The chameleon groups of Richard J. Thompson: automorphisms and dynamics

The automorphism groups of several of Thompson's countable groups of piecewise linear homeomorphisms of the line and circle are computed and it is shown that the outer automorphism groups of these groups are relatively small. These results can be interpreted as stability results for certain structures of PL functions on the circle. Machinery is developed to relate the structures on the circle to corresponding structures on the line

CHANGE DETECTION IN A TOPOGRAPHIC BUILDING DATABASE USING SUBMETRIC SATELLITE IMAGES

Submetric satellite imagery (Pleiades, GeoEye) offers advantages for map update purposes, e.g. an interesting ground resolution, a good reactivity and the ability to capture wide areas. Experiments on the use of such stereoscopic images for 2D change detection among building objects of GIS topographic database are presented in this paper. Two approaches have been tested. The first one extracts land cover from satellite ortho-images and additional information (correlation DSM-DTM, database) and compares building objects of this classification to those of the database. The second one creates a pseudo-DSM from height information of database building objects combined with a DTM and compares it to a correlation DSM computed from satellite images. Obtained results are quite encouraging even if the correctness rate remains too low for an operational use

SPECTRAL BAND SELECTION FOR URBAN MATERIAL CLASSIFICATION USING HYPERSPECTRAL LIBRARIES

In urban areas, information concerning very high resolution land cover and especially material maps are necessary for several city modelling or monitoring applications. That is to say, knowledge concerning the roofing materials or the different kinds of ground areas is required. Airborne remote sensing techniques appear to be convenient for providing such information at a large scale. However, results obtained using most traditional processing methods based on usual red-green-blue-near infrared multispectral images remain limited for such applications. A possible way to improve classification results is to enhance the imagery spectral resolution using superspectral or hyperspectral sensors. In this study, it is intended to design a superspectral sensor dedicated to urban materials classification and this work particularly focused on the selection of the optimal spectral band subsets for such sensor. First, reflectance spectral signatures of urban materials were collected from 7 spectral libraires. Then, spectral optimization was performed using this data set. The band selection workflow included two steps, optimising first the number of spectral bands using an incremental method and then examining several possible optimised band subsets using a stochastic algorithm. The same wrapper relevance criterion relying on a confidence measure of Random Forests classifier was used at both steps. To cope with the limited number of available spectra for several classes, additional synthetic spectra were generated from the collection of reference spectra: intra-class variability was simulated by multiplying reference spectra by a random coefficient. At the end, selected band subsets were evaluated considering the classification quality reached using a rbf svm classifier. It was confirmed that a limited band subset was sufficient to classify common urban materials. The important contribution of bands from the Short Wave Infra-Red (SWIR) spectral domain (1000–2400 nm) to material classification was also shown

A TWO-STEP DECISION FUSION STRATEGY: APPLICATION TO HYPERSPECTRAL AND MULTISPECTRAL IMAGES FOR URBAN CLASSIFICATION

International audienc

Extraction of optimal spectral bands using hierarchical band merging out of hyperspectral data

International audienceSpectral optimization consists in identifying the most relevant band subset for a specific application. It is a way to reduce hyperspec-tral data huge dimensionality and can be applied to design specific superspectral sensors dedicated to specific land cover applications. Spectral optimization includes both band selection and band extraction. On the one hand, band selection aims at selecting an optimal band subset (according to a relevance criterion) among the bands of a hyperspectral data set, using automatic feature selection algorithms. On the other hand, band extraction defines the most relevant spectral bands optimizing both their position along the spectrum and their width. The approach presented in this paper first builds a hierarchy of groups of adjacent bands, according to a relevance criterion to decide which adjacent bands must be merged. Then, band selection is performed at the different levels of this hierarchy. Two approaches were proposed to achieve this task : a greedy one and a new adaptation of an incremental feature selection algorithm to this hierarchy of merged bands

Large-scale classification of water areas using airborne topographic lidar data

Accurate Digital Terrain Models (DTMs) are inevitable inputs for mapping and analyzing areas subject to natural hazards. Topographic airborne laser scanning has become an established technique to characterize the Earth's surface: lidar provides 3D point clouds allowing for a fine reconstruction of the topography while preserving high frequencies of the relief. For flood hazard modeling, the key step, before going onto terrain modeling, is the discrimination of land and water areas within the delivered point clouds. Therefore, instantaneous shorelines, river banks, and inland waters can be extracted as a basis for more reliable DTM generation. This paper presents an automatic, efficient, and versatile workflow for land/water classification of airborne topographic lidar points, effective at large scales (>300 km(2)). For that purpose, the Support Vector Machine (SVM) method is used as a classification framework and it is embedded in a workflow designed for our specific goal. First, a restricted but carefully designed set of features, based only on 3D lidar point coordinates and flightline information, is defined as classifier input. Then, the SVM learning step is performed on small but well-targeted areas thanks to a semiautomatic region growing strategy. Finally, label probability output by SVM is merged with contextual knowledge during a probabilistic relaxation step in order to remove pixel-wise misclassification. Results show that a survey of hundreds of millions of points are labeled with high accuracy (>95% in most cases for coastal areas, and >90% for rivers) and that small natural and anthropic features of interest are still well classified even though we work at low point densities (0.5-4 pts/m(2)). We also noticed that it may fail in water-logged areas. Nevertheless, our approach remains valid for regional and national mapping purposes, coasts and rivers, and provides a strong basis for further discrimination of land-cover classes and coastal habitats