Vehicle Type Recognition in Sensor Networks Using Improved Time Encoded Signal Processing Algorithm

Vehicle type recognition is a demanding application of wireless sensor networks (WSN). In many cases, sensor nodes detect and recognize vehicles from their acoustic or seismic signals using wavelet based or spectral feature extraction methods. Such methods, while providing convincing results, are quite demanding in computational power and energy and are difficult to implement on low-cost sensor nodes with limitation resources. In this paper, we investigate the use of time encoded signal processing (TESP) algorithm for vehicle type recognition. The conventional TESP algorithm, which is effective for the speech signal feature extraction, however, is not suitable for the vehicle sound signal which is more complex. To solve this problem, an improved time encoded signal processing (ITESP) is proposed as the feature extraction method according to the characteristics of the vehicle sound signal. Recognition procedure is accomplished using the support vector machine (SVM) and the k-nearest neighbor (KNN) classifier. The experimental results indicate that the vehicle type recognition system with ITESP features give much better performance compared with the conventional TESP based features

Dual-band polarimetric HRRP recognition via a brain-inspired multi-channel fusion feature extraction network

Radar high-resolution range profile (HRRP) provides geometric and structural information of target, which is important for radar automatic target recognition (RATR). However, due to the limited information dimension of HRRP, achieving accurate target recognition is challenging in applications. In recent years, with the rapid development of radar components and signal processing technology, the acquisition and use of target multi-frequency and polarization scattering information has become a significant way to improve target recognition performance. Meanwhile, deep learning inspired by the human brain has shown great promise in pattern recognition applications. In this paper, a Multi-channel Fusion Feature Extraction Network (MFFE-Net) inspired by the human brain is proposed for dual-band polarimetric HRRP, aiming at addressing the challenges faced in HRRP target recognition. In the proposed network, inspired by the human brain’s multi-dimensional information interaction, the similarity and difference features of dual-frequency HRRP are first extracted to realize the interactive fusion of frequency features. Then, inspired by the human brain’s selective attention mechanism, the interactive weights are obtained for multi-polarization features and multi-scale representation, enabling feature aggregation and multi-scale fusion. Finally, inspired by the human brain’s hierarchical learning mechanism, the layer-by-layer feature extraction and fusion with residual connections are designed to enhance the separability of features. Experiments on simulated and measured datasets verify the accurate recognition capability of MFFE-Net, and ablative studies are conducted to confirm the effectiveness of components of network for recognition

Levee Slide Detection using Synthetic Aperture Radar Magnitude and Phase

The objectives of this research are to support the development of state-of-the-art methods using remotely sensed data to detect slides or anomalies in an efficient and cost-effective manner based on the use of SAR technology. Slough or slump slides are slope failures along a levee, which leave areas of the levee vulnerable to seepage and failure during high water events. This work investigates the facility of detecting the slough slides on an earthen levee with different types of polarimetric Synthetic Aperture Radar (polSAR) imagery. The source SAR imagery is fully quad-polarimetric L-band data from the NASA Jet Propulsion Laboratory’s (JPL’s) Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR). The study area encompasses a portion of the levees of the lower Mississippi river, located in Mississippi, United States. The obtained classification results reveal that the polSAR data unsupervised classification with features extraction produces more appropriate results than the unsupervised classification with no features extraction. Obviously, supervised classification methods provide better classification results compared to the unsupervised methods. The anomaly identification is good with these results and was improved with the use of a majority filter. The classification accuracy is further improved with a morphology filter. The classification accuracy is significantly improved with the use of GLCM features. The classification results obtained for all three cases (magnitude, phase, and complex data), with classification accuracies for the complex data being higher, indicate that the use of synthetic aperture radar in combination with remote sensing imagery can effectively detect anomalies or slides on an earthen levee. For all the three samples it consistently shows that the accuracies for the complex data are higher when compared to those from the magnitude and phase data alone. The tests comparing complex data features to magnitude and phase data alone, and full complex data, and use of post-processing filter, all had very high accuracy. Hence we included more test samples to validate and distinguish results

Classifying multisensor remote sensing data : Concepts, Algorithms and Applications

Today, a large quantity of the Earth’s land surface has been affected by human induced land cover changes. Detailed knowledge of the land cover is elementary for several decision support and monitoring systems. Earth-observation (EO) systems have the potential to frequently provide information on land cover. Thus many land cover classifications are performed based on remotely sensed EO data. In this context, it has been shown that the performance of remote sensing applications is further improved by multisensor data sets, such as combinations of synthetic aperture radar (SAR) and multispectral imagery. The two systems operate in different wavelength domains and therefore provide different yet complementary information on land cover. Considering the increase in revisit times and better spatial resolutions of recent and upcoming systems like TerraSAR-X (11 days; up to1 m), Radarsat-2 (24 days; up to 3 m), or RapidEye constellation (up to 1 day; 5 m), multisensor approaches become even more promising. However, these data sets with high spatial and temporal resolution might become very large and complex. Commonly used statistical pattern recognition methods are usually not appropriate for the classification of multisensor data sets. Hence, one of the greatest challenges in remote sensing might be the development of adequate concepts for classifying multisensor imagery. The presented study aims at an adequate classification of multisensor data sets, including SAR data and multispectral images. Different conventional classifiers and recent developments are used, such as support vector machines (SVM) and random forests (RF), which are well known in the field of machine learning and pattern recognition. Furthermore, the impact of image segmentation on the classification accuracy is investigated and the value of a multilevel concept is discussed. To increase the performance of the algorithms in terms of classification accuracy, the concept of SVM is modified and combined with RF for optimized decision making. The results clearly demonstrate that the use of multisensor imagery is worthwhile. Irrespective of the classification method used, classification accuracies increase by combining SAR and multispectral imagery. Nevertheless, SVM and RF are more adequate for classifying multisensor data sets and significantly outperform conventional classifier algorithms in terms of accuracy. The finally introduced multisensor-multilevel classification strategy, which is based on the sequential use of SVM and RF, outperforms all other approaches. The proposed concept achieves an accuracy of 84.9%. This is significantly higher than all single-source results and also better than those achieved on any other combination of data. Both aspects, i.e. the fusion of SAR and multispectral data as well as the integration of multiple segmentation scales, improve the results. Contrary to the high accuracy value by the proposed concept, the pixel-based classification on single-source data sets achieves a maximal accuracy of 65% (SAR) and 69.8% (multispectral) respectively. The findings and good performance of the presented strategy are underlined by the successful application of the approach to data sets from a second year. Based on the results from this work it can be concluded that the suggested strategy is particularly interesting with regard to recent and future satellite missions

Cartographie par radar à synthèse d'ouverture pour l'analyse de la variabilité spatiale du pied de glace pour la région de la péninsule de Manicouagan, Québec

RÉSUMÉ: Posséder des connaissances sur la climatologie et être en mesure de cartographier le pied de glace s’impose de plus en plus comme un élément incontournable pour s'attaquer au problème de l'érosion côtière dans l'estuaire du fleuve Saint-Laurent (Québec, Canada). Le complexe glaciel côtier joue deux rôles fondamentaux : d’une part, prévenir l'érosion en réduisant le transport sédimentaire induit par la dérive littorale et, d’autre part, être un agent d'érosion en incorporant du sédiment dans sa structure pendant la phase d'englacement et en le transportant ensuite au large lors du déglacement printanier. Pour l'instant, les estimations du bilan sédimentaire associées à la dynamique glacielle sont imprécises. Par ailleurs, dans un contexte de changements climatiques susceptibles d’abréger la période d'englacement des rives en milieux nordiques, il s’impose de se doter d’outils de collecte d’information sur les caractéristiques du pied de glace, afin de mieux équiper les gestionnaires du territoire pour affronter les défis qui s'annoncent. Avec les systèmes radar à synthèse d’ouverture (RSO) performant actuellement en opération, des applications comme la cartographie de structures de glace telles que le pied de glace deviennent potentiellement réalisables malgré les défis que cela présente. Pour atteindre l’objectif de cartographier le pied de glace, des images RADARSAT-2 couvrant la péninsule de Manicouagan ont été acquises en mode multipolarimétrique (HH, HV, VH, VV) à huit mètres de résolution. Les résultats obtenus à partir de différentes techniques de traitement d’images polarimétriques ont été comparés. Afin d'évaluer la validité et l'interprétation des résultats des diverses méthodes de cartographie, une campagne de terrain a été menée pendant les hivers des années 2009 à 2011. Les données in situ ainsi récoltées sont les suivantes : profils de glace, levés GPS de la superficie du pied de glace, carottes de glace et images obtenues à l’aide de caméras automatiques. La méthode de cartographie issue des travaux de Ferro-Famil, Pottier et Lee (2001) a permis de cartographier le pied de glace avec un taux de succès de 69% à 79% selon le site étudié et les filtres utilisés. Les résultats ainsi obtenus ont permis la mise en oeuvre d’applications pratiques comme la mesure de la variabilité spatiale du pied de glace et l’estimation de sa charge sédimentaire. Les résultats de l’analyse de la variabilité spatiale permettent d’avancer l’hypothèse que le type de côte explique de manière significative la variabilité observée dans la concentration du PDG sur les côtes de la péninsule de Manicouagan. Ces résultats montrent aussi que la télédétection peut aider à estimer le volume du pied de glace sur les côtes du Saint-Laurent. Lorsque couplée avec des mesures in situ de la charge sédimentaires incorporé à la glace, la méthodologie présentée permet de calculer la quantité de sédiments mobilisée par la glace côtière, ce qui permet de préciser l’impact du PDG sur le bilan sédimentaire d’une cellule hydro-sédimentaire côtière. -- Mot(s) clé(s) en français : Télédétection, radar à synthèse d'ouverture, pied de glace, géomorphologie côtière. -- ABSTRACT: Canada’s coasts are dynamic systems affected by a variety of processes, one of which is coastal erosion. In the Saint-Lawrence estuary, coastal erosion is a particularly important process as it threatens a variety of infrastructure built near the shorelines. In this context, it is important to acquire knowledge about this process and the factors affecting it. More particularly, in winter, northern coasts host the near shore ice complex, which protects the shores against winter storms, but also contributes to erosion by carrying sediments offshore during spring breakup. With today’s high resolution polarimetric SAR satellites, the extraction of ice structures as narrow as the strip of ice that forms the icefoot becomes potentially feasible. This study describes a method which was developed to map the nearshore ice complex and the icefoot using RADARSAT-2 fully polarimetric data. Classifications using the Ferro-Famil, Pottier and Lee (2001) methodology were tested and the results were compared against reference dataset that were acquired through field campaigns during the 2010 and 2011 winters. Accuracy results for the icefoot classification range from 69% to 79%. The spatial variability of the icefoot was analysed using the developed icefoot mapping methodology. Results indicate that during the onset and the breakup period, the spatial variability of the icefoot can be partly explained by the coast type it is set on. When combined with Lidar observations of the beach topography, this methodology can also obtain the icefoot volume. This measurement method could help improve our understanding of the impact of the icefoot on the coastal erosion process. -- Mot(s) clé(s) en anglais : Remote sensing, synthetic aperture radar, icefoot, coastal geomorphology

L’utilisation de la polarimétrie radar et de la décomposition de Touzi pour la caractérisation et la classification des physionomies végétales des milieux humides : le cas du Lac Saint-Pierre

Les milieux humides remplissent plusieurs fonctions écologiques d’importance et contribuent à la biodiversité de la faune et de la flore. Même s’il existe une reconnaissance croissante sur l’importante de protéger ces milieux, il n’en demeure pas moins que leur intégrité est encore menacée par la pression des activités humaines. L’inventaire et le suivi systématique des milieux humides constituent une nécessité et la télédétection est le seul moyen réaliste d’atteindre ce but. L’objectif de cette thèse consiste à contribuer et à améliorer la caractérisation des milieux humides en utilisant des données satellites acquises par des radars polarimétriques en bande L (ALOS-PALSAR) et C (RADARSAT-2). Cette thèse se fonde sur deux hypothèses (chap. 1). La première hypothèse stipule que les classes de physionomies végétales, basées sur la structure des végétaux, sont plus appropriées que les classes d’espèces végétales car mieux adaptées au contenu informationnel des images radar polarimétriques. La seconde hypothèse stipule que les algorithmes de décompositions polarimétriques permettent une extraction optimale de l’information polarimétrique comparativement à une approche multipolarisée basée sur les canaux de polarisation HH, HV et VV (chap. 3). En particulier, l’apport de la décomposition incohérente de Touzi pour l’inventaire et le suivi de milieux humides est examiné en détail. Cette décomposition permet de caractériser le type de diffusion, la phase, l’orientation, la symétrie, le degré de polarisation et la puissance rétrodiffusée d’une cible à l’aide d’une série de paramètres extraits d’une analyse des vecteurs et des valeurs propres de la matrice de cohérence. La région du lac Saint-Pierre a été sélectionnée comme site d’étude étant donné la grande diversité de ses milieux humides qui y couvrent plus de 20 000 ha. L’un des défis posés par cette thèse consiste au fait qu’il n’existe pas de système standard énumérant l’ensemble possible des classes physionomiques ni d’indications précises quant à leurs caractéristiques et dimensions. Une grande attention a donc été portée à la création de ces classes par recoupement de sources de données diverses et plus de 50 espèces végétales ont été regroupées en 9 classes physionomiques (chap. 7, 8 et 9). Plusieurs analyses sont proposées pour valider les hypothèses de cette thèse (chap. 9). Des analyses de sensibilité par diffusiogramme sont utilisées pour étudier les caractéristiques et la dispersion des physionomies végétales dans différents espaces constitués de paramètres polarimétriques ou canaux de polarisation (chap. 10 et 12). Des séries temporelles d’images RADARSAT-2 sont utilisées pour approfondir la compréhension de l’évolution saisonnière des physionomies végétales (chap. 12). L’algorithme de la divergence transformée est utilisé pour quantifier la séparabilité entre les classes physionomiques et pour identifier le ou les paramètres ayant le plus contribué(s) à leur séparabilité (chap. 11 et 13). Des classifications sont aussi proposées et les résultats comparés à une carte existante des milieux humide du lac Saint-Pierre (14). Finalement, une analyse du potentiel des paramètres polarimétrique en bande C et L est proposé pour le suivi de l’hydrologie des tourbières (chap. 15 et 16). Les analyses de sensibilité montrent que les paramètres de la 1re composante, relatifs à la portion dominante (polarisée) du signal, sont suffisants pour une caractérisation générale des physionomies végétales. Les paramètres des 2e et 3e composantes sont cependant nécessaires pour obtenir de meilleures séparabilités entre les classes (chap. 11 et 13) et une meilleure discrimination entre milieux humides et milieux secs (chap. 14). Cette thèse montre qu’il est préférable de considérer individuellement les paramètres des 1re, 2e et 3e composantes plutôt que leur somme pondérée par leurs valeurs propres respectives (chap. 10 et 12). Cette thèse examine également la complémentarité entre les paramètres de structure et ceux relatifs à la puissance rétrodiffusée, souvent ignorée et normalisée par la plupart des décompositions polarimétriques. La dimension temporelle (saisonnière) est essentielle pour la caractérisation et la classification des physionomies végétales (chap. 12, 13 et 14). Des images acquises au printemps (avril et mai) sont nécessaires pour discriminer les milieux secs des milieux humides alors que des images acquises en été (juillet et août) sont nécessaires pour raffiner la classification des physionomies végétales. Un arbre hiérarchique de classification développé dans cette thèse constitue une synthèse des connaissances acquises (chap. 14). À l’aide d’un nombre relativement réduit de paramètres polarimétriques et de règles de décisions simples, il est possible d’identifier, entre autres, trois classes de bas marais et de discriminer avec succès les hauts marais herbacés des autres classes physionomiques sans avoir recours à des sources de données auxiliaires. Les résultats obtenus sont comparables à ceux provenant d’une classification supervisée utilisant deux images Landsat-5 avec une exactitude globale de 77.3% et 79.0% respectivement. Diverses classifications utilisant la machine à vecteurs de support (SVM) permettent de reproduire les résultats obtenus avec l’arbre hiérarchique de classification. L’exploitation d’une plus forte dimensionalitée par le SVM, avec une précision globale maximale de 79.1%, ne permet cependant pas d’obtenir des résultats significativement meilleurs. Finalement, la phase de la décomposition de Touzi apparaît être le seul paramètre (en bande L) sensible aux variations du niveau d’eau sous la surface des tourbières ouvertes (chap. 16). Ce paramètre offre donc un grand potentiel pour le suivi de l’hydrologie des tourbières comparativement à la différence de phase entre les canaux HH et VV. Cette thèse démontre que les paramètres de la décomposition de Touzi permettent une meilleure caractérisation, de meilleures séparabilités et de meilleures classifications des physionomies végétales des milieux humides que les canaux de polarisation HH, HV et VV. Le regroupement des espèces végétales en classes physionomiques est un concept valable. Mais certaines espèces végétales partageant une physionomie similaire, mais occupant un milieu différent (haut vs bas marais), ont cependant présenté des différences significatives quant aux propriétés de leur rétrodiffusion.Wetlands fill many important ecological functions and contribute to the biodiversity of fauna and flora. Although there is a growing recognition of the importance to protect these areas, it remains that their integrity is still threatened by the pressure of human activities. The inventory and the systematic monitoring of wetlands are a necessity and remote sensing is the only realistic way to achieve this goal. The primary objective of this thesis is to contribute and improve the wetland characterization using satellite polarimetric data acquired in L (ALOS-PALSAR) and C (RADARSAT-2) band. This thesis is based on two hypotheses (Ch. 1). The first hypothesis stipulate that classes of plant physiognomies, based on plant structure, are more appropriate than classes of plant species because they are best adapted to the information content of polarimetric radar data. The second hypothesis states that polarimetric decomposition algorithms allow an optimal extraction of polarimetric information compared to a multi-polarized approach based on the HH, HV and VV channels (Ch. 3). In particular, the contribution of the incoherent Touzi decomposition for the inventory and monitoring of wetlands is examined in detail. This decomposition allows the characterization of the scattering type, its phase, orientation, symmetry, degree of polarization and the backscattered power of a target with a series of parameters extracted from an analysis of the coherency matrix eigenvectors and eigenvalues. The lake Saint-Pierre region was chosen as the study site because of the great diversity of its wetlands that are covering more than 20 000 ha. One of the challenges posed by this thesis is that there is neither a standard system enumerating all the possible physiognomic classes nor an accurate description of their characteristics and dimensions. Special attention was given to the creation of these classes by combining several data sources and more than 50 plant species were grouped into nine physiognomic classes (Ch. 7, 8 and 9). Several analyzes are proposed to validate the hypotheses of this thesis (Ch. 9). Sensitivity analysis using scatter plots are performs to study the characteristics and dispersion of plant physiognomic classes in various features space consisting of polarimetric parameters or polarization channels (Ch. 10 and 12). Time series of made of RADARSAT-2 images are used to deepen the understanding of the seasonal evolution of plant physiognomies (Ch. 12). The transformed divergence algorithm is used to quantify the separability between physiognomic classes and to identify the parameters (s) that contribute the most to their separability (Ch. 11 and 13). Classifications are also proposed and the results compared to an existing map of the lake Saint-Pierre wetlands (Ch. 14). Finally, an analysis of the potential of polarimetric parameters in C and L-band is proposed for the monitoring of peatlands hydrology (Ch. 15 and 16). Sensitivity analyses show that the parameters of the 1st component, relative to the dominant (polarized) part of the signal, are sufficient for a general characterization of plant physiognomies. The parameters of the second and third components are, however, needed for better class separability (Ch. 11 and 13) and a better discrimination between wetlands and uplands (Ch. 14). This thesis shows that it is preferable to consider individually the parameters of the 1st, 2nd and 3rd components rather than their weighted sum by their respective eigenvalues (Ch. 10 and 12). This thesis also examines the complementarity between the structural parameters and those related to the backscattered power, often ignored and normalized by most polarimetric decomposition. The temporal (seasonal) dimension is essential for the characterization and classification of plant physiognomies (Ch. 12, 13 and 14). Images acquired in spring (April and May) are needed to discriminate between upland and wetlands while images acquired in summer (July and August) are needed to refine the classifications of plant physiognomies. A hierarchical classification tree developed in this thesis represents a synthesis of the acquired knowledge (Chapter 14). Using a relatively small number of polarimetric parameters and simple decision rules, it is possible to identify, among other, three low marshes classes and to discriminate with success herbaceous high marshes from other physiognomic classes without using ancillary data source. The results obtained are comparable to those from a supervised classification using two Landsat-5 images with an overall accuracy of 77.3% and 79.0% respectively. Various classifications using the support vector machine (SVM) can reproduce the results obtained with the hierarchical classification tree. But the possible exploitation by the SVM of a higher dimensionality, with a maximum overall accuracy of 79.1%, does not allow however to achieve significantly better results. Finally, the phase of the Touzi decomposition appears to be the only parameter (in L-band) sensitive to changes in water level beneath the peat surface (Ch. 16). Therefore, this parameter offer a great potential for peatlands hydrology monitoring compared to the HH-VV phase difference. This thesis demonstrates that the Touzi decomposition parameters allow a better characterization, better separability and better classifications of wetlands plant physiognomic classes than HH, HV and VV polarization channels. The grouping of plant species into physiognomic classes is a valid concept. However, some plant species sharing a similar physiognomy, but occupying a different environment (high vs. low marshes), have presented significant differences in their scattering properties

Historical Land use/Land cover classification and its change detection mapping using Different Remotely Sensed Data from LANDSAT (MSS, TM and ETM+) and Terra (ASTER) sensors: a case study of the Euphrates River Basin in Syria with focus on agricultural irrigation projects

This thesis deals spatially and regionally with the natural boundaries of the Euphrates River Basin (ERB) in Syria. Scientifically, the research covers the application of remote sensing science (optical remote sensing: LANDSAT-MSS, TM, and ETM+; and TERRA: ASTER); and methodologically, in Land Use/Land Cover (LULC) classification and mapping, automatically and/or semi-automatically; in LULC-change detection; and finally in the mapping of historical irrigation and agricultural projects for the extraction of differing crop types and the estimation of their areas. With regard to time, the work is based on the years 1975, 1987, 2005 and 2007. Initially, preprocessing of the satellite data (geometric- and radiometric- processing, image enhancement, best bands composite selection, transformation, mosaicing and finally subsetting) was carried out. Then, the Land Use/Land Cover Classification System (LCCS) of the Food and Agriculture Organization (FAO) was chosen. The following steps were followed in LULC- classification and change detection mapping: visual interpretation in addition to digital image processing techniques; pixel-based classification methods; unsupervised classification: ISODATA-method; and supervised classification and multistage supervised approaches using the algorithms: Maximum Likelihood Classifier (MLC), Neural Network classifier (NN) and Support Vector Machines (SVM). These were trialed on a test area to determine the optimized classification approach/algorithm for application on the whole study area (ERB) based on the available imagery. Pre- and post- classification change detection methods (comparison approaches) were used to detect changes in land use/land cover-classes (for the years 1975, 1987 and 2007) in the study area. The remote sensing methods show a high potential in mapping historical and present land use/land cover classes and its changes over time. Significant results are also possible for agricultural crop classification in relatively large regional areas (the ERB in Syria is almost 50,335 km²). Change trends in the study area and period was characterized by land-intensive agricultural expansion. The rapid, more labor- and capital- intensive growth in the agricultural sector was enabled by the introduction of fertilizer, improved access to rural roads and markets, and the expansion of the government irrigation projects. Irrigated areas increased 148 % in the past 32 years from 249,681 ha in 1975 to 596,612 ha in 2007