Evaluation of a Change Detection Methodology by Means of Binary Thresholding Algorithms and Informational Fusion Processes

Landcover is subject to continuous changes on a wide variety of temporal and spatial scales. Those changes produce significant effects in human and natural activities. Maintaining an updated spatial database with the occurred changes allows a better monitoring of the Earth’s resources and management of the environment. Change detection (CD) techniques using images from different sensors, such as satellite imagery, aerial photographs, etc., have proven to be suitable and secure data sources from which updated information can be extracted efficiently, so that changes can also be inventoried and monitored. In this paper, a multisource CD methodology for multiresolution datasets is applied. First, different change indices are processed, then different thresholding algorithms for change/no_change are applied to these indices in order to better estimate the statistical parameters of these categories, finally the indices are integrated into a change detection multisource fusion process, which allows generating a single CD result from several combination of indices. This methodology has been applied to datasets with different spectral and spatial resolution properties. Then, the obtained results are evaluated by means of a quality control analysis, as well as with complementary graphical representations. The suggested methodology has also been proved efficiently for identifying the change detection index with the higher contribution

A Multistage Change detection methodology applying statistical multisource

Assessment and detection of environmental changes is one the most frequent applications in remote sensing. As a result, there has been a great proliferation of research on this particular topic, leading to different methodologies for detecting changes (Radke 2005, Foody 2009, Kennedy 2009) from data supplied by multitemporal images acquired from spaceborne sensors. The basic objective in a change detection process is to detect groups of pixels that are "significantly different" within a set of registered images of the same geographic area. Moreover it must be taken into account that in the recent decades, advances in space technologies made possible to collect a large amount of information about the Earth Surface and its environment. Since these data have been acquired from multiple sources, their quantitative exploitation requires optimal strategies to benefit from their interactions, so that information of high quality and great applicability for the proposed objectives can be extracted. (Petit 2001

Evidential Clustering: A Review

International audienceIn evidential clustering, uncertainty about the assignment of objects to clusters is represented by Dempster-Shafer mass functions. The resulting clustering structure, called a credal partition, is shown to be more general than hard, fuzzy, possibilistic and rough partitions, which are recovered as special cases. Three algorithms to generate a credal partition are reviewed. Each of these algorithms is shown to implement a decision-directed clustering strategy. Their relative merits are discussed

Traffic Scene Perception for Automated Driving with Top-View Grid Maps

Ein automatisiertes Fahrzeug muss sichere, sinnvolle und schnelle Entscheidungen auf Basis seiner Umgebung treffen. Dies benötigt ein genaues und recheneffizientes Modell der Verkehrsumgebung. Mit diesem Umfeldmodell sollen Messungen verschiedener Sensoren fusioniert, gefiltert und nachfolgenden Teilsysteme als kompakte, aber aussagekräftige Information bereitgestellt werden. Diese Arbeit befasst sich mit der Modellierung der Verkehrsszene auf Basis von Top-View Grid Maps. Im Vergleich zu anderen Umfeldmodellen ermöglichen sie eine frühe Fusion von Distanzmessungen aus verschiedenen Quellen mit geringem Rechenaufwand sowie eine explizite Modellierung von Freiraum. Nach der Vorstellung eines Verfahrens zur Bodenoberflächenschätzung, das die Grundlage der Top-View Modellierung darstellt, werden Methoden zur Belegungs- und Elevationskartierung für Grid Maps auf Basis von mehreren, verrauschten, teilweise widersprüchlichen oder fehlenden Distanzmessungen behandelt. Auf der resultierenden, sensorunabhängigen Repräsentation werden anschließend Modelle zur Detektion von Verkehrsteilnehmern sowie zur Schätzung von Szenenfluss, Odometrie und Tracking-Merkmalen untersucht. Untersuchungen auf öffentlich verfügbaren Datensätzen und einem Realfahrzeug zeigen, dass Top-View Grid Maps durch on-board LiDAR Sensorik geschätzt und verlässlich sicherheitskritische Umgebungsinformationen wie Beobachtbarkeit und Befahrbarkeit abgeleitet werden können. Schließlich werden Verkehrsteilnehmer als orientierte Bounding Boxen mit semantischen Klassen, Geschwindigkeiten und Tracking-Merkmalen aus einem gemeinsamen Modell zur Objektdetektion und Flussschätzung auf Basis der Top-View Grid Maps bestimmt

Advances and Applications of Dezert-Smarandache Theory (DSmT) for Information Fusion (Collected Works), Vol. 4

The fourth volume on Advances and Applications of Dezert-Smarandache Theory (DSmT) for information fusion collects theoretical and applied contributions of researchers working in different fields of applications and in mathematics. The contributions (see List of Articles published in this book, at the end of the volume) have been published or presented after disseminating the third volume (2009, http://fs.unm.edu/DSmT-book3.pdf) in international conferences, seminars, workshops and journals. First Part of this book presents the theoretical advancement of DSmT, dealing with Belief functions, conditioning and deconditioning, Analytic Hierarchy Process, Decision Making, Multi-Criteria, evidence theory, combination rule, evidence distance, conflicting belief, sources of evidences with different importance and reliabilities, importance of sources, pignistic probability transformation, Qualitative reasoning under uncertainty, Imprecise belief structures, 2-Tuple linguistic label, Electre Tri Method, hierarchical proportional redistribution, basic belief assignment, subjective probability measure, Smarandache codification, neutrosophic logic, Evidence theory, outranking methods, Dempster-Shafer Theory, Bayes fusion rule, frequentist probability, mean square error, controlling factor, optimal assignment solution, data association, Transferable Belief Model, and others. More applications of DSmT have emerged in the past years since the apparition of the third book of DSmT 2009. Subsequently, the second part of this volume is about applications of DSmT in correlation with Electronic Support Measures, belief function, sensor networks, Ground Moving Target and Multiple target tracking, Vehicle-Born Improvised Explosive Device, Belief Interacting Multiple Model filter, seismic and acoustic sensor, Support Vector Machines, Alarm classification, ability of human visual system, Uncertainty Representation and Reasoning Evaluation Framework, Threat Assessment, Handwritten Signature Verification, Automatic Aircraft Recognition, Dynamic Data-Driven Application System, adjustment of secure communication trust analysis, and so on. Finally, the third part presents a List of References related with DSmT published or presented along the years since its inception in 2004, chronologically ordered

Evaluation of a Change Detection Methodology by Means of Binary Thresholding Algorithms and Informational Fusion Processes

Landcover is subject to continuous changes on a wide variety of temporal and spatial scales. Those changes produce significant effects in human and natural activities. Maintaining an updated spatial database with the occurred changes allows a better monitoring of the Earth’s resources and management of the environment. Change detection (CD) techniques using images from different sensors, such as satellite imagery, aerial photographs, etc., have proven to be suitable and secure data sources from which updated information can be extracted efficiently, so that changes can also be inventoried and monitored. In this paper, a multisource CD methodology for multiresolution datasets is applied. First, different change indices are processed, then different thresholding algorithms for change/no_change are applied to these indices in order to better estimate the statistical parameters of these categories, finally the indices are integrated into a change detection multisource fusion process, which allows generating a single CD result from several combination of indices. This methodology has been applied to datasets with different spectral and spatial resolution properties. Then, the obtained results are evaluated by means of a quality control analysis, as well as with complementary graphical representations. The suggested methodology has also been proved efficiently for identifying the change detection index with the higher contribution

Flood risk assessment using multi-sensor remote sensing, geographic information system, 2D hydraulic and machine learning based models

University of Technology Sydney. Faculty of Engineering and Information Technology.Flooding events threaten the population, economy and environment worldwide. In recent years, several spatial methods have been developed to map flood susceptibility, hazard and risk for predicting and modelling flooding events. However, this research proposes multiple state-of-the-art approaches to assess, simulate and forecast flooding from recent satellite imagery. Firstly, a model was proposed to monitor changes in surface runoff and forecast future surface runoff on the basis of land use/land cover (LULC) and precipitation factors because the effects of precipitation and LULC dynamics have directly affected surface runoff and flooding events. Land transformation model (LTM) was used to detect the LULC changes. Moreover, an autoregressive integrated moving average (ARIMA) model was applied to analyse and forecast rainfall trends. The parameters of the ARIMA time series model were calibrated and fitted statistically to minimise prediction uncertainty through modern Taguchi method. Then, a GIS -based soil conservation service-curve number (SCS-CN) model was developed to simulate the maximum probable surface runoff. Results showed that deforestation and urbanisation have occurred upon a given time and have been predicted to increase. Furthermore, given negative changes in LULC, surface runoff increased and was forecasted to exceed gradually by 2020. In accordance with the implemented model calibration and accuracy assessment, the GIS-based SCS-CN combined with the LTM and ARIMA model is an efficient and accurate approach to detecting, monitoring and forecasting surface runoff. Secondly, a physical vulnerability assessment of flood was conducted by extracting detailed urban features from Worldview-3. Panchromatic sharpening in conjunction with atmospheric and topographic corrections was initially implemented to increase spatial resolution and reduce atmospheric distortion from satellite images. Dempster–Shafer (DS) fusion classifier was proposed in this part as a feature-based image analysis (FBIA) to extract urban complex objects. The DS-FBIA was investigated among two sites to examine the transferability of the proposed method. In addition, the DS-FBIA was compared with other common image analysis approaches (pixel- and object-based image analyses) to discover its accuracy and computational operating time. k-nearest neighbour, Bayes and support vector machine (SVM) classifiers were tested as pixel-based image analysis approaches, while decision tree classifier was examined as an object-based image analysis approach. The results showed improvements in detailed urban extraction obtained using the proposed FBIA with 92.2% overall accuracy and with high transferability from one site to another. Thirdly, an integrated model was developed for probability analysis of different types of flood using fully distributed GIS-based algorithms. These methods were applicable, particularly where annual monsoon rains trigger fluvial floods (FF) with pluvial flash flood (PFF) events occur simultaneously. A hydraulic 2D high-resolution sub-grid model of hydrologic engineering centre river analysis system was performed to simulate FF probability and hazard. Moreover, machine learning random forest (RF) method was used to model PFF probability and hazard. The RF was optimised by particle swarm optimisation (PSO) algorithm. Both models were verified and calibrated by cross validation and sensitivity analysis to create a coupled PFF– FF probability mapping. The results showed high accuracy in generating a coupled PFF–FF probability model that can discover the impact and contribution of each type to urban flood hazard. Furthermore, the results provided detailed flood information for urban managers to equip infrastructures, such as highways, roads and sewage network, actively. Fourthly, the risk of a flood can be assessed through different stages of flood probability, hazard and vulnerability. A total of 13 flood conditioning parameters were created to construct a geospatial database for flood probability estimation in two study areas. To estimate flood probability, five approaches, namely, logistic regression, frequency ratio (FR), SVM, analytical hierarchy process and combined FR–SVM, were adopted. Then, a flood risk map was generated by integrating flood hazard and vulnerability. The accuracy of flood probability indices indicated that the combined FR–SVM method achieved the highest accuracy among the other approaches. The reliability of the results obtained from this research was also verified in the field. The most effective parameters that would trigger flood occurrence were rainfall and flood inundation depth. In this research, transferable residency from one study area to another was verified through all the implemented methods. Therefore, the proposed approaches would be effectively and easily replicated in other regions with a similar climate condition, that condition that is, having a sufficient amount of flooding inventory events. Moreover, the results of the proposed approaches provided solid-detailed information that would be used for making favourable decisions to reduce and control future flood risks