investigated in this work by studying hyperspectral region models and the associated similarity metrics. As a matter of fact, the very high dimensionality and the complexity of the data require the definition of specific region models and similarity measures. Once the BPT is constructed, the fixed tree structure allows implementing efficient and advanced application-dependent techniques on it. The application-dependent processing of BPT is generally implemented through a specific pruning of the tree. Accordingly, some pruning techniques are proposed and discussed
according to different applications. This Ph.D is focused in particular on segmentation, object detection and classification of hyperspectral imagery. Experimental results on various hyperspectral data sets demonstrate the interest and the good performances of the BPT representatio