Bayesian gravitation based classification for hyperspectral images.

Integration of spectral and spatial information is extremely important for the classification of high-resolution hyperspectral images (HSIs). Gravitation describes interaction among celestial bodies which can be applied to measure similarity between data for image classification. However, gravitation is hard to combine with spatial information and rarely been applied in HSI classification. This paper proposes a Bayesian Gravitation based Classification (BGC) to integrate the spectral and spatial information of local neighbors and training samples. In the BGC method, each testing pixel is first assumed as a massive object with unit volume and a particular density, where the density is taken as the data mass in BGC. Specifically, the data mass is formulated as an exponential function of the spectral distribution of its neighbors and the spatial prior distribution of its surrounding training samples based on the Bayesian theorem. Then, a joint data gravitation model is developed as the classification measure, in which the data mass is taken to weigh the contribution of different neighbors in a local region. Four benchmark HSI datasets, i.e. the Indian Pines, Pavia University, Salinas, and Grss_dfc_2014, are tested to verify the BGC method. The experimental results are compared with that of several well-known HSI classification methods, including the support vector machines, sparse representation, and other eight state-of-the-art HSI classification methods. The BGC shows apparent superiority in the classification of high-resolution HSIs and also flexibility for HSIs with limited samples

Spectral and spatial methods for the classification of urban remote sensing data

Lors de ces travaux, nous nous sommes intéressés au problème de la classification supervisée d'images satellitaires de zones urbaines. Les données traitées sont des images optiques à très hautes résolutions spatiales: données panchromatiques à très haute résolution spatiale (IKONOS, QUICKBIRD, simulations PLEIADES) et des images hyperspectrales (DAIS, ROSIS). Deux stratégies ont été proposées. La première stratégie consiste en une phase d'extraction de caractéristiques spatiales et spectrales suivie d'une phase de classification. Ces caractéristiques sont extraites par filtrages morphologiques : ouvertures et fermetures géodésiques et filtrages surfaciques auto-complémentaires. La classification est réalisée avec les machines à vecteurs supports (SVM) non linéaires. Nous proposons la définition d'un noyau spatio-spectral utilisant de manière conjointe l'information spatiale et l'information spectrale extraites lors de la première phase. La seconde stratégie consiste en une phase de fusion de données pre- ou post-classification. Lors de la fusion postclassification, divers classifieurs sont appliqués, éventuellement sur plusieurs données issues d'une même scène (image panchromat ique, image multi-spectrale). Pour chaque pixel, l'appartenance à chaque classe est estimée à l'aide des classifieurs. Un schéma de fusion adaptatif permettant d'utiliser l'information sur la fiabilité locale de chaque classifieur, mais aussi l'information globale disponible a priori sur les performances de chaque algorithme pour les différentes classes, est proposé. Les différents résultats sont fusionnés à l'aide d'opérateurs flous. Les méthodes ont été validées sur des images réelles. Des améliorations significatives sont obtenues par rapport aux méthodes publiées dans la litterature

Landslide mapping from aerial photographs using change detection-based Markov random field

Landslide mapping (LM) is essential for hazard prevention, mitigation, and vulnerability assessment. Despite the great efforts over the past few years, there is room for improvement in its accuracy and efficiency. Existing LM is primarily achieved using field surveys or visual interpretation of remote sensing images. However, such methods are highly labor-intensive and time-consuming, particularly over large areas. Thus, in this paper a change detection-based Markov random field (CDMRF) method is proposed for near-automatic LM from aerial orthophotos. The proposed CDMRF is applied to a landslide-prone site with an area of approximately 40 km2 on Lantau Island, Hong Kong. Compared with the existing region-based level set evolution (RLSE), it has three main advantages: 1) it employs a more robust threshold method to generate the training samples; 2) it can identify landslides more accurately as it takes advantages of both the spectral and spatial contextual information of landslides; and 3) it needs little parameter tuning. Quantitative evaluation shows that it outperforms RLSE in the whole study area by almost 5.5% in Correctness and by 4% in Quality. To our knowledge, it is the first time CDMRF is used to LM from bitemporal aerial photographs. It is highly generic and has great potential for operational LM applications in large areas and also can be adapted for other sources of imagery data

Deep Learning Methods for Remote Sensing

Remote sensing is a field where important physical characteristics of an area are exacted using emitted radiation generally captured by satellite cameras, sensors onboard aerial vehicles, etc. Captured data help researchers develop solutions to sense and detect various characteristics such as forest fires, flooding, changes in urban areas, crop diseases, soil moisture, etc. The recent impressive progress in artificial intelligence (AI) and deep learning has sparked innovations in technologies, algorithms, and approaches and led to results that were unachievable until recently in multiple areas, among them remote sensing. This book consists of sixteen peer-reviewed papers covering new advances in the use of AI for remote sensing

Modeling historic, current, and available aboveground forest biomass along the Missouri River corridor

"July 2014."Dissertation Supervisor: Dr. Hong S. He.Dissertation Supervisor: Dr. Shibu Jose.Includes vita.This research presents the culmination of statistical, landscape, and geospatial analyses that examine the geographic dynamics of aboveground forest biomass (AFB) within the Missouri River corridor, Missouri USA. The Missouri River corridor is a region specifically within Missouri that encompasses 106,000 km², and is regarded as a processing region for improving the viability of Missouri's biomass/biofuel industry. Current and historic forest inventory data coupled with remote sensing, edaphic, physiographic, and climate variables were integrated into an ensemble regression tree method, Random Forest (RF), to estimate AFB, determine external driving forces of AFB, and visualize geographic locations where the greatest deviations exist between current and historic AFB values. The applicability of constructing a hybrid modeling framework using RF was initially tested in Chapter 2 by estimating current (observed data derived from Forest Inventory and Analysis) and theoretical (based on 20% of AFB found within Missouri) AFB, and calculating the percent change to determine percent changes in AFB across the landscape. The third chapter extended the RF modeling procedure to include historical information derived from General Land Office (GLO) data to estimate a baseline measure of AFB. Current AFB was again estimated and then compared to historic values where an additional synthesis was performed to investigate the top predictors of AFB. The fourth chapter examined a fuzzy logic approach for developing a suitability index based on available AFB. Available AFB was determined by applying physical constraints onto estimated AFB from the RF model, which included forest transitions and distance to rivers. The model results failed to reject our null hypothesis that there were no differences between observed and predicted AFB, and x model accuracy was very low for all AFB estimate. Results from these investigations indicated that 1) the greatest potential for increasing AFB may be along the floodplains of the Missouri anIncludes bibliographical references (pages 123-137)

Advances and Applications of DSmT for Information Fusion. Collected Works, Volume 5

This fifth volume on Advances and Applications of DSmT for Information Fusion collects theoretical and applied contributions of researchers working in different fields of applications and in mathematics, and is available in open-access. The collected contributions of this volume have either been published or presented after disseminating the fourth volume in 2015 in international conferences, seminars, workshops and journals, or they are new. The contributions of each part of this volume are chronologically ordered. First Part of this book presents some theoretical advances on DSmT, dealing mainly with modified Proportional Conflict Redistribution Rules (PCR) of combination with degree of intersection, coarsening techniques, interval calculus for PCR thanks to set inversion via interval analysis (SIVIA), rough set classifiers, canonical decomposition of dichotomous belief functions, fast PCR fusion, fast inter-criteria analysis with PCR, and improved PCR5 and PCR6 rules preserving the (quasi-)neutrality of (quasi-)vacuous belief assignment in the fusion of sources of evidence with their Matlab codes. Because more applications of DSmT have emerged in the past years since the apparition of the fourth book of DSmT in 2015, the second part of this volume is about selected applications of DSmT mainly in building change detection, object recognition, quality of data association in tracking, perception in robotics, risk assessment for torrent protection and multi-criteria decision-making, multi-modal image fusion, coarsening techniques, recommender system, levee characterization and assessment, human heading perception, trust assessment, robotics, biometrics, failure detection, GPS systems, inter-criteria analysis, group decision, human activity recognition, storm prediction, data association for autonomous vehicles, identification of maritime vessels, fusion of support vector machines (SVM), Silx-Furtif RUST code library for information fusion including PCR rules, and network for ship classification. Finally, the third part presents interesting contributions related to belief functions in general published or presented along the years since 2015. These contributions are related with decision-making under uncertainty, belief approximations, probability transformations, new distances between belief functions, non-classical multi-criteria decision-making problems with belief functions, generalization of Bayes theorem, image processing, data association, entropy and cross-entropy measures, fuzzy evidence numbers, negator of belief mass, human activity recognition, information fusion for breast cancer therapy, imbalanced data classification, and hybrid techniques mixing deep learning with belief functions as well

Applied Metaheuristic Computing

For decades, Applied Metaheuristic Computing (AMC) has been a prevailing optimization technique for tackling perplexing engineering and business problems, such as scheduling, routing, ordering, bin packing, assignment, facility layout planning, among others. This is partly because the classic exact methods are constrained with prior assumptions, and partly due to the heuristics being problem-dependent and lacking generalization. AMC, on the contrary, guides the course of low-level heuristics to search beyond the local optimality, which impairs the capability of traditional computation methods. This topic series has collected quality papers proposing cutting-edge methodology and innovative applications which drive the advances of AMC

Fusion of high spatial resolution multispectral & object height data for urban environmental monitoring: methods & applications

High spatial resolution (HSR) multispectral and object height data are becoming increasingly available in the urbanized regions of the world. The synergistic utilization of these data sources holds a large potential for the fine-scale characterization of a city because they are of high descriptive power and non-redundant. However, despite this promising development, detailed and area-wide maps of important settlement parameters, like land cover (LC), urban site characteristics (USCs), and urban structure types (USTs), are still lacking in many municipalities. One reason for this observation is the methodological challenge of turning the wealth of geospatial data into reliable thematic information. Accordingly, there is a strong need for accurate and transferable software solutions being able to produce some of the key data sets for human settlement monitoring from HSR multispectral and object height data. The present work aims at addressing this need. The overall goal of the dissertation was to develop methods for the fusion of HSR multispectral and object height data as well as to showcase their utility in the context of different urban environmental mapping and monitoring applications. It therefore intended to make both a technical and an applied contribution to the field of urban remote sensing. Particular emphasis was put on mapping urban LC, USCs, and USTs, as well as the usage of USCs to study urban land surface temperature (LST) and the surface urban heat island (UHI) effect. These settlement parameters were chosen because they are thematically connected, difficult to obtain from other data sources, and of high relevance for urban planning. To meet the above goal, a comprehensive literature review was conducted in advance. The review helped identifying current deficits within the chosen research fields and led to the formulation of specific thesis objectives. The latter determined the practical agenda of this work, comprising an overall number of four studies.Die Verfügbarkeit räumlich hochaufgelöster Multispektral- und Objekthöhendaten nimmt für die urbanen Gebiete der Erde stetig zu. Die synergetische Verknüpfung solcher Daten birgt ein großes Potential zur genauen Beschreibung von Städten, da diese Daten einen hohen Informationsgehalt aufweisen und redundanzfrei sind. Trotz dieser positiven Entwicklung fehlt es in vielen Städten an detaillierten Karten, welche Aufschluss über planungsrelevante Siedlungsparameter geben. Ein Grund für diese Beobachtung ist die methodische Herausforderung, die Fülle an zugänglichen Geodaten in verlässliche thematische Informationen zu überführen. Demzufolge besteht ein großer Bedarf an akkuraten und übertragbaren Auswertungsverfahren, welche sich das Synergiepotential räumlich hochaufgelöster Multispektral- und Objekthöhendaten für ein verbessertes Stadtmonitoring zunutze machen. Die vorliegende Arbeit zielt darauf ab, diesen Bedarf zu decken. Das übergeordnete Ziel der Dissertation war, Methoden zur Fusion räumlich hochaufgelöster Multispektral- und Objekthöhendaten zu entwickeln und deren Nutzen im Rahmen stadtumweltbezogener Fragestellungen zu demonstrieren. Folglich sollte die Arbeit einen technischen und einen angewandten Beitrag auf dem Gebiet der urbanen Fernerkundung leisten. Das Hauptaugenmerk lag auf der genauen und robusten Kartierung der Landbedeckung und Stadtstruktur. Darüber hinaus wurden verschiedene urbane Bewertungsindikatoren extrahiert und zu einem neuen Dichtemaß verknüpft. Die abgeleiteten Karten und Indikatoren kamen im Zuge einer abschließenden Analyse zum Einsatz, welche sich mit den Ursprüngen städtischer Wärmeinseln befasste. Um das obige Ziel zu erreichen, wurde im Vorfeld eine umfangreiche Literaturrecherche vorgenommen. Diese ermöglichte die Identifikation derzeitiger Forschungsdefizite und führte zur Formulierung spezifischer Arbeitsziele. Nach den Zielen richtete sich der praktische Teil der kumulativen Dissertation, welcher insgesamt vier Studien umfasste