Indexation et navigation dans les contenus visuels : approches basées sur les graphes

La première partie de cette thèse concerne l’indexation des documents vidéo en scènes. Les scènes sont des ensembles de plans vidéo partageant des caractéristiques similaires. Nous proposons d’abord une méthode interactive de détection de groupes de plans, partageant un contenu couleur similaire, basé sur la fragmentation de graphe. Nous abordons ensuite l’indexation des documents vidéo en scènes de dialogue, basée sur des caractéristiques sémantiques et structurelles présentes dans l’enchaînement des plans vidéo. La seconde partie de cette thèse traite de la visualisation et de la recherche dans des collections d’images indexées. Nous présentons un algorithme de plongement d’un espace métrique dans le plan appliqué à la visualisation de collections d’images indexées. Ce type de visualisation permet de représenter les relations de dissimilarité entre images et d’identifier visuellement des groupes d’images similaires. Nous proposons enfin une interface de recherche d’images basée sur le routage local dans un graphe. Les résultats d’une validation expérimentale sont présentés et discutés.This thesis deals with the indexation and the visualisation of video documents and collections of images. The proposed methods are based on graphs to represent similarity relationships between indexed video shots and images. The first part of this thesis deals with the indexation of video documents into scenes. A scene is a set of video shots that share common features. We first propose an interactive method to group shots with similar color content using graph clustering. We then present a technique to index video documents into dialogue scenes based on semantic and structural features. The second part of this thesis deals with visualisation and search in collections of indexed images.We present an algorithm for embedding a metric space in the plane applied to collections of indexed images. The aim of this technique is to visualise the dissimilarity relationships between images to identify clusters of similar images. Finally, we present a user interface for searching images, inspired from greedy routing in networks. Results from experimental validation are presented and discussed

On the application of dynamical measures of hydrologic response to prediction and similarity assessment in watersheds

The Prediction in Ungauged Basins (PUB) initiative set out to improve the understanding of hydrological processes with an aim of improving hydrologic models for application in ungauged basins. With a majority of basins around the world essentially ungauged, this suggests the need to shift from calibration-based models that rely on observed streamflow data to models based on process understanding. This is especially important in natural infrastructure planning projects such as investments in the conservation of wetlands across the watershed, where the lack of streamflow data hinders the quantification of their benefits (such as flood attenuation), resulting in a difficulty in prioritization. This research sought to contribute to this growing body of literature by (a) developing visual tools and metrics for assessing flow dynamics and flood attenuation benefits of wetlands in relation to their position in the watershed, (b) examining distribution-based topographic metrics in regard to their efficacy in predicting hydrologic response and providing a methodology for examining other metrics in future studies, (c) building robust functional forms for two important catchment metrics: the width function and hypsometric curve, and (d) devising a hierarchical clustering approach to assess hydrological similarity and find analogous basins that is computationally efficient and has a potential for large-scale applications. Taken together, this study paves the way toward an analytical formulation of the geomorphological instantaneous unit hydrograph (GIUH) that can be used to assess the hydrological behavior in ungauged or data-scarce basins

Land/Water Interface Delineation Using Neural Networks.

The rapid decline in acreage of land areas in wetlands caused by frequent inundations and flooding has brought about an increased awareness and emphasis on the identification and inventory of land and water areas. This dissertation evaluates three classification methods--Normalized Difference Vegetation Index technique, Artificial Neural Networks, and Maximum-Likelihood classifier for the delineation of land/water interface conditions using Landsat-TM imagery. The effects of three scaling algorithms, including resampling by aggregation, Gaussian smoothing, and local variance analysis, on the classification accuracy are analyzed to determine how the delineation, quantification and analysis of land/water boundaries relate to problems of mixed pixels, scale and resolution. Bands 3, 4, and 5 of a Landsat TM image from Huntsville, Alabama were used as a multispectral data set, and ancillary data included USGS 7.5 minute Digital Line Graphs for classification accuracy assessment. The 30 m resolution multispectral imagery was used as baseline data and the images were degraded to a series of resolution levels and Gaussian smoothed through various scaling constants to simulate images of coarser resolution. Local variance was applied at each aggregation and scaling level to analyze the textural pattern. Classifications were then performed to delineate land/water interface conditions. To study effects of scale and resolution on the land/water boundaries delineated, overall percent classification accuracies, fractal analysis (area-perimeter relationships), and lacunarity analysis were applied to identify the range of spatial resolutions within which land/water boundaries were scale dependent. Results from maximum-likelihood classifier indicate that the method marginally produced higher overall accuracies than either NDVI or neural network methods. Effects from applying the three scaling algorithms indicate that overall classification accuracies decrease with coarser resolution, increase marginally with scaling constant, and vary non-linearly with local variance mask sizes. It was discovered that the application of Gaussian smoothing to neural network classifier produces very encouraging results in classifying the transition zone between land and water (mixed pixels) areas. Fractal analysis on the classified images indicates that coarser resolutions, higher scaling constants and higher degrees of complexity, wiggliness or contortion of the perimeter of water polygons span higher ranges of fractal dimension. As the water polygons become more complex, the perimeter becomes increasingly plane filling. From the changes in fractal dimension, lacunarity analysis and local variance analysis, it is observed that at 150 m, a peak value of measured index is obtained, before dropping off. This suggests that at 150 m, the aggregated water bodies shift to a different \u27characteristic\u27 scale and the water features formed are smooth, compact, have more regular boundaries and form connected regions. This scale dependence phenomenon can help to optimize efficient data resampling methodologies

Machine Learning Predicts Reach-Scale Channel Types From Coarse-Scale Geospatial Data in a Large River Basin

Hydrologic and geomorphic classifications have gained traction in response to the increasing need for basin-wide water resources management. Regardless of the selected classification scheme, an open scientific challenge is how to extend information from limited field sites to classify tens of thousands to millions of channel reaches across a basin. To address this spatial scaling challenge, this study leverages machine learning to predict reach-scale geomorphic channel types using publicly available geospatial data. A bottom-up machine learning approach selects the most accurate and stable model among∼20,000 combinations of 287 coarse geospatial predictors, preprocessing methods, and algorithms in a three-tiered framework to (i) define a tractable problem and reduce predictor noise, (ii) assess model performance in statistical learning, and (iii) assess model performance in prediction. This study also addresses key issues related to the design, interpretation, and diagnosis of machine learning models in hydrologic sciences. In an application to the Sacramento River basin (California, USA), the developed framework selects a Random Forest model to predict 10 channel types previously determined from 290 field surveys over 108,943 two hundred-meter reaches. Performance in statistical learning is reasonable with a 61% median cross-validation accuracy, a sixfold increase over the 10% accuracy of the baseline random model, and the predictions coherently capture the large-scale geomorphic organization of the landscape. Interestingly, in the study area, the persistent roughness of the topography partially controls channel types and the variation in the entropy-based predictive performance is explained by imperfect training information and scale mismatch between labels and predictors

Integrating spatial and spectral information for automatic feature identification in high -resolution remotely sensed images

This research used image objects, instead of pixels, as the basic unit of analysis in high-resolution imagery. Thus, not only spectral radiance and texture were used in the analysis, but also spatial context. Furthermore, the automated identification of attributed objects is potentially useful for integrating remote sensing with a vector-based GIS.;A study area in Morgantown, WV was chosen as a site for the development and testing of automated feature extraction methods with high-resolution data. In the first stage of the analysis, edges were identified using texture. Experiments with simulated data indicated that a linear operator identified curved and sharp edges more accurately than square shaped operators. Areas with edges that formed a closed boundary were used to delineate sub-patches. In the region growing step, the similarities of all adjacent subpatches were examined using a multivariate Hotelling T2 test that draws on the classes\u27 covariance matrices. Sub-patches that were not sufficiently dissimilar were merged to form image patches.;Patches were then classified into seven classes: Building, Road, Forest, Lawn, Shadowed Vegetation, Water, and Shadow. Six classification methods were compared: the pixel-based ISODATA and maximum likelihood approaches, field-based ECHO, and region based maximum likelihood using patch means, a divergence index, and patch probability density functions (pdfs). Classification with the divergence index showed the lowest accuracy, a kappa index of 0.254. The highest accuracy, 0.783, was obtained from classification using the patch pdf. This classification also produced a visually pleasing product, with well-delineated objects and without the distracting salt-and-pepper effect of isolated misclassified pixels. The accuracies of classification with patch mean, pixel based maximum likelihood, ISODATA and ECHO were 0.735, 0.687, 0.610, and 0.605, respectively.;Spatial context was used to generate aggregate land cover information. An Urbanized Rate Index, defined based on the percentage of Building and Road area within a local window, was used to segment the image. Five summary landcover classes were identified from the Urbanized Rate segmentation and the image object classification: High Urbanized Rate and large building sizes, Intermediate Urbanized Rate and intermediate building sizes, Low urbanized rate and small building sizes, Forest, and Water

The interpretation and characterisation of lineaments identified from Landsat TM imagery of SW England

Two Landsat TM scenes of SW England and a sub-scene of North Cornwall have been analysed visually in order to examine the effect of resolution on lineament interpretation. Images were viewed at several different scales as a result of varying image resolution whilst maintaining a fixed screen pixel size. Lineament analysis at each scale utilised GIS techniques and involved several stages: initial lineament identification and digitisation; removal of lineaments related to anthropogenic features to produce cleansed lineament maps; compilation of lineament attributes using ARC/INFO; cluster analysis for identification of lineament directional families; and line sampling of lineament maps in order to determine spacing. SW England lies within the temperate zone of Europe and the extensive agricultural cover and infrastructure conceal the underlying geology. The consequences of this for lineament analysis were examined using sub-images of North Cornwall. Here anthropogenic features are visible at all resolutions between 30m and 120m pixel sizes but lie outside the observation threshold at 150m. Having confidence that lineaments at this resolution are of non-anthropogenic origin optimises lineament identification since the image may be viewed in greater detail. On this basis, lineament analysis of SW England was performed using image resolutions of 150m. Valuable geological information below the observation threshold in 150m resolution images is likely, however, to be contained in the lineament maps produced from higher resolution images. For images analysed at higher resolutions, therefore, knowledge-based rules were established in order to cleanse the lineament populations. Compiled lineament maps were 'ground truthed' (primarily involving comparison with published geological maps but included phases of field mapping) in order to characterise their geological affinities. The major lineament trends were correlated to lithotectonic boundaries, and cross-cutting fractures sets. Major lineament trends produced distinct frequency/orientation maxima. Multiple minor geological structures, however, produced semi-overlapping groups. A clustering technique was devised to resolve overlapping groups into lineament directional families. The newly defined lineament directional families were further analysed in two ways: (i) Analysis of the spatial density of the length and frequency of lineaments indicates that individual and multiple lineament directional families vary spatially and are compartmentalised into local tectonic domains, often bounded by major lineaments. Hence, such density maps provide useful additional information about the structural framework of SW England. (ii) Lineament spacing and length of the lineament directional families were analysed for the effect of scale and geological causes on their frequency/size distributions. Spacing of fracture lineaments were found to be power-law, whereas lengths showed power-law and non-power-law distributions. Furthermore the type of frequency/size distribution for a lineament directional family can change with increasing resolution

Fundamental remote sensing science research program. Part 1: Scene radiation and atmospheric effects characterization project

Brief articles summarizing the status of research in the scene radiation and atmospheric effect characterization (SRAEC) project are presented. Research conducted within the SRAEC program is focused on the development of empirical characterizations and mathematical process models which relate the electromagnetic energy reflected or emitted from a scene to the biophysical parameters of interest