Une nouvelle méthode d’élagage d’ensemble de classifieurs basée sur le concept de marge

Les méthodes d’ensemble ont été utilisées avec succès comme schéma de classification. Les algorithmes d’élagage d’ensembles de classifieurs sont apparus afin de réduire la complexité de ce paradigme populaire d’apprentissage. Cet article présente une nouvelle méthode efficace d’élagage d’ensembles qui, non seulement réduit de manière significative la complexité des méthodes d’ensemble, mais permet aussi une meilleure précision de classification que la version sans élagage. Cet algorithme consiste à ordonner tous les classifieurs de base par rapport à leur entropie qui exploite une nouvelle version de la marge des méthodes d’ensemble. La confrontation de cette méthode avec l’approche naïve d’élagage aléatoire des classifieurs de base et avec un autre algorithme d’élagage par ordonnancement a permis de montrer sa supériorité à travers une analyse empirique conséquente.Ensemble methods have been successfully used as a classification scheme. The reduction of the complexity of this popular learning paradigm motivated the appearance of ensemble pruning algorithms. This paper presents a new efficient ensemble pruning method which not only highly reduces the complexity of ensemble methods but also performs better than the non-pruned version in terms of classification accuracy. This algorithm consists in ordering all the base classifiers with respect to their entropy which exploits a new version of the margin of ensemble methods. Confrontation with both the naive approach of randomly pruning base classifiers and another ordered-based pruning algorithm turned out convincing in an extensive empirical analysis

A Marked Point Process for Modeling Lidar Waveforms

International audienceLidar waveforms are 1-D signals representing a train of echoes caused by reflections at different targets. Modeling these echoes with the appropriate parametric function is useful to retrieve information about the physical characteristics of the targets. This paper presents a new probabilistic model based upon a marked point process which reconstructs the echoes from recorded discrete waveforms as a sequence of parametric curves. Such an approach allows to fit each mode of a waveform with the most suitable function and to deal with both, symmetric and asymmetric, echoes. The model takes into account a data term, which measures the coherence between the models and the waveforms, and a regularization term, which introduces prior knowledge on the reconstructed signal. The exploration of the associated configuration space is performed by a reversible jump Markov chain Monte Carlo (RJMCMC) sampler coupled with simulated annealing. Experiments with different kinds of lidar signals, especially from urban scenes, show the high potential of the proposed approach. To further demonstrate the advantages of the suggested method, actual laser scans are classified and the results are reported

A Nonparametric Approach to Segmentation of Ladar Images

The advent of advanced laser radar (ladar) systems that record full-waveform signal data has inspired numerous inquisitions which aspire to extract additional, previously unavailable, information about the illuminated scene from the collected data. The quality of the information, however, is often related to the limitations of the ladar camera used to collect the data. This research project uses full-waveform analysis of ladar signals, and basic principles of optics, to propose a new formulation for an accepted signal model. A new waveform model taking into account backscatter reflectance is the key to overcoming specific deficiencies of the ladar camera at hand, namely the ability to discern pulse-spreading effects of elongated targets. A concert of non-parametric statistics and familiar image processing methods are used to calculate the orientation angle of the illuminated objects, and the deficiency of the hardware is circumvented. Segmentation of the various ladar images performed as part of the angle estimation, and this is shown to be a new and effective strategy for analyzing the output of the AFIT ladar camera

Calibration of full-waveform airborne laser scanning data for 3D object segmentation

Phd ThesisAirborne Laser Scanning (ALS) is a fully commercial technology, which has seen rapid uptake from the photogrammetry and remote sensing community to classify surface features and enhance automatic object recognition and extraction processes. 3D object segmentation is considered as one of the major research topics in the field of laser scanning for feature recognition and object extraction applications. The demand for automatic segmentation has significantly increased with the emergence of full-waveform (FWF) ALS, which potentially offers an unlimited number of return echoes. FWF has shown potential to improve available segmentation and classification techniques through exploiting the additional physical observables which are provided alongside the standard geometric information. However, use of the FWF additional information is not recommended without prior radiometric calibration, taking into consideration all the parameters affecting the backscattered energy. The main focus of this research is to calibrate the additional information from FWF to develop the potential of point clouds for segmentation algorithms. Echo amplitude normalisation as a function of local incidence angle was identified as a particularly critical aspect, and a novel echo amplitude normalisation approach, termed the Robust Surface Normal (RSN) method, has been developed. Following the radar equation, a comprehensive radiometric calibration routine is introduced to account for all variables affecting the backscattered laser signal. Thereafter, a segmentation algorithm is developed, which utilises the raw 3D point clouds to estimate the normal for individual echoes based on the RSN method. The segmentation criterion is selected as the normal vector augmented by the calibrated backscatter signals. The developed segmentation routine aims to fully integrate FWF data to improve feature recognition and 3D object segmentation applications. The routine was tested over various feature types from two datasets with different properties to assess its potential. The results are compared to those delivered through utilizing only geometric information, without the additional FWF radiometric information, to assess performance over existing methods. The results approved the potential of the FWF additional observables to improve segmentation algorithms. The new approach was validated against manual segmentation results, revealing a successful automatic implementation and achieving an accuracy of 82%

Aplicación de técnicas de teledetección en ecosistemas macaronésicos.

The climatic and orographic conditions of Tenerife Island (Canaray Islands, Spain) have allowed the development of a rich biodiversity and different ecosystems. However, such a fragmented territory, the spectral similarity and complex spatial distribution of the species of vegetation, complicates the task of maintaining updated cartography using the traditional, costly and time-consuming fieldwork. Because of these limitations, this thesis proposed the use of remote sensing techniques from satellites and unmanned aerial vehicles in conjunction with selective field spectroradiometry, to map two important ecosystems situated in Tenerife: an agro-ecosystem dominated by chestnut trees (Castanea sativa Mill.) in the north slope of the island and a natural ecosystem (Malpaís de Güímar) in the southeast, with vegetation of beekeeping interest where the Cardonal-Tabaibal prevails. We performed thee different studies. In the first, the potential of very high- resolution (VHR) WorldView imagery and extended morphological profiles for mapping chestnut trees in the agroecosystem was analysed. Secondly, we spectrally characterized the vegetation of beekeeping interest in the Malpaís de Güímar and determined the wavelengths that best discriminated the vegetation species using a spectral separability analysis. Finally, we analysed the potential of 10 cm spatial resolution hyperspectral images to obtain cartography of the selected plant species in the Malpaís de Güímar. All remote sensing datasets (images) were classified with the Random Forest (RF) machine-learning algorithm to obtain the thematic maps of the ecosystems. RF is an adequate algorithm for mapping classes with complex characteristics, such as many of the plant species considered in this study. It demonstrated its ability to classify, with overall accuracies greater than 85%, both high-dimensional dataset (WorldView and hyperspectral images). In relation to the agro-ecosystem, two VHR WorldView images (March and May) to cover different phenological phases of chestnut trees were used. Moreover, was included spatial information in the classification process by extended morphological profiles (EMPs). The detailed accuracy assessment clearly reveals the benefits of multitemporal images in terms of mapping accuracy. The overall accuracies of the mono-temporal classifications are increased between 2% and 15%, when compared to the results achieved on the multitemporal data set. The inclusion of spatial information by EMPs further increases the classification accuracy by 5% and reduces the quantity and allocation disagreements in the final map. During years 2017 and 2018, an intense fieldwork was carried out to measure, with the ASD FieldSpec 3 spectroradiometer, the characteristic spectral signatures of the plant species with the greatest pollination potential in the Malpaís de Güímar. A spectral separability analysis, with the Jeffries-Matusita distance, made it possible to find the fourteen best spectral bands to discriminate the species. This analysis was customized for a Resonon Pika L camera that captures hyperspectral data from 400 nm to 1000 nm from a UAV. The results show that only eleven plant species show spectral separability with values higher than 1.9. The red-edge interval (705.5 nm - 757.5 nm) is particularly noteworthy. For the mapping of the Malpais de Güímar, a hyperspectral image covering approximately 6 ha was selected. Eight thematic classes were identified and defined: Aulaga, Balo, Cardón, Salado, Tabaiba amarga, Suelo, Sombra and Colmenas. From the original image of 120 spectral bands (ORIGINAL), two other reduced dimension datasets were constructed: PCA, which included the first five principal components; and SPECTRAL with the fourteen spectral bands of the previous spectral separability analysis. The ORIGINAL and PCA images showed the best results, with overall accuracies of 91.5% and 91.3%, respectively. However, no significant differences were found between them. The best-classified plant species was Cardón with a commission error of 1.1% and an omission error of 4%. In general, the thesis shows a methodology to generate accurate maps and monitor changes in the two relevant macaronesic ecosystems under study. The maps obtained in this work, increase and improve the information available so far, and could help reduce the vulnerability of these ecosystems to global climate change, strengthen their adaptation processes to prevent the loss of biological diversity and promote the development of sustainable activities to achieve their conservation